Revert "merge upstream changes" (pytorch#570)

Reverts pytorch#569

sorry accidental commit things to the wrong fork

.gitignore

@@ -17,6 +17,3 @@ torchtitan/datasets/**/*.model
 *.log
 error.json
 _remote_module_non_scriptable.py
-
-# torch compile debug related
-torch_compile_debug/*

run_llama_train.sh

@@ -19,8 +19,6 @@ if [ $# -ne 0 ]; then
     overrides="$*"
 fi
 
-# TORCH_TRACE="./outputs/trace" \
-TORCH_NCCL_AVOID_RECORD_STREAMS=1 \
 torchrun --nproc_per_node=${NGPU} --rdzv_backend c10d --rdzv_endpoint="localhost:0" \
 --local-ranks-filter ${LOG_RANK} --role rank --tee 3 \
 train.py --job.config_file ${CONFIG_FILE} $overrides

torchtitan/config_manager.py

@@ -241,14 +241,6 @@ def __init__(self):
             action="store_true",
             help="Whether to apply loss parallel when sequence parallel is enabled",
         )
-
-        # experimental configs
-        self.parser.add_argument(
-            "--experimental.torch_spmd",
-            default=False,
-            action="store_true",
-            help="Whether to use the experimental torch_spmd style parallelism",
-        )
         self.parser.add_argument(
             "--experimental.enable_async_tensor_parallel",
             default=False,

torchtitan/parallelisms/parallelize_llama.py

@@ -29,55 +29,7 @@
 from torchtitan.config_manager import JobConfig, TORCH_DTYPE_MAP
 from torchtitan.logging import logger
 from torchtitan.parallelisms.parallel_dims import ParallelDims
-
-
-# NOTE(lty): experimental for the PT-D 24 research internship project
-def torch_spmd_parallelize(
-    model: nn.Module,
-    world_mesh: DeviceMesh,
-    parallel_dims: ParallelDims,
-    job_config: JobConfig,
-):
-    torch._inductor.config.simplefsdp.enable_reorder = True
-    torch._inductor.config.simplefsdp.enable_bucket = True
-
-    if parallel_dims.tp_enabled:
-        apply_tp(
-            model,
-            world_mesh["tp"],
-            loss_parallel=parallel_dims.loss_parallel_enabled,
-            enable_float8=job_config.float8.enable_float8_linear,
-            enable_async_tp=job_config.experimental.enable_async_tensor_parallel,
-        )
-
-    ac_config = job_config.activation_checkpoint
-    if ac_config.mode != "none":
-        apply_ac(model, ac_config)
-        logger.info(f"Applied {ac_config.mode} activation checkpointing to the model")
-
-    if parallel_dims.dp_enabled:
-        from torch_spmd.data_parallel import data_parallel, MixedPrecisionPolicy
-
-        mp_policy = MixedPrecisionPolicy(
-            param_dtype=TORCH_DTYPE_MAP[job_config.training.mixed_precision_param],
-            reduce_dtype=TORCH_DTYPE_MAP[job_config.training.mixed_precision_reduce],
-        )
-        dp_mesh = world_mesh["dp"] if world_mesh.ndim > 1 else world_mesh
-
-        model = data_parallel(
-            model,
-            dp_mesh,
-            mode="fully_shard",
-            ac_mode=ac_config.mode,
-            mp_policy=mp_policy,
-        )
-        logger.info("Applied Simple FSDP to the model")
-
-    if job_config.training.compile:
-        model = torch.compile(model, fullgraph=True)
-        logger.info("Compiling with torch.compile")
-
-    return model
+from torchtitan.parallelisms.utils import check_strided_sharding_enabled
 
 
 def parallelize_llama(
@@ -93,9 +45,6 @@ def parallelize_llama(
     NOTE: The passed-in model preferably should be on meta device. Otherwise,
     the model must fit on GPU or CPU memory.
     """
-    # NOTE(lty): experimental for the PT-D 24 research internship project
-    if job_config.experimental.torch_spmd:
-        return torch_spmd_parallelize(model, world_mesh, parallel_dims, job_config)
 
     if parallel_dims.tp_enabled:
         if (
@@ -351,12 +300,11 @@ def apply_fsdp(
     mp_policy = MixedPrecisionPolicy(param_dtype=param_dtype, reduce_dtype=reduce_dtype)
     fsdp_config = {"mesh": dp_mesh, "mp_policy": mp_policy}
 
-    # TODO(lty): the check below requires the latest PyTorch nightly; remove for now
     # TODO: remove this check once PyTorch 2.5 is released. We can safely assume
     # that users won't use a nightly build which is older than 20240809 by then.
-    # if tp_enabled:
-    #     # check if strided sharding is enabled, which is necessary for 2D/3D DCP
-    #     check_strided_sharding_enabled()
+    if tp_enabled:
+        # check if strided sharding is enabled, which is necessary for 2D/3D DCP
+        check_strided_sharding_enabled()
 
     for layer_id, transformer_block in model.layers.items():
         if pp_enabled: