Unrolling tensor subclasses in fwd/bwd split

thunder/__init__.py

@@ -587,8 +587,6 @@ def get_computation_and_inputs(*args, **kwargs):
                     if len(tensor_args_consumed_by_inplace_grouped_by_numel) > 1:
                         vanilla_tensor_args = set(tensor_indices)
 
-            computation_trc = flatten_tensor_subclasses(computation_trc)
-
             if epilogue_trc is not None:
                 epilogue_traces = [epilogue_trc]
             else:
@@ -649,6 +647,7 @@ def get_computation_and_inputs(*args, **kwargs):
             computation_trc = dce(computation_trc)
             computation_traces.append(computation_trc)
 
+            _tensor_subclass_transform_applied = False
             backward_trc = None
             if not cd.disable_torch_autograd_support:
                 tensor_cls = (pytorch.Tensor, TensorProxy)
@@ -662,6 +661,9 @@ def get_computation_and_inputs(*args, **kwargs):
                     computation_trc, backward_trc = split_forward_backward(computation_trc, cd, cs, *inps)
                     # Note computation_trc and backward_trc have been appended to cs.last_(backward_)traces
                     # by split_forward_backward
+                    _tensor_subclass_transform_applied = True
+            if not _tensor_subclass_transform_applied:
+                computation_trc, _ = flatten_tensor_subclasses(computation_trc)
 
             if backward_trc is None:
                 from thunder.executors.passes import transform_for_execution as transform_for_execution_pass

thunder/core/jit_ext.py

@@ -637,7 +637,7 @@ def _convert_pytorchfunc_to_thundertrace(
     trace = TraceCtx()
     trace.bound_symbols.extend(active_jit_ctx.computation_trace.pop_scope())
     func_result = unwrap(wrapped_func_result)
-    if shallow_copy_output:
+    if shallow_copy_output and not trace.bound_symbols:
         from thunder.core.baseutils import sequencify
 
         out_to_shallow_copy: dict[Variable, TensorProxy] = {}

thunder/core/pytree.py

@@ -1,6 +1,7 @@
 from functools import partial
 from types import FunctionType
 import dataclasses
+from enum import Enum
 
 import optree
 import torch
@@ -64,6 +65,7 @@ def tree_flatten(args, namespace=OPTREE_NAMESPACE):
         and not is_likely_from_collections_namedtuple(args)
         and not dataclasses.is_dataclass(args)
         and not type(args).__module__.startswith("torch.return_types")
+        and not issubclass(type(args), Enum)
     ):
         raise TypeError(f"tree_flatten of type {type(args)} is not supported.")
     return optree.tree_flatten(args, none_is_leaf=True, namespace=namespace)

thunder/executors/torch_autograd.py

@@ -132,6 +132,7 @@ def split_forward_backward(computation_trc: TraceCtx, compile_data, compile_stat
     from thunder.distributed.transforms import FSDPCommBucketing
     from thunder.distributed.utils import sort_data_parallel_syncs, sort_waits, sort_communication_ops
     from thunder.executors.passes import del_last_used, transform_for_execution
+    from thunder.transforms.tensor_subclasses import flatten_tensor_subclasses, DesugarTensorSubclass
 
     utils.check(compile_data is not None, lambda: "`compile_data` is required")
     # NOTE: This function is rather slow, so it's intended to be used
@@ -154,6 +155,7 @@ def split_forward_backward(computation_trc: TraceCtx, compile_data, compile_stat
     # not any other container type. So we need to flatten the outputs of
     # the forward trace and inputs of the backward trace.
     fw_trace, bw_trace = forward_and_backward_from_trace(primal_trace, torch_autograd=True)
+    fw_trace, fw_tensor_subclass_desugar = flatten_tensor_subclasses(fw_trace)
 
     fw_traces = [fw_trace]
     bw_traces = [bw_trace]
@@ -245,6 +247,8 @@ def split_forward_backward(computation_trc: TraceCtx, compile_data, compile_stat
     if getattr(compile_data.fn, "use_fsdp", False):
         bw_trace = _fsdp_comm_bucketing.apply_bucketing_to_backward_trace(bw_trace)
 
+    bw_trace, bw_tensor_subclass_desugar = flatten_tensor_subclasses(bw_trace)
+
     # Now we can run the optimization passes on the backward trace
     # TODO Restore request for no rematerialization
     bw_extrace = transform_for_execution(

thunder/executors/torchex.py

@@ -1408,9 +1408,6 @@ def _scaled_mm_transform(
     if b.stride()[0] != 1 and b.stride()[1] > 1:
         b = b.t().contiguous().t()
 
-    print(
-        f"{type(a)=}, {type(b)=}, {type(scale_a)=}, {type(scale_b)=}, {type(bias)=}, {type(scale_result)=}, {type(result_dtype)=}, {type(use_fast_accum)=}"
-    )
     return _scaled_mm(a, b, scale_a, scale_b, bias, scale_result, result_dtype, use_fast_accum)
 
 

thunder/tests/test_tensor_subclass.py

@@ -248,9 +248,6 @@ def g(x: ScaleTensorSubclass, data: torch.Tensor, scale: torch.Tensor) -> torch.
             not (TORCHAO_AVAILABLE and torch.cuda.get_device_capability() >= (8, 9)),
             reason="Requires capability >= 8.9 and torchao",
         ),
-        # forward-backward split is failing.
-        # TypeError: tree_flatten of type <enum 'GemmInputRole'> is not supported.
-        pytest.mark.xfail(),
     ),
 )
 def test_torchao_float8_linear(executor, device, _):
@@ -269,3 +266,5 @@ def test_torchao_float8_linear(executor, device, _):
 
     jitted = executor.make_callable(fp8_model)
     actual = jitted(x)
+
+    torch.testing.assert_close(actual, expected)

thunder/torch/__init__.py

@@ -1259,7 +1259,9 @@ def t(a: TensorLike, /) -> TensorLike:
         lambda: f"t() expects a tensor with <= 2 dimensions, but self is {a.ndim}D",
         RuntimeError,
     )
-    return transpose(a, 0, 1) if a.ndim == 2 else a
+    if a.ndim == 2:
+        return transpose(a, 0, 1)
+    return a
 
 
 @run_once
@@ -1312,6 +1314,17 @@ def transpose(a: TensorLike, /, dim0: int, dim1: int) -> TensorLike:
     return clang.transpose(a, permutation)
 
 
+def _transpose_grad(a: TensorLike, /, dim0: int, dim1: int) -> TensorLike:
+    fwd = transpose(a, dim0, dim1)
+    g = get_grad(fwd)
+    a_grad = transpose(g, dim0, dim1)
+    put_grad(a, a_grad)
+    return fwd
+
+
+register_grad(transpose, _transpose_grad)
+
+
 @torchsymbol(torch.unbind, is_method=True)
 def unbind(a: TensorLike, /, dim: int = 0) -> tuple[TensorLike, ...]:
     utils.check(

thunder/transforms/tensor_subclasses.py

@@ -41,10 +41,10 @@
     from torch.fx import GraphModule
     from torch._ops import OpOverload
     from thunder.core.symbol import Symbol, BoundSymbol
-    from torch._C import _TensorMeta
 
 
 __all__ = [
+    "DesugarTensorSubclass",
     "flatten_tensor_subclasses",
 ]
 
@@ -249,17 +249,18 @@ def translate_fx_graph_into_bsym(
         import thunder.torch as ltorch
 
         unwrapped_bsym_args: dict[int, ProxyInterface] = {}
-        list_of_unflatten_bsym: list[BoundSymbol] = []
+        list_of_flattening_bsyms: list[BoundSymbol] = []
         for a in bsym.flat_args:
             if isinstance(a, SubclassTensorProxy):
                 if variableify(a) in self.subclass_proxy_to_flatten:
                     self.computation_trace.push_scope([])
                     with tracectx(self.computation_trace):
                         prims.flatten_tensor_subclass(a)
-                    unflatten_bsym = self.computation_trace.pop_scope()[0]
-                    list_of_unflatten_bsym.append(unflatten_bsym)
+                    flattening_bsym = self.computation_trace.pop_scope()[0]
+                    list_of_flattening_bsyms.append(flattening_bsym)
                 tensor_attr_names = self._get_tensor_attr_names(a)
                 tensors = a._tensors
+
                 non_tensor_attr_names = self._get_non_tensor_attr_names(a)
                 non_tensors = a._non_tensors
                 metadata = dict(zip(non_tensor_attr_names, non_tensors))
@@ -307,8 +308,8 @@ def translate_fx_graph_into_bsym(
             ltorch_ops_for_node_of_ops.append(getattr(ltorch, node.target._opname))
 
         bsyms: list[BoundSymbol] = []
-        if list_of_unflatten_bsym:
-            bsyms.extend(list_of_unflatten_bsym)
+        if list_of_flattening_bsyms:
+            bsyms.extend(list_of_flattening_bsyms)
         fxnode_output_name_to_tensor_proxy: dict[str, OpOverload] = {}
         for node, ltorch_op in zip(list_of_function_call_node, ltorch_ops_for_node_of_ops):
             args: list[Node] = node.args
@@ -379,10 +380,22 @@ def translate_fx_graph_into_bsym(
                     f"{len(new_tensor_proxies)=} != {len(orig_output._tensors)=}"
                 ),
             )
-            if [variableify(t) for t in orig_output._tensors] != [variableify(t) for t in new_tensor_proxies]:
-                orig_output._tensors = new_tensor_proxies
-                for name, tensor in zip(orig_output._tensor_attr_names, new_tensor_proxies):
-                    setattr(orig_output, name, tensor)
+            with tracectx(self.computation_trace):
+                new_subclass = orig_output.replace()
+            new_subclass._tensors = new_tensor_proxies
+            for name, value in zip(new_subclass._tensor_attr_names, new_tensor_proxies):
+                setattr(new_subclass, name, value)
+            bsyms.append(
+                prims.unflatten_tensor_subclass.bind(
+                    new_subclass._subclass_type,
+                    dict(zip(new_subclass._tensor_attr_names, new_tensor_proxies)),
+                    dict(zip(new_subclass._non_tensor_attr_names, new_subclass._non_tensors)),
+                    output=new_subclass,
+                )
+            )
+
+            self.swap_map[variableify(orig_output)] = new_subclass
+            self.subclass_proxy_to_flatten.add(variableify(new_subclass))
 
         else:
             non_none_args = [n for n in node_of_output.args[0] if n is not None]
@@ -502,7 +515,12 @@ def f_with_wrap_and_unwrap(*desugared_args) -> tuple[OutputWrapperForFxTracing,
 
     def __call__(self, bsym: BoundSymbol) -> list[BoundSymbol]:
         updated_bsym: BoundSymbol = bsym.from_bsym_swap_proxies(self.swap_map)
-        if updated_bsym.sym.id == prims.PrimIDs.RETURN:
+        if bsym.sym.id == prims.PrimIDs.RETURN:
+            new_swap_map = {}
+            for k, v in self.swap_map.items():
+                if isinstance(v, SubclassTensorProxy):
+                    continue
+                new_swap_map[k] = v
             if not self.subclass_proxy_to_flatten or True:
                 return [updated_bsym]
 
@@ -567,7 +585,7 @@ def __call__(self, bsym: BoundSymbol) -> list[BoundSymbol]:
         return self.translate_fx_graph_into_bsym(bsym_with_modified_output, fx)
 
 
-def flatten_tensor_subclasses(computation_trace: TraceCtx) -> TraceCtx:
+def flatten_tensor_subclasses(computation_trace: TraceCtx) -> tuple[TraceCtx, DesugarTensorSubclass]:
     """Flatten tensor subclasses in ``computation_trace``.
 
     Two things are happening inside of this function:
@@ -601,9 +619,9 @@ def flatten_tensor_subclasses(computation_trace: TraceCtx) -> TraceCtx:
         updated_bsyms.extend(maybe_desugared_bsyms)
 
     if not desugar_tensor_subclass.subclass_proxy_to_flatten:
-        return computation_trace
+        return computation_trace, None
 
     computation_trace_with_subclass_tensor_proxy_output = from_trace(computation_trace)
     computation_trace_with_subclass_tensor_proxy_output.bound_symbols.extend(updated_bsyms)
     computation_trace_with_subclass_tensor_proxy_output.set_provenance(TraceProvenance("tensor subclasses desugared"))
-    return computation_trace_with_subclass_tensor_proxy_output
+    return computation_trace_with_subclass_tensor_proxy_output, desugar_tensor_subclass