wip stats

lint

ghstack-source-id: 9bfc70d6a5fc3288cc749648356d730ed90199da
Pull Request resolved: #93013

rm unused

benchmarks/dynamo/common.py

@@ -1518,9 +1518,14 @@ def run_one_model(
             )
             print(status)
         if self.args.timing:
-            from torch._dynamo.utils import print_time_report
+            from torch._dynamo.utils import op_count, print_time_report
+            from torch.utils._stats import simple_call_counter
 
             print_time_report()
+            stats = f"STATS: call_* op count: {op_count}"
+            for key, value in simple_call_counter.items():
+                stats = f"{stats} | {key}:{value}"
+            print(stats)
 
         end_calls_captured = torch._dynamo.utils.counters["stats"]["calls_captured"]
         end_unique_graphs = torch._dynamo.utils.counters["stats"]["unique_graphs"]

benchmarks/dynamo/parse_logs.py

@@ -46,7 +46,22 @@ def chunker(seq, size):
 i = 0
 
 out = csv.writer(sys.stdout, dialect="excel")
-out.writerow(["", hash, "", "", "", "", gist_url])
+out.writerow(
+    [
+        "",
+        hash,
+        "",
+        "",
+        "",
+        "",
+        gist_url,
+        "frame_time",
+        "backend_time",
+        "total_ops",
+        "fake_tensor_dispatch_calls",
+        "proxy_torch_dispatch_calls",
+    ]
+)
 
 # Sometimes backtraces will be in third party code, which results
 # in very long file names.  Delete the absolute path in this case.
@@ -130,6 +145,22 @@ def normalize_file(f):
         if len(split_str) == 2:
             backend_time = float(split_str[1])
             frame_time = float(split_str[0].split("entire_frame_compile:")[1])
+
+    tot_ops = None
+    fm_dispatches = None
+    pm_dispatches = None
+    if "STATS:" in log:
+        result = re.search("STATS:(.*)\n", log).group(1)
+        # call_* op count: 970 | FakeTensor.__torch_dispatch__:35285 | ProxyTorchDispatchMode.__torch_dispatch__:13339
+        split_all = result.split("|")
+
+        if len(split_all) == 3:
+            tot_ops = int(split_all[0].split("call_* op count:")[1])
+            fm_dispatches = int(split_all[0].split("FakeTensor.__torch_dispatch__:")[1])
+            pm_dispatches = int(
+                split_all[0].split("ProxyTorchDispatchMode.__torch_dispatch__:")[1]
+            )
+
     # If the context string is too long, don't put it in the CSV.
     # This is a hack to try to make it more likely that Google Sheets will
     # offer to split columns
@@ -143,7 +174,20 @@ def normalize_file(f):
         context = ""
 
     out.writerow(
-        [bench, name, "", r, component, context, explain, frame_time, backend_time]
+        [
+            bench,
+            name,
+            "",
+            r,
+            component,
+            context,
+            explain,
+            frame_time,
+            backend_time,
+            tot_ops,
+            fm_dispatches,
+            pm_dispatches,
+        ]
     )
     i += 1
 

torch/_dynamo/output_graph.py

@@ -627,6 +627,11 @@ def compile_and_call_fx_graph(self, tx, rv, root):
 
     @dynamo_timed(phase_name="backend_compile")
     def call_user_compiler(self, gm: fx.GraphModule) -> CompiledFn:
+        tot = 0
+        for node in gm.graph.nodes:
+            if node.op in ("call_function", "call_method", "call_module"):
+                tot += 1
+        torch._dynamo.utils.increment_op_count(tot)
         try:
             name = (
                 self.compiler_fn.__name__

torch/_dynamo/utils.py

@@ -50,7 +50,6 @@
 # profiling compilation time
 compilation_metrics = collections.OrderedDict()
 
-
 timer_counter = itertools.count()
 
 
@@ -103,6 +102,14 @@ def reset_frame_count():
     curr_frame = 0
 
 
+op_count = 0
+
+
+def increment_op_count(cnt):
+    global op_count
+    op_count += cnt
+
+
 # Print a report of time spent so far
 # Ex:
 # TIMING:

torch/_ops.py

@@ -239,6 +239,7 @@ def __init__(self, overloadpacket, op, op_dk, schema, tags):
         self._schema = schema
         self._overloadpacket = overloadpacket
         self._tags = tags
+        self.dk_res = None
         self._overloadname = (
             "default" if schema.overload_name == "" else schema.overload_name
         )
@@ -301,9 +302,14 @@ def decompose(self, *args, **kwargs):
             # apply Python CompositeImplicitAutograd *before* tracing
             # using Python dispatcher (also taking advantage of the autograd
             # formula).  But it's included for completeness
-            return self.py_kernels[dk](*args, **kwargs)
+            self.dk_res = self.py_kernels[dk](*args, **kwargs)
+            return self.dk_res
         elif torch._C._dispatch_has_kernel_for_dispatch_key(self.name(), dk):
-            return self._op_dk(dk, *args, **kwargs)
+            self.dk_res = self._op_dk(dk, *args, **kwargs)
+            from torch.fx.experimental.proxy_tensor import get_isolated_graphmodule
+
+            # with no_dispatch():
+            return self.dk_res
         else:
             return NotImplemented
 

torch/_subclasses/fake_tensor.py

@@ -20,6 +20,7 @@
 from torch.utils._python_dispatch import TorchDispatchMode
 
 from torch.utils._pytree import PyTree, tree_flatten, tree_map, tree_map_only
+from torch.utils._stats import count
 from torch.utils.weak import WeakIdRef
 
 pytree = torch.utils._pytree
@@ -623,6 +624,7 @@ def __repr__(self):
         return f"FakeTensor({self_repr}, {self.fake_device})"
 
     @classmethod
+    @count
     def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
         # need to handle here to avoid infinite recursion
         # see [in_kernel_invocation]

torch/fx/experimental/proxy_tensor.py

@@ -18,6 +18,7 @@
 from dataclasses import dataclass
 import weakref
 import operator
+from torch.utils._stats import count
 
 from torch.utils._python_dispatch import TorchDispatchMode, _pop_mode_temporarily, _get_current_dispatch_mode
 from torch._subclasses import FakeTensor
@@ -477,6 +478,7 @@ def __init__(self, tracer, tracing_mode):
         self.trace_state = {}
         self._managers = []
 
+    @count
     def __torch_dispatch__(self, func, types, args=(), kwargs=None):
         with self.sym_mode.enable(False):
             return self.inner_torch_dispatch(func, types, args, kwargs)

torch/utils/_stats.py

@@ -0,0 +1,16 @@
+# NOTE! PLEASE KEEP THIS FILE *FREE* OF TORCH DEPS! IT SHOULD BE IMPORTABLE ANYWHERE.
+# IF YOU FEEL AN OVERWHELMING URGE TO ADD A TORCH DEP, MAKE A TRAMPOLINE FILE A LA torch._dynamo.utils
+# AND SCRUB AWAY TORCH NOTIONS THERE.
+import collections
+import functools
+
+simple_call_counter = collections.OrderedDict()
+
+def count(fn):
+    @functools.wraps(fn)
+    def wrapper(*args, **kwargs):
+        if fn.__qualname__ not in simple_call_counter:
+            simple_call_counter[fn.__qualname__] = 0
+        simple_call_counter[fn.__qualname__] = simple_call_counter[fn.__qualname__] + 1
+        return fn(*args, **kwargs)
+    return wrapper