fix get_hash function

py/torch_tensorrt/dynamo/_engine_cache.py

@@ -6,11 +6,10 @@
 import pickletools
 import shutil
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional, Sequence, Tuple, cast
+from typing import Any, Dict, List, Optional, Sequence, Tuple
 
 import torch
-from torch._inductor.codecache import FxGraphCachePickler, sha256_hash
-from torch.fx.experimental.proxy_tensor import unset_fake_temporarily
+from torch._inductor.codecache import sha256_hash
 from torch_tensorrt._Input import Input
 from torch_tensorrt.dynamo._settings import (
     _SETTINGS_TO_BE_ENGINE_INVARIANT,
@@ -49,17 +48,38 @@ def get_hash(
 
         Args:
             gm (torch.fx.GraphModule): GraphModule to hash
+            input_specs (Sequence[Input]): input specs for the GraphModule
+            settings (CompilationSettings): compilation settings for the GraphModule
 
         Returns:
             str: hash value of the GraphModule
         """
-        # parameters are set to 0
-        with unset_fake_temporarily():
-            new_gm = copy.deepcopy(gm)
-            for name, param in new_gm.named_parameters():
-                param.data.zero_()
 
-            graph_hash_val = cast(str, FxGraphCachePickler.get_hash(new_gm))
+        def canonicalize_graph(graph: torch.fx.Graph) -> str:
+            """Canonicalize the graph to a string for isomorphic graph comparison
+
+            Args:
+                graph (torch.fx.Graph): graph to canonicalize
+
+            Returns:
+                str: canonicalized graph string
+            """
+            canonical_nodes = []
+            input_counter = 0
+
+            for node in graph.nodes:
+                if node.op == "placeholder":
+                    canonical_nodes.append(f"placeholder_input_{input_counter}")
+                    input_counter += 1
+                else:
+                    canonical_nodes.append(f"{node.op}_{node.target}")
+
+            return " ".join(canonical_nodes)
+
+        graph_str = canonicalize_graph(gm.graph)
+        _LOGGER.debug(f"graph_str:\n {graph_str}")
+
+        graph_hash = sha256_hash(graph_str.encode())
 
         input_spec_strs = [str(i) for i in input_specs]
         with io.BytesIO() as stream:
@@ -75,7 +95,7 @@ def get_hash(
             engine_specs_data = pickletools.optimize(engine_specs_data)
         engine_specs_hash = sha256_hash(engine_specs_data)
 
-        hash_val: str = graph_hash_val + input_specs_hash + engine_specs_hash
+        hash_val: str = graph_hash + input_specs_hash + engine_specs_hash
 
         return hash_val
 

tests/py/dynamo/models/test_engine_cache.py

@@ -231,7 +231,6 @@ def remove_timing_cache(path=TIMING_CACHE_PATH):
             )
             end.record()
             torch.cuda.synchronize()
-            torch._dynamo.reset()
             times.append(start.elapsed_time(end))
             results.append(trt_gm(*inputs))
 
@@ -396,7 +395,6 @@ def remove_timing_cache(path=TIMING_CACHE_PATH):
                     "reuse_cached_engines": reuse_cached_engines,
                     "engine_cache_dir": engine_cache_dir,
                     "engine_cache_size": 1 << 30,  # 1GB
-                    "torch_executed_ops": {"torch.ops.aten.relu.default"},
                 },
             )
             results.append(compiled_model(*inputs))  # trigger the compilation
@@ -441,7 +439,6 @@ def test_torch_compile_with_custom_engine_cache(self):
         start = torch.cuda.Event(enable_timing=True)
         end = torch.cuda.Event(enable_timing=True)
         for i in range(3):
-            # remove timing cache and reset dynamo for engine caching messurement
             if i == 0:
                 cache_built_engines = False
                 reuse_cached_engines = False
@@ -462,7 +459,6 @@ def test_torch_compile_with_custom_engine_cache(self):
                     "cache_built_engines": cache_built_engines,
                     "reuse_cached_engines": reuse_cached_engines,
                     "custom_engine_cache": custom_engine_cache,
-                    "torch_executed_ops": {"torch.ops.aten.relu.default"},
                 },
             )
             results.append(compiled_model(*inputs))  # trigger the compilation