diff --git a/vllm_ascend/compilation/piecewise_backend.py b/vllm_ascend/compilation/piecewise_backend.py
new file mode 100644
index 000000000..5fdf6a908
--- /dev/null
+++ b/vllm_ascend/compilation/piecewise_backend.py
@@ -0,0 +1,213 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import dataclasses
+from contextlib import ExitStack
+from typing import Any, Callable, Dict, List, Optional, Set
+from unittest.mock import patch
+
+import torch
+import torch.fx as fx
+
+import vllm.envs as envs
+from vllm.compilation.backends import VllmBackend
+from vllm.compilation.counter import compilation_counter
+from vllm.compilation.monitor import end_monitoring_torch_compile
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.utils import weak_ref_tensors
+
+
+logger = init_logger(__name__)
+
+@dataclasses.dataclass
+class ConcreteSizeEntry:
+    runtime_shape: int
+    need_to_compile: bool  # the size is in compile_sizes
+    use_npugraph: bool  # the size is in cudagraph_capture_sizes
+
+    compiled: bool = False
+    runnable: Callable = None  # type: ignore
+    num_finished_warmup: int = 0
+    npugraph: Optional[torch.npu.NPUGraph] = None
+    output: Optional[Any] = None
+
+    # for aclgraph debugging, track the input addresses
+    # during capture, and check if they are the same during replay
+    input_addresses: Optional[List[int]] = None
+
+
+class NPUPiecewiseBackend:
+
+    def __init__(self, graph: fx.GraphModule, vllm_config: VllmConfig,
+                 graph_pool: Any, piecewise_compile_index: int,
+                 total_piecewise_compiles: int, sym_shape_indices: List[int],
+                 compiled_graph_for_general_shape: Callable,
+                 vllm_backend: VllmBackend):
+        """
+        The backend for piecewise compilation.
+        It mainly handles the compilation and aclgraph capturing.
+
+        We will compile `self.graph` once for the general shape,
+        and then compile for different shapes specified in
+        `compilation_config.compile_sizes`.
+
+        Independently, we will capture aclgraph for different shapes.
+
+        If a shape needs both compilation and aclgraph, we will
+        compile it first, and then capture aclgraph.
+        """
+        self.graph = graph
+        self.vllm_config = vllm_config
+        self.compilation_config = vllm_config.compilation_config
+        self.graph_pool = graph_pool
+        self.piecewise_compile_index = piecewise_compile_index
+        self.total_piecewise_compiles = total_piecewise_compiles
+        self.vllm_backend = vllm_backend
+
+        self.is_first_graph = piecewise_compile_index == 0
+        self.is_last_graph = (
+            piecewise_compile_index == total_piecewise_compiles - 1)
+
+        self.compile_sizes: Set[int] = set(
+            self.compilation_config.compile_sizes)
+        self.cudagraph_capture_sizes: Set[int] = set(
+            self.compilation_config.cudagraph_capture_sizes
+        ) if self.compilation_config.use_cudagraph else set()
+
+        self.first_run_finished = False
+
+        self.compiled_graph_for_general_shape = compiled_graph_for_general_shape  # noqa
+
+        self.sym_shape_indices = sym_shape_indices
+
+        self.is_debugging_mode = envs.VLLM_LOGGING_LEVEL == "DEBUG"
+
+        # the entries for different shapes that we need to either
+        # compile or capture aclgraph
+        self.concrete_size_entries: Dict[int, ConcreteSizeEntry] = {}
+
+        # to_be_compiled_sizes tracks the remaining sizes to compile,
+        # and updates during the compilation process, so we need to copy it
+        self.to_be_compiled_sizes: Set[int] = self.compile_sizes.copy()
+        for shape in self.compile_sizes.union(self.cudagraph_capture_sizes):
+            self.concrete_size_entries[shape] = ConcreteSizeEntry(
+                runtime_shape=shape,
+                need_to_compile=shape in self.compile_sizes,
+                use_npugraph=shape in self.cudagraph_capture_sizes,
+            )
+
+    def check_for_ending_compilation(self):
+        if self.is_last_graph and not self.to_be_compiled_sizes:
+            # no specific sizes to compile
+            # save the hash of the inductor graph for the next run
+            self.vllm_backend.compiler_manager.save_to_file()
+            end_monitoring_torch_compile(self.vllm_config)
+
+    def __call__(self, *args) -> Any:
+        if not self.first_run_finished:
+            self.first_run_finished = True
+            self.check_for_ending_compilation()
+            return self.compiled_graph_for_general_shape(*args)
+
+        runtime_shape = args[self.sym_shape_indices[0]]
+        if runtime_shape not in self.concrete_size_entries:
+            # we don't need to do anything for this shape
+            return self.compiled_graph_for_general_shape(*args)
+
+        entry = self.concrete_size_entries[runtime_shape]
+
+        if entry.runnable is None:
+            entry.runnable = self.compiled_graph_for_general_shape
+
+        if entry.need_to_compile and not entry.compiled:
+            entry.compiled = True
+            self.to_be_compiled_sizes.remove(runtime_shape)
+            # args are real arguments
+            entry.runnable = self.vllm_backend.compiler_manager.compile(
+                self.graph,
+                args,
+                self.compilation_config.inductor_compile_config,
+                self.compilation_config,
+                graph_index=self.piecewise_compile_index,
+                num_graphs=self.total_piecewise_compiles,
+                runtime_shape=runtime_shape)
+
+            # finished compilations for all required shapes
+            if self.is_last_graph and not self.to_be_compiled_sizes:
+                self.check_for_ending_compilation()
+
+        if not entry.use_npugraph:
+            return entry.runnable(*args)
+
+        if entry.npugraph is None:
+            if entry.num_finished_warmup < self.compilation_config.cudagraph_num_of_warmups:  # noqa
+                entry.num_finished_warmup += 1
+                if self.is_first_graph:
+                    logger.debug(
+                        "Warming up %s/%s for shape %s",
+                        entry.num_finished_warmup,
+                        self.compilation_config.cudagraph_num_of_warmups,
+                        runtime_shape)
+                return entry.runnable(*args)
+
+            if self.is_first_graph:
+                # Since we capture aclgraph for many different shapes and
+                # capturing is fast, we don't need to log it for every shape.
+                # We only log it in the debug mode.
+                logger.debug("Capturing a aclgraph for shape %s",
+                             runtime_shape)
+
+            input_addresses = [
+                x.data_ptr() for x in args if isinstance(x, torch.Tensor)
+            ]
+            entry.input_addresses = input_addresses
+            npugraph = torch.npu.NPUGraph()
+
+            with ExitStack() as stack:
+                if not self.is_first_graph:
+                    # during every model forward, we will capture
+                    # many pieces of aclgraphs (roughly one per layer).
+                    # running gc again and again across layers will
+                    # make the aclgraph capture very slow.
+                    # therefore, we only run gc for the first graph,
+                    # and disable gc for the rest of the graphs.
+                    stack.enter_context(patch("gc.collect", lambda: None))
+                    stack.enter_context(
+                        patch("torch.npu.empty_cache", lambda: None))
+
+                # mind-exploding: carefully manage the reference and memory.
+                with torch.npu.graph(npugraph, pool=self.graph_pool):
+                    # `output` is managed by pytorch's npugraph pool
+                    output = entry.runnable(*args)
+                    if self.is_last_graph:
+                        # by converting it to weak ref,
+                        # the original `output` will immediately be released
+                        # to save memory. It is only safe to do this for
+                        # the last graph, because the output of the last graph
+                        # will not be used by any other cuda graph.
+                        output = weak_ref_tensors(output)
+
+            # here we always use weak ref for the output
+            # to save memory
+            entry.output = weak_ref_tensors(output)
+            entry.npugraph = npugraph
+
+            compilation_counter.num_cudagraph_caputured += 1
+
+            # important: we need to return the output, rather than
+            # the weak ref of the output, so that pytorch can correctly
+            # manage the memory during cuda graph capture
+            return output
+
+        if self.is_debugging_mode:
+            # check if the input addresses are the same
+            new_input_addresses = [
+                x.data_ptr() for x in args if isinstance(x, torch.Tensor)
+            ]
+            assert new_input_addresses == entry.input_addresses, (
+                "Input addresses for aclgraphs are different during replay."
+                f" Expected {entry.input_addresses}, got {new_input_addresses}"
+            )
+
+        entry.npugraph.replay()
+        return entry.output
diff --git a/vllm_ascend/platform.py b/vllm_ascend/platform.py
index fbc1dc688..a48047706 100644
--- a/vllm_ascend/platform.py
+++ b/vllm_ascend/platform.py
@@ -121,8 +121,7 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
             enforce_eager = getattr(vllm_config.model_config, "enforce_eager",
                                     False)
 
-        # TODO(Yizhou): Override the value of enforce_eager to True before
-        # the CANN and torch_npu support NPU compilation.
+        # TODO: revert me when the fallback of aclgraph is done.
         enforce_eager = True
         logger.warning(
             "NPU compilation support pending. Will be available in future CANN and "
@@ -237,3 +236,10 @@ def supports_v1(cls, model_config: ModelConfig) -> bool:
         model configuration.
         """
         return True
+
+    @classmethod
+    def get_piecewise_backend_cls(cls) -> str:
+        """
+        Get piecewise backend class for piecewise graph.
+        """
+        return "vllm_ascend.compilation.piecewise_backend.NPUPiecewiseBackend"  # noqa