support no_grad in thunder.jit

thunder/__init__.py

@@ -442,6 +442,11 @@ def get_computation_and_inputs(*args, **kwargs):
         # which seems to break the consistency of cache_info, leading to a failure in cache_info check.
         cache_info["alias_tensor_indices"] = _alias_tensor_of_args_kwargs(*args, **kwargs)
 
+        # Store the `is_grad_enabled` state of PyTorch. This is used by vjp transform
+        # to treat certain Symbols as constant.
+        cache_info["is_grad_enabled"] = pytorch.is_grad_enabled()
+        cd.is_grad_enabled = pytorch.is_grad_enabled()
+
         # TODO RC1 Add module and function checks to prologue (make it a compile option)
 
         # Checks cache

thunder/common.py

@@ -221,6 +221,10 @@ def __init__(
         # State for pytorch autocast context managers.
         self.autocast_stack: AutocastStack = AutocastStack()
 
+        # State to query whether grad is enabled or disabled using
+        # torch.no_grad/torch.enable_grad/torch._C._set_grad_enabled
+        self.is_grad_enabled: bool = True
+
         #
         # Gathers additional metadata
         #

thunder/core/symbol.py

@@ -21,7 +21,8 @@
 from thunder.core.pytree import tree_flatten_with_dataclass, tree_unflatten, tree_map
 import thunder.core.dtypes as dtypes
 import thunder.core.devices as devices
-from thunder.core.proxies import Proxy, NumberProxy, variableify, CollectionProxy
+from thunder.core.proxies import Proxy, TensorProxy, NumberProxy, variableify, CollectionProxy, ProxyTag
+from thunder.core.compile_data import get_compile_data
 
 from thunder.core.trace import (
     get_tracectx,
@@ -320,6 +321,20 @@ def __call__(self, *args, **kwargs):
             result = self.meta(*args, **kwargs)
             trace.pop_scope()
 
+        cd = get_compile_data()
+        if cd is not None and not cd.is_grad_enabled:
+            # If grad is disabled using `torch.no_grad` or `torch._C._set_grad_enabled(False)`,
+            # tag the results with `DETACHED_AUTOGRAD_GRAPH` which makes this Symbol a constant for
+            # vjp transform (applied later).
+            def tag_tensorproxy_output_as_detached(proxy):
+                if isinstance(proxy, TensorProxy):
+                    # We need to remove name from trace, otherwise replace will return a proxy with new name.
+                    trace.names.remove(proxy.name)
+                    return proxy.replace(tags=(ProxyTag.DETACHED_AUTOGRAD_GRAPH,))
+                return proxy
+
+            result = tree_map(tag_tensorproxy_output_as_detached, result)
+
         bsym = self.bind(*args, **kwargs, output=result, subsymbols=subsymbols)
         symbols_list = trace.peek_scope()
 

thunder/core/transforms.py

@@ -33,6 +33,7 @@
     variableify,
     unvariableify,
     FutureTensorProxy,
+    ProxyTag,
 )
 from thunder.core.compile_data import get_compile_data, get_compile_option
 from thunder.core.langctxs import langctx, Languages
@@ -2485,10 +2486,17 @@ def is_constant_for_vjp(symbol: prims.Symbol) -> bool:
         bool: True if the symbol is constant, False otherwise.
     """
     are_all_args_non_differentiable = not any(isinstance(arg, (FloatProxy, TensorProxy)) for arg in symbol.flat_args)
+    # Symbol's tag their output in `torch.no_grad` regions with `DETACHED_AUTOGRAD_GRAPH`.
+    # These are treated as constant for VJP.
+    # NOTE - `any(()) is False`
+    output_disconnected_from_graph = any(
+        ProxyTag.DETACHED_AUTOGRAD_GRAPH in o.tags for o in symbol.flat_outs if isinstance(o, TensorProxy)
+    )
     return (
         are_all_args_non_differentiable
         or symbol.are_all_args_constant
         or symbol.sym.id in nondifferentiable_vjp_symbols
+        or output_disconnected_from_graph
     )
 
 

thunder/tests/test_core.py

@@ -2099,7 +2099,7 @@ def func(x):
     compiled = executor.make_callable(func)
     out = compiled(x)
     assert out is x
-    initial_trace_with_dce = thunder.last_traces(compiled)[3]
+    initial_trace_with_dce = thunder.last_traces(compiled)[4]
     assert "Constructed by Dead Code Elimination" in str(initial_trace_with_dce)
     assert len(initial_trace_with_dce.bound_symbols) == 2
     assert initial_trace_with_dce.bound_symbols[0].sym.id == prims.PrimIDs.UNPACK_TRIVIAL
@@ -2480,27 +2480,57 @@ def foo_error(args):
 
 
 def test_grad_ctx():
+    # Test `enable_grad` on a function works correctly
     @torch.enable_grad()
     def foo1(x):
         return x + 1
 
     x = torch.randn(3, 3, requires_grad=True)
-    with pytest.warns(UserWarning, match="have no effect under thunder.jit"):
-        thunder.jit(foo1)(x).sum().backward()
-
+    thunder.jit(foo1)(x).sum().backward()
     assert x.grad is not None
 
+    # Test `no_grad` on a function works correctly
     @torch.no_grad()
     def foo2(x):
         return x + 1
 
     x = torch.randn(3, 3, requires_grad=True)
-    with pytest.warns(UserWarning, match="have no effect under thunder.jit"):
-        thunder.jit(foo2)(x).sum().backward()
+    thunder.jit(foo2)(x).sum().backward()
+    assert x.grad is None
 
-    # `torch.no_grad` has no effect on thunder's autodiff which determines whether to compute grad based on `requires_grad=True`.
-    # Thus when backward is called it computes grad for the input.
-    assert x.grad is not None
+    # Test `no_grad` ctx correctly disable gradient computation
+    def foo3(x):
+        with torch.no_grad():
+            y = x * 3
+        return x * 2 + y
+
+    x = torch.randn(3, 3, requires_grad=True)
+    with torch.no_grad():
+        x_ref = x.clone()
+        x_ref.requires_grad_(True)
+
+    foo3(x_ref).sum().backward()
+    thunder.jit(foo3)(x).sum().backward()
+    # Verify the gradients match
+    torch.testing.assert_close(x.grad, x_ref.grad)
+
+    # Test nested `no_grad` and `enable_grad`
+    def foo4(x):
+        with torch.enable_grad():
+            with torch.no_grad():
+                y = x * 3
+            z = x * 4
+        return x * 2 + y + z
+
+    x = torch.randn(3, 3, requires_grad=True)
+    with torch.no_grad():
+        x_ref = x.clone()
+        x_ref.requires_grad_(True)
+
+    foo4(x_ref).sum().backward()
+    thunder.jit(foo4)(x).sum().backward()
+    # Verify the gradients match
+    torch.testing.assert_close(x.grad, x_ref.grad)
 
 
 def test_serialize_trace():

thunder/torch/__init__.py

@@ -47,6 +47,7 @@
     ListProxy,
     DictProxy,
     numberproxy,
+    ProxyTag,
 )
 from thunder.core.pytree import tree_map, tree_flatten, tree_unflatten
 from thunder.core.symbol import Symbol
@@ -5226,11 +5227,24 @@ def torch_device(type: DeviceLike, index: int | None = None) -> devices.Device:
 register_function(torch.device, torch_device)
 
 
-def _set_grad_enabled_with_warning(enabled: bool) -> None:
-    warnings.warn("torch.enable_grad/torch.no_grad/torch._C._set_grad_enabled have no effect under thunder.jit")
+# Tag to use on Proxies created in `no_grad` regions.
+# VJP transform will treat BoundSymbol's whose output has these tags
+# as constant.
+ProxyTag.register_tag("DETACHED_AUTOGRAD_GRAPH")
 
 
-register_function(torch._C._set_grad_enabled, _set_grad_enabled_with_warning)
+# This is just a marker Symbol. `tag_no_grad_symbols_pass` pass uses these symbols
+# to find the `no_grad` regions and mark the BoundSymbols within them as constant
+# for VJP using the `DETACHED_AUTOGRAD_GRAPH` tag.
+@torchsymbol(torch._C._set_grad_enabled, id="set_grad_enabled", tags=(prims.OpTags.CTX_MANAGER_ENTER_EXIT_OP,))
+def _set_grad_enabled_with_warning(enabled: bool) -> None:
+    cd = get_compile_data()
+    if cd is None:
+        warnings.warn(
+            "torch.enable_grad/torch.no_grad/torch._C._set_grad_enabled have no effect, use thunder.jit for correct behaviour"
+        )
+        return
+    get_compile_data().is_grad_enabled = enabled
 
 
 def _unwrap_if_dead(tensor):