[torch.compile] avoid Dynamo guard evaluation overhead (vllm-project#…

…7898)

Co-authored-by: Woosuk Kwon <[email protected]>
Signed-off-by: Alvant <[email protected]>

.buildkite/run-tpu-test.sh

@@ -12,4 +12,4 @@ remove_docker_container
 # For HF_TOKEN.
 source /etc/environment
 # Run a simple end-to-end example.
-docker run --privileged --net host --shm-size=16G -it -e HF_TOKEN=$HF_TOKEN --name tpu-test vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git && python3 /workspace/vllm/tests/tpu/test_compilation.py && python3 /workspace/vllm/examples/offline_inference_tpu.py"
+docker run --privileged --net host --shm-size=16G -it -e HF_TOKEN=$HF_TOKEN --name tpu-test vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git && python3 -m pip install pytest  && pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py && python3 /workspace/vllm/tests/tpu/test_compilation.py && python3 /workspace/vllm/examples/offline_inference_tpu.py"

.buildkite/test-pipeline.yaml

@@ -173,6 +173,7 @@ steps:
   - vllm/
   commands:
     - pytest -v -s ./compile/test_full_graph.py
+    - pytest -v -s ./compile/test_wrapper.py
 
 
 - label: Vision Language Models Test # 42min

tests/compile/test_wrapper.py

@@ -0,0 +1,59 @@
+from typing import Optional
+
+import torch
+
+from vllm.compilation.wrapper import TorchCompileWrapperWithCustomDispacther
+
+
+class MyMod(torch.nn.Module):
+
+    def forward(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
+        if cache is not None:
+            return x + cache
+        return x * 2
+
+
+class MyWrapper(TorchCompileWrapperWithCustomDispacther):
+
+    def __init__(self, model):
+        self.model = model
+        compiled_callable = torch.compile(self.forward, backend="eager")
+        super().__init__(compiled_callable)
+
+    def forward(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
+        # this is the function to be compiled
+        return self.model(x, cache)
+
+    def __call__(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
+        # let torch.compile compile twice
+        if len(self.compiled_codes) == 2:
+            dispatch_id = 0 if cache is None else 1
+            with self.dispatch_to_code(dispatch_id):
+                return self.forward(x, cache)
+        else:
+            return self.compiled_callable(x, cache)
+
+
+def test_torch_compile_wrapper():
+    mod = MyMod()
+    wrappers = []
+    for i in range(3):
+        torch._dynamo.reset()
+        wrapper = MyWrapper(mod)
+        wrappers.append(wrapper)
+        x = torch.tensor([1])
+        wrapper(x, None)  # profile run, compile
+        # create a cache tensor
+        cache = torch.tensor([2])
+        wrapper(x, cache)  # warm up with cache, recompile
+
+        # for new input, dispatch to the compiled code directly
+        new_x = torch.tensor([3])
+        assert wrapper(new_x,
+                       None).item() == 6  # dispatch to the first compiled code
+        assert wrapper(
+            new_x, cache).item() == 5  # dispatch to the second compiled code
+
+    for wrapper in wrappers:
+        # make sure they have independent compiled codes
+        assert len(wrapper.compiled_codes) == 2

tests/tpu/test_custom_dispatcher.py

@@ -0,0 +1,9 @@
+from ..utils import compare_two_settings
+
+
+def test_custom_dispatcher():
+    compare_two_settings("google/gemma-2b",
+                         arg1=["--enforce-eager"],
+                         arg2=["--enforce-eager"],
+                         env1={"VLLM_DYNAMO_USE_CUSTOM_DISPATCHER": "0"},
+                         env2={})

vllm/compilation/wrapper.py

@@ -0,0 +1,81 @@
+import os
+import sys
+from abc import abstractmethod
+from contextlib import contextmanager
+from types import CodeType
+from typing import Callable, List
+
+import torch
+
+import vllm.envs as envs
+
+
+class TorchCompileWrapperWithCustomDispacther:
+    """
+    A wrapper class for torch.compile, with a custom dispatch logic.
+    Subclasses should:
+    1. Implement the forward method
+    2. Implement the dispatch logic in the __call__ method
+        It can use `self.compiled_codes` to access the compiled bytecode,
+        and `with self.dispatch_to_code(index):` to dispatch to
+        the compiled code.
+    3. Implement the `__init__` method to determine how to call
+        `torch.compile` over the forward method.
+    """
+
+    def __init__(self, compiled_callable: Callable):
+        self.compiled_callable = compiled_callable
+        self.original_code_object = self.__class__.forward.__code__
+        self.compiled_codes: List[CodeType] = []
+        torch._dynamo.convert_frame.register_bytecode_hook(self.bytecode_hook)
+
+        # read the env var to determine whether to use the custom dispatcher
+        # subclasses can use this to switch between the custom dispatcher
+        # and the default Dynamo guard mechanism.
+        self.use_custom_dispatcher: bool = \
+            envs.VLLM_DYNAMO_USE_CUSTOM_DISPATCHER
+
+    def __call__(self, *args, **kwargs):
+        """Implement the dispatch logic here, beyond the torch.compile level.
+        NOTE: this function can have additional arguments beyond the forward
+         method, for directly dispatching to the compiled code.
+        """
+        return self.compiled_callable(*args, **kwargs)
+
+    @abstractmethod
+    def forward(self, *args, **kwargs):
+        ...
+
+    def bytecode_hook(self, old_code: CodeType, new_code: CodeType):
+        """Hook to save the compiled bytecode for direct execution."""
+        if old_code is not self.original_code_object:
+            return
+        # code borrowed from https://github.com/thuml/depyf/blob/f4ad79fadee27ea113b4c75202db1eb1a11c0dbc/depyf/explain/enable_debugging.py#L25
+        frame = sys._getframe()
+        while True:
+            frame = frame.f_back
+            code_name = frame.f_code.co_name
+            file_name = frame.f_code.co_filename.split(os.path.sep)[-1]
+            if code_name == "_compile" and file_name == "convert_frame.py":
+                break
+        frame = frame.f_locals["frame"]
+        assert frame.f_code == old_code
+
+        if frame.f_locals["self"] is not self:
+            return
+
+        self.compiled_codes.append(new_code)
+
+    @contextmanager
+    def dispatch_to_code(self, index: int):
+        """Context manager to dispatch to the compiled code.
+        Why does this work? Because Dynamo guarantees that the compiled
+        bytecode has exactly the same arguments, cell variables, and free
+        variables as the original code. Therefore we can directly switch
+        the code object in the function and call it.
+
+        See https://dev-discuss.pytorch.org/t/what-is-the-relationship-requirement-among-original-bytecode-transformed-bytecode-and-bytecode-returned-by-hooks-in-dynamo/1693/7 for more details.
+        """ # noqa
+        self.__class__.forward.__code__ = self.compiled_codes[index]
+        yield
+        self.__class__.forward.__code__ = self.original_code_object

vllm/envs.py

@@ -196,6 +196,10 @@ def get_default_config_root():
     # Internal flag to enable Dynamo graph capture
     "VLLM_TEST_DYNAMO_GRAPH_CAPTURE":
     lambda: int(os.environ.get("VLLM_TEST_DYNAMO_GRAPH_CAPTURE", "0")),
+    "VLLM_DYNAMO_USE_CUSTOM_DISPATCHER":
+    lambda:
+    (os.environ.get("VLLM_DYNAMO_USE_CUSTOM_DISPATCHER", "True").lower() in
+     ("true", "1")),
 
     # local rank of the process in the distributed setting, used to determine
     # the GPU device id

vllm/worker/tpu_model_runner.py

@@ -10,6 +10,7 @@
 import torch_xla.runtime as xr
 
 from vllm.attention import AttentionMetadata, get_attn_backend
+from vllm.compilation.wrapper import TorchCompileWrapperWithCustomDispacther
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig)
 from vllm.logger import init_logger
@@ -144,11 +145,7 @@ def load_model(self) -> None:
             )
         model = model.eval()
         xm.wait_device_ops()
-        model = ModelWrapper(model)
-        self.model = torch.compile(model,
-                                   backend="openxla",
-                                   fullgraph=True,
-                                   dynamic=False)
+        self.model = ModelWrapper(model)
 
     def _dummy_run(
         self,
@@ -235,8 +232,15 @@ def _dummy_run(
             torch._dynamo.mark_dynamic(t, 0)
             torch._dynamo.mark_dynamic(p, 0)
         # Dummy run.
-        self.model(token_ids, position_ids, attn_metadata, input_lens, t, p,
-                   num_samples, kv_caches)
+        self.model(token_ids,
+                   position_ids,
+                   attn_metadata,
+                   input_lens,
+                   t,
+                   p,
+                   num_samples,
+                   kv_caches,
+                   is_prompt=is_prompt)
 
     def warmup_model(
         self,
@@ -530,7 +534,7 @@ def _execute_model(*args):
                     if getattr(arg, "context_lens", None) is not None:
                         arg.context_lens = arg.context_lens.to(self.device)
                 new_args.append(arg)
-            return self.model(*new_args)
+            return self.model(*new_args, is_prompt=is_prompt)
 
         num_prefills = model_input.attn_metadata.num_prefills
         is_prompt = num_prefills > 0
@@ -601,11 +605,32 @@ def _execute_model(*args):
         return [SamplerOutput(sampler_outputs)]
 
 
-class ModelWrapper(nn.Module):
+class ModelWrapper(TorchCompileWrapperWithCustomDispacther):
 
     def __init__(self, model: nn.Module):
-        super().__init__()
         self.model = model
+        compiled_callable = torch.compile(self.forward,
+                                          backend="openxla",
+                                          fullgraph=True,
+                                          dynamic=False)
+        super().__init__(compiled_callable)
+
+    def __call__(self, *args, is_prompt: bool, **kwargs):
+        if len(self.compiled_codes) < 3 or not self.use_custom_dispatcher:
+            # not fully compiled yet, or not using the custom dispatcher,
+            # let PyTorch handle it
+            return self.compiled_callable(*args, **kwargs)
+        # the 3 compiled codes are:
+        # 0: for profiling
+        # 1: for prompt
+        # 2: for decode
+        # dispatch to the compiled code directly, skip PyTorch
+        if is_prompt:
+            with self.dispatch_to_code(1):
+                return self.forward(*args, **kwargs)
+        else:
+            with self.dispatch_to_code(2):
+                return self.forward(*args, **kwargs)
 
     def forward(
         self,