tiled_mlp supports single gpu

docs/multi-gpu.qmd

@@ -66,6 +66,15 @@ Start from Stage 1 -> Stage 2 -> Stage 3.
 
 :::
 
+::: {.callout-tip}
+
+Using ZeRO Stage 3 with Single-GPU training
+
+ZeRO Stage 3 can be used for training on a single GPU by manually setting the environment variables:
+`WORLD_SIZE=1 LOCAL_RANK=0 MASTER_ADDR=0.0.0.0 MASTER_PORT=29500`
+
+:::
+
 ## FSDP {#sec-fsdp}
 
 ### Basic FSDP Configuration {#sec-fsdp-config}

src/axolotl/loaders/patch_manager.py

@@ -7,6 +7,7 @@
 from functools import cached_property
 
 import addict
+import torch
 import transformers
 from transformers import PretrainedConfig, PreTrainedModel
 
@@ -165,10 +166,25 @@ def _apply_gradient_checkpointing_patches(self):
         """Apply patches for gradient checkpointing."""
         if self.cfg.gradient_checkpointing in ["unsloth", "offload"]:
             from axolotl.monkeypatch.gradient_checkpointing import (
+                CheckpointFunctionWithCPUOffload,
                 hf_grad_checkpoint_offload_wrapper,
             )
 
-            transformers.modeling_utils.checkpoint = hf_grad_checkpoint_offload_wrapper
+            if (
+                self.cfg.gradient_checkpointing_kwargs
+                and "use_reentrant" in self.cfg.gradient_checkpointing_kwargs
+                and self.cfg.gradient_checkpointing_kwargs["use_reentrant"] is False
+            ):
+                transformers.modeling_utils.checkpoint = (
+                    hf_grad_checkpoint_offload_wrapper
+                )
+            else:
+                transformers.modeling_utils.checkpoint.CheckpointFunction = (
+                    CheckpointFunctionWithCPUOffload
+                )
+                torch.utils.checkpoint.CheckpointFunction = (
+                    CheckpointFunctionWithCPUOffload
+                )
         if self.cfg.gradient_checkpointing == "offload_disk":
             from axolotl.monkeypatch.gradient_checkpointing import (
                 hf_grad_checkpoint_disk_offload_wrapper,

src/axolotl/monkeypatch/gradient_checkpointing/__init__.py

@@ -5,7 +5,8 @@
 
 from packaging import version
 
-from axolotl.monkeypatch.gradient_checkpointing.offload_cpu import (
+from axolotl.monkeypatch.gradient_checkpointing.offload_cpu import (  # noqa: F401
+    CheckpointFunctionWithCPUOffload,
     CPU_Offloaded_Gradient_Checkpointer,
 )
 from axolotl.monkeypatch.gradient_checkpointing.offload_disk import (

src/axolotl/monkeypatch/gradient_checkpointing/offload_cpu.py

@@ -13,8 +13,24 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+import contextlib
+import inspect
+
 import torch
 from packaging import version
+from torch.utils.checkpoint import (
+    _get_autocast_kwargs,
+    _get_device_module,
+    _infer_device_type,
+    check_backward_validity,
+    detach_variable,
+    get_device_states,
+    set_device_states,
+)
+
+# support different pytorch versions
+has_device_type = "device_type" in inspect.signature(set_device_states).parameters
 
 torch_version = version.parse(torch.__version__)
 
@@ -60,3 +76,153 @@ def backward(ctx, dY):
         ) + (
             None,
         ) * len(ctx.args)
+
+
+# Copyright 2025 Snowflake Inc.
+# SPDX-License-Identifier: Apache-2.0
+# https://github.com/snowflakedb/ArcticTraining/blob/main/arctic_training/monkey_patches.py
+class CheckpointFunctionWithCPUOffload(torch.autograd.Function):
+    """
+    This is a torch/utils/checkpoint.py CheckpointFunction monkey patch that offloads the first tensor to cpu during forward and back to cuda during backward. This allows significant memory savings when using a very long seqlen. e.g. for llama 8b at 100k it's 24GB saved per gpu: `((100_000*4096)*2*32/2**30)`
+    In the case of a very long seqlen 100k+ the copying to/from cpu overhead is not big, because dense quadratic attention compute will dominate.
+    """
+
+    @staticmethod
+    def forward(ctx, run_function, preserve_rng_state, *args):
+        check_backward_validity(args)
+        ctx.run_function = run_function
+        ctx.preserve_rng_state = preserve_rng_state
+        # Accommodates the (remote) possibility that autocast is enabled for cpu AND gpu.
+        ctx.device_type = _infer_device_type(*args)
+        ctx.device_autocast_kwargs, ctx.cpu_autocast_kwargs = _get_autocast_kwargs(
+            ctx.device_type
+        )
+        if preserve_rng_state:
+            ctx.fwd_cpu_state = torch.get_rng_state()
+            # Don't eagerly initialize the cuda context by accident.
+            # (If the user intends that the context is initialized later, within their
+            # run_function, we SHOULD actually stash the cuda state here.  Unfortunately,
+            # we have no way to anticipate this will happen before we run the function.)
+            ctx.had_device_in_fwd = False
+            device_module = _get_device_module(ctx.device_type)
+            if getattr(device_module, "_initialized", False):
+                ctx.had_device_in_fwd = True
+                ctx.fwd_devices, ctx.fwd_device_states = get_device_states(*args)
+
+        # Save non-tensor inputs in ctx, keep a placeholder None for tensors
+        # to be filled out during the backward.
+        ctx.inputs = []
+        ctx.tensor_indices = []
+        tensor_inputs = []
+        # x = None
+        for i, arg in enumerate(args):
+            if torch.is_tensor(arg):
+                # cpu-offload
+                # we don't want the 2nd tensor - usually it's a shared 4D attn mask which is huge [seq,seq]
+                # upstream could accept a list of arg indices to offload
+                if i == 0:
+                    # print(f"{arg.shape=}")
+                    ctx.x_device = arg.device
+                    ctx.x_requires_grad = arg.requires_grad
+                    t = arg.detach().cpu()
+                else:
+                    t = arg
+                tensor_inputs.append(t)
+                ctx.tensor_indices.append(i)
+                ctx.inputs.append(None)
+            else:
+                ctx.inputs.append(arg)
+
+        ctx.save_for_backward(*tensor_inputs)
+
+        with torch.no_grad():
+            outputs = run_function(*args)
+
+        return outputs
+
+    @staticmethod
+    def backward(ctx, *args):
+        if (
+            not torch.autograd._is_checkpoint_valid()  # pylint: disable=protected-access
+        ):
+            raise RuntimeError(
+                "When use_reentrant=True, torch.utils.checkpoint is incompatible"
+                " with .grad() or passing an `inputs` parameter to .backward()."
+                " To resolve this error, you can either set use_reentrant=False,"
+                " or call .backward() without passing the `inputs` argument."
+            )
+        # Copy the list to avoid modifying original list.
+        inputs = list(ctx.inputs)
+        tensor_indices = ctx.tensor_indices
+        tensors = ctx.saved_tensors
+
+        # Fill in inputs with appropriate saved tensors.
+        for i, idx in enumerate(tensor_indices):
+            if i == 0:
+                t = (
+                    tensors[i]
+                    .to(ctx.x_device)
+                    .detach()
+                    .requires_grad_(ctx.x_requires_grad)
+                )
+            else:
+                t = tensors[i]
+            inputs[idx] = t
+
+        # Stash the surrounding rng state, and mimic the state that was
+        # present at this time during forward.  Restore the surrounding state
+        # when we're done.
+        rng_devices = []
+        if ctx.preserve_rng_state and ctx.had_device_in_fwd:
+            rng_devices = ctx.fwd_devices
+        with torch.random.fork_rng(
+            devices=rng_devices,
+            enabled=ctx.preserve_rng_state,
+            device_type=ctx.device_type,
+        ):
+            if ctx.preserve_rng_state:
+                torch.set_rng_state(ctx.fwd_cpu_state)
+                if ctx.had_device_in_fwd:
+                    if has_device_type:
+                        # newer pytorch (as early as 2.7)
+                        set_device_states(
+                            ctx.fwd_devices,
+                            ctx.fwd_device_states,
+                            device_type=ctx.device_type,
+                        )
+                    else:
+                        # older pytorch (at least 2.4)
+                        set_device_states(ctx.fwd_devices, ctx.fwd_device_states)
+            detached_inputs = detach_variable(tuple(inputs))
+
+            device_autocast_ctx = (
+                torch.amp.autocast(
+                    device_type=ctx.device_type, **ctx.device_autocast_kwargs
+                )
+                if torch.amp.is_autocast_available(ctx.device_type)
+                else contextlib.nullcontext()
+            )
+            with torch.enable_grad(), device_autocast_ctx, torch.amp.autocast("cpu", **ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
+                outputs = ctx.run_function(*detached_inputs)
+
+        if isinstance(outputs, torch.Tensor):
+            outputs = (outputs,)
+
+        # run backward() with only tensor that requires grad
+        outputs_with_grad = []
+        args_with_grad = []
+        for i in range(len(outputs)):  # pylint: disable=consider-using-enumerate
+            if torch.is_tensor(outputs[i]) and outputs[i].requires_grad:
+                outputs_with_grad.append(outputs[i])
+                args_with_grad.append(args[i])
+        if len(outputs_with_grad) == 0:
+            raise RuntimeError(
+                "none of output has requires_grad=True, this checkpoint() is not necessary"
+            )
+        torch.autograd.backward(outputs_with_grad, args_with_grad)
+        grads = tuple(
+            inp.grad if isinstance(inp, torch.Tensor) else None
+            for inp in detached_inputs
+        )
+
+        return (None, None) + grads

src/axolotl/monkeypatch/tiled_mlp.py

@@ -1,6 +1,7 @@
 """Monkeypatch for Tiled MLP implementation"""
 
 import math
+import os
 
 import torch
 import torch.distributed as dist
@@ -29,15 +30,18 @@ def generic_mlp_forward(self_, hs):
 
             mlp_forward = torch.compile(generic_mlp_forward)
 
+        is_distributed = int(os.environ.get("WORLD_SIZE", 1)) > 1
+
         def tiled_mlp_forward(self, x):
             input_shape = x.shape
             seqlen = input_shape[-2]
             hidden = input_shape[-1]
             if cfg_num_shards is None:
                 num_shards = math.ceil(seqlen / hidden)
-                num_shards_tensor = torch.tensor(num_shards, device=x.device)
-                dist.all_reduce(num_shards_tensor, op=dist.ReduceOp.MAX)
-                num_shards = num_shards_tensor.item()
+                if is_distributed:
+                    num_shards_tensor = torch.tensor(num_shards, device=x.device)
+                    dist.all_reduce(num_shards_tensor, op=dist.ReduceOp.MAX)
+                    num_shards = num_shards_tensor.item()
             else:
                 num_shards = cfg_num_shards
 

src/axolotl/utils/schemas/validation.py

@@ -479,8 +479,14 @@ def pretrain_with_tps(cls, data):
     @model_validator(mode="before")
     @classmethod
     def check_tiled_mlp_deepspeed(cls, data):
-        if data.get("tiled_mlp", False) and not data.get("deepspeed"):
-            raise ValueError("tiled_mlp requires deepspeed ZeRO to be enabled")
+        capabilities = data.get("capabilities")
+        n_gpu = 0
+        if capabilities and capabilities.get("n_gpu", 0) >= 1:
+            n_gpu = capabilities.get("n_gpu", 0)
+        if data.get("tiled_mlp", False) and (n_gpu > 1 and not data.get("deepspeed")):
+            raise ValueError(
+                "tiled_mlp requires deepspeed ZeRO to be enabled for multi-gpu"
+            )
         return data
 
 

src/axolotl/utils/trainer.py

@@ -546,6 +546,15 @@ def setup_deepspeed_env(cfg, stage=None):
     # NOTE(djsaunde): The distribued state cannot be initialized prior to the
     # ACCELERATE_USE_DEEPSPEED assignment, but it must be initialized some time prior
     # to model load.
+    if int(os.environ.get("WORLD_SIZE", "1")) == 1:
+        os.environ["WORLD_SIZE"] = "1"  # force it in case not set
+        os.environ["LOCAL_RANK"] = "0"  # force it in case not set
+        os.environ["RANK"] = os.environ.get("LOCAL_RANK", "0")
+        import deepspeed.comm as dist
+
+        dist.init_distributed(
+            dist_backend="nccl", auto_mpi_discovery=False, dist_init_required=True
+        )
     init_distributed_state()
 
     # If we don't assign this, it doesn't actually get set in the accelerate weakref