Fixing backward not being called, gradient scaling and enabling backward with torch.no_grad() (#237)

stack-info: PR: #239, branch: xmfan/stack/17

Author: sanketpurandare

autoparallel/_testing/models/dsv3.py

@@ -1062,7 +1062,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         # HOPs don't support buffer mutations, keep this outside
         with torch.no_grad():
-            self.tokens_per_expert.add_(num_tokens_per_expert)
+            self.tokens_per_expert.add_(num_tokens_per_expert)  # type: ignore[operator]
         return out
 
     def init_weights(
@@ -1076,14 +1076,10 @@ def init_weights(
             self.shared_experts.init_weights(init_std)
 
         with torch.device(buffer_device):
-            self.tokens_per_expert = torch.zeros(
-                self.experts.num_experts, dtype=torch.float32
-            )
+            self.tokens_per_expert.zero_()  # type: ignore[operator]
             if self.load_balance_coeff is not None:
                 assert isinstance(self.expert_bias, torch.Tensor)
-                self.expert_bias = torch.zeros(
-                    self.experts.num_experts, dtype=torch.float32
-                )
+                self.expert_bias.zero_()  # type: ignore[operator]
 
 
 def has_cuda_capability(major: int, minor: int) -> bool:

autoparallel/graph_pp_runner.py

@@ -3,6 +3,7 @@
 # This source code is licensed under the BSD license found in the
 # LICENSE file in the root directory of this source tree.
 
+import logging
 from dataclasses import dataclass
 from typing import Any, Callable, Optional, Union, cast
 
@@ -20,6 +21,11 @@
 )
 from torch.distributed.tensor import DTensor
 
+from autoparallel.utils import DebugInterpreter
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
 
 @dataclass
 class GraphCallables:
@@ -75,14 +81,39 @@ def __init__(
             "unsharded_grads": [],
         }
 
+    def scale_grads(self, grad_scale_factor: int) -> None:
+        """Scale stage's gradients by `grad_scale_factor`, which should be specified in coordination with the
+        loss function used with pipelining.  For loss functions which perform 'mean' loss reduction, `grad_scale_factor`
+        should be set to num_microbatches.  For loss functions that use `sum` reduction, `grad_scale_factor` should
+        be set to 1.
+
+        Should only be called once per pipeline schedule step, after all backwards passes have completed.
+        """
+
+        # PP scales only for its own contribution (microbatches), but relies on DP to scale further
+        # for DP degree.
+        if grad_scale_factor != 1:
+            for grad in self.state["unsharded_grads"]:
+                if grad is not None:
+                    grad.div_(grad_scale_factor)
+
 
 def _run_fw_module(
-    fw_module: fx.GraphModule, graph_meta: GraphMeta, fw_args: list[Any]
+    fw_module: fx.GraphModule,
+    graph_meta: GraphMeta,
+    fw_args: list[Any],
+    numerics_logs: Optional[list[str]] = None,
 ) -> tuple[Any, tuple[list[Any], list[Any]]]:
     assert len([n for n in fw_module.graph.nodes if n.op == "placeholder"]) == len(
         fw_args
     ), f"Mismatched number of inputs to fwd, {len([n for n in fw_module.graph.nodes if n.op == 'placeholder'])}, {len(fw_args)}"
-    fw_outputs = torch.fx.Interpreter(fw_module).boxed_run(fw_args)
+    if numerics_logs is not None:
+        debug_interpreter = DebugInterpreter(fw_module)
+        fw_outputs = debug_interpreter.boxed_run(fw_args)
+        numerics_logs += debug_interpreter.get_logs()
+    else:
+        fw_outputs = torch.fx.Interpreter(fw_module).boxed_run(fw_args)
+
     num_inner_fwd_outputs = graph_meta.num_mutate_inputs + graph_meta.num_user_outputs
     saved_intermediates = fw_outputs[num_inner_fwd_outputs:]
     num_tensors_for_backward = (
@@ -153,14 +184,16 @@ def _run_reduce_grad_module(
     return sharded_grads
 
 
-def _run_forward_microbatch(stage: GraphPipelineStage, *args) -> tuple[Any, Any]:
+def _run_forward_microbatch(
+    stage: GraphPipelineStage, *args, numerics_logs: Optional[list[str]] = None
+) -> tuple[Any, Any]:
     fw_args = [
         *stage.state["unsharded_params"],
         *stage.state["buffers"],
         *args,
     ]
     user_outputs, saved_intermediates = _run_fw_module(
-        stage.graph_callables.fw, stage.graph_meta, fw_args
+        stage.graph_callables.fw, stage.graph_meta, fw_args, numerics_logs=numerics_logs
     )
     return (user_outputs, saved_intermediates)
 
@@ -200,6 +233,7 @@ def _run_backward_microbatch(
 def stage_forward(
     action: _Action,
     ctx: _PipelineContext,
+    numerics_logs: Optional[list[str]] = None,
 ) -> None:
     schedule = ctx.schedule_ref
     assert isinstance(schedule, _PipelineScheduleRuntime)
@@ -243,8 +277,13 @@ def stage_forward(
         composite_args = stage._retrieve_recv_activations(mb_index)
 
     # stage._validate_fwd_input(args, kwargs) Maybe need to validate composite args?
-
-    output, saved_intermediates = _run_forward_microbatch(stage, *composite_args)
+    logger.debug(
+        "GraphPPRunner running action %s",
+        action,
+    )
+    output, saved_intermediates = _run_forward_microbatch(
+        stage, *composite_args, numerics_logs=numerics_logs
+    )
 
     # See [Note: pipeline model output type]
     output_tuple = _normalize_model_output_as_tuple(output)
@@ -306,6 +345,7 @@ def stage_full_backward(
     grad_scale_factor = schedule._n_microbatches if schedule.scale_grads else 1
 
     if not backward_stage.has_backward:
+        logger.debug("Returning early for backward stage")
         return
     (
         stage_output,
@@ -320,7 +360,7 @@ def stage_full_backward(
         # HACK till we have loss function, we populate the tangents here manually
         bwd_kwargs = {
             "stage_output": loss,
-            "tangents": [torch.randn_like(stage_output)],
+            "tangents": [torch.randn_like(stage_output[0])],
             "saved_intermediates": saved_intermediates,
         }
     else:
@@ -334,10 +374,14 @@ def stage_full_backward(
             "tangents": output_grads,
             "saved_intermediates": saved_intermediates,
         }
-
+    logger.debug(
+        "GraphPPRunner running action %s",
+        action,
+    )
     input_grads = _run_backward_microbatch(backward_stage, bwd_kwargs)
-
-    backward_stage.bwd_cache[backward_mb_index] = input_grads
+    backward_stage.bwd_cache[backward_mb_index] = (
+        tuple(input_grads) if not isinstance(input_grads, tuple) else input_grads
+    )
 
     # skipping detach logic
 
@@ -362,9 +406,20 @@ def stage_unshard(
         stage.stage_index: cast(GraphPipelineStage, stage) for stage in schedule._stages
     }
     stage = stage_index_to_stage[action.stage_index]
+    logger.debug(
+        "GraphPPRunner running action %s",
+        action,
+    )
     if stage.graph_callables.unshard is None:
         stage.state["unsharded_params"] = stage.state["sharded_params"]
-    # TODO (sanketpurandare): Add the fw_fsdp_all_gather graph call here
+    else:
+        sharded_params = list(stage.state["sharded_params"])
+        unsharded_params = _run_unshard_module(
+            stage.graph_callables.unshard,
+            stage.graph_meta,
+            sharded_params,
+        )
+        stage.state["unsharded_params"] = unsharded_params
 
 
 def stage_reshard(
@@ -377,6 +432,10 @@ def stage_reshard(
         stage.stage_index: cast(GraphPipelineStage, stage) for stage in schedule._stages
     }
     stage = stage_index_to_stage[action.stage_index]
+    logger.debug(
+        "GraphPPRunner running action %s",
+        action,
+    )
     stage.state["unsharded_params"].clear()
 
 
@@ -390,8 +449,19 @@ def stage_reduce_grad(
         stage.stage_index: cast(GraphPipelineStage, stage) for stage in schedule._stages
     }
     stage = stage_index_to_stage[action.stage_index]
+    logger.debug(
+        "GraphPPRunner running action %s",
+        action,
+    )
     if stage.graph_callables.reduce_grad is None:
         stage.state["sharded_grads"] = stage.state["unsharded_grads"]
+    else:
+        sharded_grads = _run_reduce_grad_module(
+            stage.graph_callables.reduce_grad,
+            stage.graph_meta,
+            stage.state["unsharded_grads"],
+        )
+        stage.state["sharded_grads"] = sharded_grads
 
 
 class GraphPPRunner:
@@ -400,6 +470,19 @@ def __init__(
         schedule: _PipelineScheduleRuntime,
     ):
         self.schedule = schedule
+        if not schedule._backward_requires_autograd:
+            assert all(
+                isinstance(stage, GraphPipelineStage)
+                and (
+                    stage.graph_callables.full_bw is not None
+                    or (
+                        stage.graph_callables.bw_dI is not None
+                        and stage.graph_callables.bw_dW is not None
+                    )
+                )
+                for stage in schedule._stages
+            )
+            self.schedule._has_backward = True
 
     def _populate_stage_states(self, stage: GraphPipelineStage) -> None:
         sharded_params = [
@@ -415,21 +498,10 @@ def _populate_stage_states(self, stage: GraphPipelineStage) -> None:
         stage.state["sharded_params"] = sharded_params
         stage.state["buffers"] = buffers
         stage.state["unsharded_grads"] = [None] * len(sharded_params)
-        # TODO (sanketpurandare)
-        # pipeline schedule runtime does not allow us to register a custom function
-        # for UNSHARD/RESHARD/REDUCE_GRAD action types yet
-        # HACK remove this once we support this
-        if stage.graph_callables.unshard is None:
-            stage.state["unsharded_params"] = stage.state["sharded_params"]
 
     def _accumulate_stage_grads_and_clear_states(
         self, stage: GraphPipelineStage
     ) -> None:
-        # TODO (sanketpurandare)
-        # We don't have a REDUCE_GRAD action yet in the ScheduleIR yet
-        # HACK remove this once Ivan's PR lands
-        if stage.graph_callables.reduce_grad is None:
-            stage.state["sharded_grads"] = stage.state["unsharded_grads"]
         grads = stage.state["sharded_grads"]
         params = list(stage.submod.parameters())
         for param, grad in zip(params, grads):

autoparallel/utils.py

@@ -3,7 +3,10 @@
 # This source code is licensed under the BSD license found in the
 # LICENSE file in the root directory of this source tree.
 
+from typing import Any, Iterable
+
 import torch
+import torch.utils._pytree as pytree
 from torch.distributed._tensor.placement_types import Placement, TensorMeta
 from torch.distributed.device_mesh import _get_device_handle
 from torch.distributed.tensor._dtensor_spec import DTensorSpec
@@ -310,3 +313,67 @@ def _get_device_from_mesh(mesh):
         return torch.device("cpu")
     device_handle = _get_device_handle(mesh.device_type)
     return torch.device(mesh.device_type, device_handle.current_device())
+
+
+# An FX graph interpreter that logs inputs and outputs of each node
+# with a few exceptions for c10d ops
+class DebugInterpreter(torch.fx.Interpreter):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._logs = []
+
+    def log(self, node: str, args: Iterable[Any], inputs_or_outputs: str):
+        leaves, _ = pytree.tree_flatten(args)
+        for i, arg in enumerate(leaves):
+            if not isinstance(arg, torch.Tensor):
+                self._logs.append(f"{node=}, {inputs_or_outputs}[{i}]={arg}")
+                continue
+
+            if arg.numel() == 0:
+                self._logs.append(f"{node=}, {inputs_or_outputs}[{i}].numel()=0")
+                continue
+
+            if arg.is_complex():
+                real = torch.hash_tensor(arg.real)
+                imag = torch.hash_tensor(arg.imag)
+                self._logs.append(f"{node=}, {inputs_or_outputs}[{i}], {real=} {imag=}")
+                continue
+
+            self._logs.append(
+                f"{node=}, {inputs_or_outputs}[{i}]={torch.hash_tensor(arg)}"
+            )
+
+    def run_node(self, n: torch.fx.Node) -> Any:
+        args, kwargs = self.fetch_args_kwargs_from_env(n)
+
+        # reading wait_tensor inputs is undefined behavior
+        if "wait_tensor" not in n.name:
+            args, _ = self.fetch_args_kwargs_from_env(n)
+            self.log(n.name, args, "args")
+
+        out = super().run_node(n)
+
+        # reading functional collectives outputs before wait_tensor is undefined behavior
+        if "c10d" not in str(n.target):
+            outs = out
+            if isinstance(outs, torch.Tensor):
+                outs = [outs]
+            self.log(n.name, outs, "outs")
+
+        return out
+
+    def get_logs(self):
+        return self._logs
+
+
+# Always prints from rank 0 to rank N
+def print_rank_by_rank(msg: Any):
+    rank = torch.distributed.get_rank()
+    world_size = torch.distributed.get_world_size()
+    torch.distributed.barrier()
+    for i in range(world_size):
+        if rank == i:
+            print(f"{rank=} start")
+            print(msg)
+            print(f"{rank=} done")
+        torch.distributed.barrier()