Simplify function implementation returned by thunder.jit for easier instrumentation of different stages

thunder/__init__.py

@@ -458,15 +458,10 @@ def get_computation_and_inputs(*args, **kwargs):
                         _vanilla_args,
                     ) = cache_entry
                     try:
-                        cs.last_prologue_execution_start = time.perf_counter_ns()
                         inps, pro_to_epi = pro(*args, **kwargs)
-                        cs.last_prologue_execution_stop = time.perf_counter_ns()
                     except Exception as _:
                         continue
 
-                    cs.last_trace_host_tracing_start = time.perf_counter_ns()
-                    cs.last_trace_host_tracing_stop = time.perf_counter_ns()
-
                     # Updates cache statistics
                     cs.cache_hits += 1
                     cs.last_traces = comp_traces
@@ -495,12 +490,7 @@ def get_computation_and_inputs(*args, **kwargs):
                     backward_traces,
                 ) = cache_entry
 
-                cs.last_prologue_execution_start = time.perf_counter_ns()
                 inps, pro_to_epi = pro(*args, **kwargs)
-                cs.last_prologue_execution_stop = time.perf_counter_ns()
-
-                cs.last_trace_host_tracing_start = time.perf_counter_ns()
-                cs.last_trace_host_tracing_stop = time.perf_counter_ns()
 
                 # Updates cache statistics
                 cs.cache_hits += 1
@@ -622,6 +612,7 @@ def get_computation_and_inputs(*args, **kwargs):
             )
             prologue_trc = prologue_traces[-1]
             pro = prologue_trc.python_callable(include_decorators=False)
+            pro = prologue_execution_timer(pro)
 
             if epilogue_trc is not None:
                 epilogue = epilogue_trc.python_callable()
@@ -637,9 +628,7 @@ def get_computation_and_inputs(*args, **kwargs):
             cs.last_interpreter_log = last_interpreter_log
             cs.last_interpreted_instructions = (i for i in last_interpreter_log if isinstance(i, dis.Instruction))
 
-            cs.last_prologue_execution_start = time.perf_counter_ns()
             inps, pro_to_epi = pro(*args, **kwargs)
-            cs.last_prologue_execution_stop = time.perf_counter_ns()
 
             computation_trc = dce(computation_trc)
             computation_traces.append(computation_trc)
@@ -729,23 +718,55 @@ def get_computation_and_inputs(*args, **kwargs):
 
         return cache_entry, inps, pro_to_epi
 
-    cd.get_computation_and_inputs = get_computation_and_inputs
+    def host_execution_timer(fn):
+        def wrapped(*args, **kwargs):
+            cs.last_trace_host_execution_start = time.perf_counter_ns()
+            try:
+                return fn(*args, **kwargs)
+            finally:
+                cs.last_trace_host_execution_stop = time.perf_counter_ns()
 
-    @wraps(fn)
-    def fn_(*args, **kwargs) -> Any:
-        if is_tracing():
-            _recursive_jit_call_warning()
-            return fn(*args, **kwargs)
+        return wrapped
 
-        # Updats call statistics
-        cs.last_trace_host_start = time.perf_counter_ns()
-        cs.calls += 1
+    def prologue_execution_timer(fn):
+        def wrapped(*args, **kwargs):
+            cs.last_prologue_execution_start = time.perf_counter_ns()
+            try:
+                return fn(*args, **kwargs)
+            finally:
+                cs.last_prologue_execution_stop = time.perf_counter_ns()
 
-        cache_entry, inps, pro_to_epi = get_computation_and_inputs(*args, **kwargs)
-        cs.last_trace_host_execution_start = time.perf_counter_ns()
+        return wrapped
+
+    def decorate_computation_function(get_computation_and_inputs_fn, *decorators):
+        def wrapped(*args, **kwargs):
+            cache_entry, inps, pro_to_epi = get_computation_and_inputs_fn(*args, **kwargs)
+            decorated_computation_fn = cache_entry.computation_fn
+            for decorator in decorators:
+                decorated_computation_fn = decorator(decorated_computation_fn)
+            if decorators:
+                cache_entry = cache_entry._replace(computation_fn=decorated_computation_fn)
+            return cache_entry, inps, pro_to_epi
+
+        return wrapped
+
+    get_computation_and_inputs = decorate_computation_function(get_computation_and_inputs, host_execution_timer)
+    cd.get_computation_and_inputs = get_computation_and_inputs
+
+    def update_call_statistics(fn):
+        def wrapped(*args, **kwargs):
+            cs.calls += 1
+            cs.last_trace_host_start = time.perf_counter_ns()
+            try:
+                return fn(*args, **kwargs)
+            finally:
+                cs.last_trace_host_stop = time.perf_counter_ns()
 
+        return wrapped
+
+    def check_storage_aliases(cache_entry, args):
         if cache_entry.vanilla_tensor_args:
-            if alias_tensor_indices_str := _alias_tensor_of_args_kwargs(*inps):
+            if alias_tensor_indices_str := _alias_tensor_of_args_kwargs(*args):
                 alias_tensor_indices = alias_tensor_indices_str
                 alias_tensor_indices = {int(i) for i in alias_tensor_indices_str.split(",")}
                 vanilla_tensor_args = cache_entry.vanilla_tensor_args
@@ -755,13 +776,12 @@ def fn_(*args, **kwargs) -> Any:
                     NotImplementedError,
                 )
 
-        result = cache_entry.computation_fn(*inps)
-
+    def maybe_connect_to_autograd(cache_entry, result):
         if cache_entry.backward_fn:
-            # Run the compiled forward function
+            # If the backward function is available, we need to connect the
+            # resulting tensors to PyTorch's Autograd graph using the
+            # ThunderFunction (which is a torch.autograd.Function subclass)
             data_for_autograd, (saved_tensors, saved_other) = result
-
-            # Connect produced tensors with PyTorch's autograd graph
             ThunderFunction.apply(
                 cache_entry.return_none_instead_of_grads,
                 cache_entry.backward_fn,
@@ -772,17 +792,31 @@ def fn_(*args, **kwargs) -> Any:
             )
             result = data_for_autograd["output"]
 
+        return result
+
+    def maybe_call_epilogue(cache_entry, result, pro_to_epi):
         if cache_entry.epilogue_fn:
             result, comp_to_epi = result
             cache_entry.epilogue_fn(*pro_to_epi, *comp_to_epi)
 
-        cs.last_trace_host_execution_stop = time.perf_counter_ns()
-        cs.last_computation_execution_stop = cs.last_trace_host_execution_stop
+        return result
 
-        cs.last_executed = cache_entry.computation_fn
-        cs.last_trace_cache_stop = time.perf_counter_ns()
-        cs.last_trace_host_stop = time.perf_counter_ns()
+    @wraps(fn)
+    @update_call_statistics
+    def fn_(*args, **kwargs) -> Any:
+        if is_tracing():
+            _recursive_jit_call_warning()
+            return fn(*args, **kwargs)
+
+        cache_entry, inps, pro_to_epi = get_computation_and_inputs(*args, **kwargs)
+
+        check_storage_aliases(cache_entry, inps)
+
+        result = cache_entry.computation_fn(*inps)
+        result = maybe_connect_to_autograd(cache_entry, result)
+        result = maybe_call_epilogue(cache_entry, result, pro_to_epi)
 
+        cs.last_computation = cache_entry.computation_fn
         return result
 
     if isinstance(fn, pytorch.nn.Module):

thunder/common.py

@@ -64,7 +64,6 @@
 
 
 # Holds statistics and caches for a compiled function
-# TODO RC1 Update last_executed to last_computation
 # TODO RC1 Review how autograd traces are presented
 class CompileStats:
     """A class holding statistics and caches for a compiled function.
@@ -76,7 +75,7 @@ class CompileStats:
         See :mod:`thunder` for more of such utility functions.
 
     Attributes:
-        last_executed:
+        last_computation (Callable):
         last_traces (Sequence[TraceCtx]):
         last_prologue (TraceCtx):
         last_prologue_traces (Sequence[TraceCtx]):
@@ -107,7 +106,7 @@ class CompileStats:
 
     def __init__(self):
         # Callables and traces
-        self.last_executed = None
+        self.last_computation = None
         self.last_traces = None
         self.last_prologue = None
         self.last_prologue_traces = None