diff --git a/train/compute/python/tools/et_replay.py b/train/compute/python/tools/et_replay.py
index 6c4a8e02..48731d00 100644
--- a/train/compute/python/tools/et_replay.py
+++ b/train/compute/python/tools/et_replay.py
@@ -3,6 +3,7 @@
 import json
 
 import logging
+import os
 import sys
 import time
 from collections import defaultdict
@@ -18,6 +19,7 @@
 from param_bench.train.compute.python.tools.et_replay_utils import (
     build_fbgemm_func,
     build_torchscript_func,
+    build_triton_func,
     fbgemm_input_args_indices,
     generate_fbgemm_tensors,
     generate_prefix,
@@ -45,6 +47,10 @@
 
 from param_bench.train.compute.python.tools.utility import trace_handler
 from param_bench.train.compute.python.workloads import pytorch as workloads_pytorch
+from torch._inductor.codecache import AsyncCompile, TritonFuture
+
+# grid and split_scan_grid are dynamically loaded
+from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid
 from torch.profiler import ExecutionTraceObserver
 
 
@@ -227,8 +233,6 @@ def initBench(self):
             self.dump_path += "benchmark.py"
         # Multiple traces.
         else:
-            import os
-
             print(f"{os.getpid()} is rank{self.comms_env_params['global_rank']}")
             self.cuda_id = self.comms_env_params["local_rank"]
             self.cuda = f"cuda:{self.comms_env_params['local_rank']}"
@@ -253,6 +257,14 @@ def initBench(self):
 
             self.dump_path += f"benchmark_{self.comms_env_params['global_rank']}.py"
 
+        # base_path is used to find the generated kernel files in the same directory of the trace file.
+        base_path, file_name = os.path.split(self.trace_file)
+        self.resource_dir = os.path.join(
+            base_path, os.path.splitext(file_name)[-2] + "_resources"
+        )
+        self.kernel_map = {}
+        self.async_compile = AsyncCompile()
+
         if self.cpu:
             self.device = torch.device("cpu")
         else:
@@ -364,6 +376,12 @@ def dfs_traverse(root):
         print("#Operators to execute: ", len(self.sorted_nodes))
         for node in self.sorted_nodes:
             anlayze_node(node)
+
+        # triton kernels are compiled in parallel, need to wait until
+        # all kernels are compiled.
+        self.async_compile.wait(globals())
+        del self.async_compile
+
         self.select_parallel_nodes()
 
     def select_parallel_nodes(self):
@@ -618,7 +636,22 @@ def build_func(self, node):
             assert self.fbgemm_backward_ops
             backward_op, forward_id = self.fbgemm_backward_ops.pop(-1)
             return backward_op, len(node.output_types)
-        func, output_count = build_torchscript_func(node)
+
+        if node.kernel_backend == "triton":
+            if node.kernel_file in self.kernel_map:
+                func = self.kernel_map[node.kernel_file]
+                # For a triton kernel, it is the caller's responsibility to allocate
+                # the output tensors, and pass them in as the input arguments.
+                # So the number of the output tensors is always 0
+                output_count = 0
+            else:
+                func, output_count = build_triton_func(
+                    node, self.resource_dir, self.async_compile, self.device
+                )
+                self.kernel_map[node.kernel_file] = func
+        else:
+            func, output_count = build_torchscript_func(node)
+
         if not func:
             self.actual_skip_nodes.append(node.name)
             self.actual_skip_nodes_cnt += 1
@@ -890,6 +923,9 @@ def _generate_run_ops_str(override):
                 func, output_count = self.funcs[node.id]
                 if not func:
                     continue
+                if isinstance(func, TritonFuture):
+                    func = func.result()
+
                 func_str = f"funcs[{node.id}]"
                 inputs_str = _generate_inputs_str(node)
                 outputs_str = _generate_outputs_str(node, override=override)
@@ -1135,7 +1171,17 @@ def run_op(self, node, iter):
         try:
             outputs = []
             if output_count == 0:
-                func(*inputs)
+                if node.kernel_backend == "triton":
+                    # remove the last comma
+                    grid_info = inputs[-2]
+                    index = grid_info.rfind(",")
+                    if index >= 0:
+                        grid_info = grid_info[:index] + grid_info[index + 1 :]
+                    exec(
+                        f"func.run(*inputs[:-2], grid={grid_info}, stream={inputs[-1]})"
+                    )
+                else:
+                    func(*inputs)
             else:
                 if output_count == 1:
                     tmp = (func(*inputs),)
diff --git a/train/compute/python/tools/et_replay_utils.py b/train/compute/python/tools/et_replay_utils.py
index d9ef8190..d9f8f29c 100644
--- a/train/compute/python/tools/et_replay_utils.py
+++ b/train/compute/python/tools/et_replay_utils.py
@@ -1,3 +1,4 @@
+import os
 import re
 
 import torch
@@ -139,7 +140,6 @@ def skip_op(op):
                 and "fbgemm::split_embedding_codegen_lookup_" not in op.name
             )
         )
-        or ("fused" in op.name)
         or (
             op.name
             in [
@@ -453,6 +453,14 @@ def build_torchscript_func(n):
     return func, output_count
 
 
+def build_triton_func(n, resources_dir, async_compile, device):
+    with open(os.path.join(resources_dir, n.kernel_file), "r") as f:
+        code = f.read()
+
+    func = async_compile.triton(n.name, code, device_str=device)
+    return func, 0
+
+
 def generate_prefix(label, skip_nodes, et_input, cuda, compute_only, tf32, rows):
     template_prefix = """import gc
 import argparse
diff --git a/train/compute/python/tools/execution_trace.py b/train/compute/python/tools/execution_trace.py
index 00e23a75..991b37a8 100644
--- a/train/compute/python/tools/execution_trace.py
+++ b/train/compute/python/tools/execution_trace.py
@@ -59,6 +59,9 @@ class NodeType(Enum):
     "All2All_Pooled_Req",
     "All2All_Pooled_Wait",
     "c10d::",
+    "TorchDynamo Cache Lookup",
+    "CompiledFunction",
+    "Torch-Compiled Region",
 ]
 
 
@@ -136,6 +139,8 @@ def __init__(
         output_types: List[str],
         output_shapes: List[Any],
         rf_id: Optional[int] = None,
+        kernel_backend: Optional[str] = None,
+        kernel_file: Optional[str] = None,
     ):
         self.name: str = name
         self.parent_id: int = parent_id
@@ -143,6 +148,8 @@ def __init__(
         self.children: List[Node] = []
         self.id: int = id
         self.rf_id: Optional[int] = rf_id
+        self.kernel_backend: Optional[str] = kernel_backend
+        self.kernel_file: Optional[str] = kernel_file
         self.pid: int = pid
         self.tid: int = tid
         self.fw_tid: int = fw_tid
@@ -298,6 +305,7 @@ def __init__(self, json):
             "1.0.2-chakra.0.0.4": ExecutionTrace._create_node_v1_0_2_chakra_0_0_4,
             # 1.0.3 expands pg name to <pg_name, pg_desc> so it use the same parser as 1.0.2
             "1.0.3-chakra.0.0.4": ExecutionTrace._create_node_v1_0_2_chakra_0_0_4,
+            "1.0.4-chakra.0.0.4": ExecutionTrace._create_node_v1_0_4_chakra_0_0_4,
             # Add future versions here
         }
         create_node = node_creation_func.get(self.schema, None)
@@ -371,6 +379,8 @@ def _read_attrs(node: Dict[str, Any]) -> Tuple:
             "rf_id": int,
             "scope": int,
             "tid": int,
+            "kernel_backend": str,
+            "kernel_file": str,
         }
         attr_dict = {
             attr["name"]: attr_types[attr["name"]](attr["value"])
@@ -378,12 +388,9 @@ def _read_attrs(node: Dict[str, Any]) -> Tuple:
             if attr["name"] in attr_types.keys()
         }
 
-        # Ensure all keys have values
-        if attr_dict.keys() != attr_types.keys():
-            raise ValueError(
-                "Not all keys in attr_dict have updated values. Node:" + str(node)
-            )
-        return tuple(attr_dict[key] for key in attr_types.keys())
+        return tuple(
+            attr_dict[key] for key in attr_types.keys() if key in attr_dict.keys()
+        )
 
     @staticmethod
     def _create_node_v1_0_1(pid, x: Dict[str, Any]) -> Node:
@@ -439,6 +446,42 @@ def _create_node_v1_0_2_chakra_0_0_4(pid, x: Dict[str, Any]) -> Node:
             rf_id,
         )
 
+    @staticmethod
+    def _create_node_v1_0_4_chakra_0_0_4(pid, x: Dict[str, Any]) -> Node:
+        (
+            fw_parent,
+            seq_id,
+            fw_tid,
+            op_schema,
+            rf_id,
+            scope,
+            tid,
+            kernel_backend,
+            kernel_file,
+        ) = ExecutionTrace._read_attrs(x)
+
+        return Node(
+            x["name"],
+            x["id"],
+            x["ctrl_deps"],
+            fw_parent,
+            seq_id,
+            pid,
+            tid,
+            fw_tid,
+            op_schema,
+            scope,
+            x["inputs"]["values"],
+            x["inputs"]["types"],
+            x["inputs"]["shapes"],
+            x["outputs"]["values"],
+            x["outputs"]["types"],
+            x["outputs"]["shapes"],
+            rf_id,
+            kernel_backend,
+            kernel_file,
+        )
+
     def get_nodes(self, clean: bool = False):
         if clean:
             return self.clean_nodes