ADLR/megatron-lm!1962 - docs: Fixes to allow building docs again

.gitlab/stages/00.pre.yml

@@ -38,6 +38,13 @@ label_merge_request:
       source labels
       curl --header "PRIVATE-TOKEN: ${PROJECT_ACCESS_TOKEN_MCORE}" --url "https://${GITLAB_ENDPOINT}/api/v4/projects/${CI_PROJECT_ID}/merge_requests/${CI_MERGE_REQUEST_IID}" --data-urlencode "add_labels=$LABELS" -X PUT
 
+clean_docker_node:
+  stage: .pre
+  image: docker:26.1.4-dind
+  tags: [mcore-docker-node]
+  script:
+    - docker system prune -a --filter "until=48h" -f
+
 check_milestone:
   rules: 
     - if: $CI_PIPELINE_SOURCE == "merge_request_event"

.gitlab/stages/01.tests.yml

@@ -104,17 +104,15 @@ unit_tests:
       - coverage
 
 docs_build_test:
-  image: ${GITLAB_ENDPOINT}:5005/adlr/megatron-lm/python-format:0.0.1
+  image: ${CI_MCORE_IMAGE}:${CI_PIPELINE_ID}
   tags: [mcore-docker-node-small]
+  needs: [build_image]
   script:
     - cd ..
     - rm -rf documentation && git clone https://gitlab-ci-token:${CI_JOB_TOKEN}@${GITLAB_ENDPOINT}/nemo-megatron-core-tme/documentation.git
     - mv megatron-lm/ documentation/
     - cd documentation/
     - ./repo docs
-  allow_failure: true
-  except:
-    - main
 
 formatting:
   extends: [.tests_common]

docs/source/distrib_optimizer.md → docs/source/api-guide/dist_optimizer.md

@@ -28,11 +28,11 @@ The figures below illustrate the grad buffer's sharding scheme, and the key step
 
 ## Data flow
 
-![Data flow](images/distrib_optimizer/data_flow.png)
+![Data flow](../images/distrib_optimizer/data_flow.png)
 
 ## Sharding scheme
 
-![Sharding scheme](images/distrib_optimizer/sharding_scheme.png)
+![Sharding scheme](../images/distrib_optimizer/sharding_scheme.png)
 
 ## Key steps
 

docs/source/api-guide/fusions.rst

@@ -58,7 +58,7 @@ fusions.fused\_cross\_entropy\_loss module
 
 This module uses PyTorch JIT to fuse the cross entropy loss calculation and batches communication calls.
 
-.. automodule:: core.fusions.fused_softmax
+.. automodule:: core.fusions.fused_cross_entropy
    :members:
    :undoc-members:
    :show-inheritance:

docs/source/api-guide/index.rst

@@ -12,6 +12,7 @@ API Guide
    transformer
    moe
    dist_checkpointing
+   dist_optimizer
    distributed
    datasets
    num_microbatches_calculator

docs/source/api-guide/num_microbatches_calculator.rst

@@ -1,5 +1,5 @@
 Microbatches Calculator
-==============
+=======================
 This api is used to calculate the number of microbatches required to fit a given model on a given batch size.
 
 

megatron/core/dist_checkpointing/strategies/__init__.py

@@ -2,4 +2,8 @@
 
 """ Various loading and saving strategies """
 
-from .common import _import_trigger
+# We mock imports to populate the `default_strategies` objects.
+# Since they are defined in base but populated in common, we have to mock
+# import both modules.
+from megatron.core.dist_checkpointing.strategies.base import _import_trigger
+from megatron.core.dist_checkpointing.strategies.common import _import_trigger

megatron/core/dist_checkpointing/strategies/base.py

@@ -6,6 +6,7 @@
 from collections import defaultdict
 from enum import Enum
 from pathlib import Path
+from typing import Any, DefaultDict
 
 from ..mapping import CheckpointingException, ShardedStateDict, StateDict
 from .async_utils import AsyncCallsQueue, AsyncRequest
@@ -18,7 +19,8 @@ class StrategyAction(Enum):
     SAVE_SHARDED = 'save_sharded'
 
 
-default_strategies = defaultdict(dict)
+_import_trigger = None
+default_strategies: DefaultDict[str, dict[tuple, Any]] = defaultdict(dict)
 
 async_calls = AsyncCallsQueue()
 
@@ -35,7 +37,8 @@ def get_default_strategy(action: StrategyAction, backend: str, version: int):
             from .torch import _import_trigger
     except ImportError as e:
         raise CheckpointingException(
-            f'Cannot import a default strategy for: {(action.value, backend, version)}. Error: {e}. Hint: {error_hint}'
+            f'Cannot import a default strategy for: {(action.value, backend, version)}. '
+            f'Error: {e}. Hint: {error_hint}'
         ) from e
     try:
         return default_strategies[action.value][(backend, version)]
@@ -46,7 +49,8 @@ def get_default_strategy(action: StrategyAction, backend: str, version: int):
 
 
 class LoadStrategyBase(ABC):
-    """Base class for a load strategy. Requires implementing checks for compatibility with a given checkpoint version."""
+    """Base class for a load strategy. Requires implementing checks for compatibility with a
+    given checkpoint version."""
 
     @abstractmethod
     def check_backend_compatibility(self, loaded_version):
@@ -63,7 +67,8 @@ def can_handle_sharded_objects(self):
 
 
 class SaveStrategyBase(ABC):
-    """Base class for a save strategy. Requires defining a backend type and version of the saved format."""
+    """Base class for a save strategy. Requires defining a backend type and
+    version of the saved format."""
 
     def __init__(self, backend: str, version: int):
         self.backend = backend

megatron/core/dist_checkpointing/strategies/common.py

@@ -4,7 +4,6 @@
 
 import logging
 import os
-from itertools import product
 from pathlib import Path
 
 import torch
@@ -68,10 +67,12 @@ def load_common(self, checkpoint_dir: Path):
     def load_sharded_objects(
         self, sharded_objects_state_dict: ShardedStateDict, checkpoint_dir: Path
     ):
-        """Replaces all ShardedObject from a given state dict with values loaded from the checkpoint.
+        """Replaces all ShardedObject from a given state dict with values loaded from the
+        checkpoint.
 
         Args:
-            sharded_objects_state_dict (ShardedStateDict): sharded state dict defining what objects should be loaded.
+            sharded_objects_state_dict (ShardedStateDict):
+                sharded state dict defining what objects should be loaded.
             checkpoint_dir (Path): checkpoint directory
 
         Returns:
@@ -99,7 +100,8 @@ def load_sharded_object(sh_obj: ShardedObject):
                     else:
                         ckpt_files = [f.name for f in checkpoint_dir.iterdir()]
                         logger.debug(
-                            f'{err_msg}. Object {sh_obj.key} directory does not exist. Checkpoint directory content: {ckpt_files}'
+                            f'{err_msg}. Object {sh_obj.key} directory does not exist. Checkpoint'
+                            f' directory content: {ckpt_files}'
                         )
                     raise CheckpointingException(err_msg) from e
             return loaded_obj
@@ -119,7 +121,8 @@ def load_sharded_metadata(self, checkpoint_dir: Path) -> ShardedStateDict:
                 full_key = f'{subdir.name}/{shard_file.stem}'
                 sh_objs.append(ShardedObject.empty_from_unique_key(full_key))
 
-            # This is a backward-compatibility fix, where the last global shape is missing in the name
+            # This is a backward-compatibility fix, where the last global shape is missing in the
+            # name
             if sh_objs[0].global_shape[-1] < 0:
                 max_last_offset = max(map(lambda sh_obj: sh_obj.global_offset[-1], sh_objs))
                 for sh_obj in sh_objs:

megatron/core/fusions/fused_bias_gelu.py

@@ -4,7 +4,7 @@
 
 from megatron.core.jit import jit_fuser
 
-###### BIAS GELU FUSION/ NO AUTOGRAD ################
+# BIAS GELU FUSION/ NO AUTOGRAD ################
 # 1/sqrt(2*pi)-> 0.3989423
 # 1/sqrt(2)   -> 0.70710678
 # sqrt(2/pi)  -> 0.79788456
@@ -46,5 +46,10 @@ def backward(ctx, grad_output):
         tmp = bias_gelu_back(grad_output, bias, input)
         return tmp, tmp
 
+    # This is required to make Sphinx happy :-(
+    @classmethod
+    def apply(cls, *args, **kwargs):
+        super().apply(*args, **kwargs)
+
 
 bias_gelu_impl = GeLUFunction.apply