Add files using upload-large-folder tool

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/distributed/rpc/message.h

@@ -0,0 +1,193 @@
+#pragma once
+
+#include <torch/types.h>
+#include <vector>
+
+namespace torch {
+namespace distributed {
+namespace rpc {
+
+// An enum denoting common RPC errors to allow specific error handling for them.
+enum RPCErrorType {
+  UNKNOWN_ERROR = 0, /* Indicates that error type could not be parsed */
+  TIMEOUT = 1, /* Indicates that the RPC has timed out */
+  INTENTIONAL_FAILURE = 2 /* Deliberate failure, such as those injected by
+                             FaultyAgent for testing */
+};
+
+// The enum values are bitwise ORed with MessageType
+// They are bit flags starting from 0x100 and should have
+// value such as 0x100, 0x200, 0x400, 0x800, 0xF00, etc.
+enum MessageTypeFlags {
+  REQUEST_TYPE = 0x100,
+  RESPONSE_TYPE = 0x200,
+};
+
+// Message types must have values between 0x00 to 0xff
+enum MessageType {
+  // messages for dist.rpc on builtin operators
+  SCRIPT_CALL = 0x00 | MessageTypeFlags::REQUEST_TYPE,
+  SCRIPT_RET = 0x01 | MessageTypeFlags::RESPONSE_TYPE,
+
+  // messages for dist.rpc on Python UDF
+  PYTHON_CALL = 0x02 | MessageTypeFlags::REQUEST_TYPE,
+  PYTHON_RET = 0x03 | MessageTypeFlags::RESPONSE_TYPE,
+
+  // messages for dist.remote on builtin operators and Python UDF
+  SCRIPT_REMOTE_CALL = 0x04 |
+      MessageTypeFlags::REQUEST_TYPE, // A remote call on a builtin operator
+  PYTHON_REMOTE_CALL =
+      0x05 | MessageTypeFlags::REQUEST_TYPE, // A remote call on a Python UDF
+  REMOTE_RET =
+      0x06 | MessageTypeFlags::RESPONSE_TYPE, // Response for remote calls for
+                                              // UDF, builtin, or script
+
+  // RRef related internal messages
+  SCRIPT_RREF_FETCH_CALL =
+      0x07 | MessageTypeFlags::REQUEST_TYPE, // A UserRRef<IValue> fetches value
+                                             // from owner
+  PYTHON_RREF_FETCH_CALL =
+      0x08 | MessageTypeFlags::REQUEST_TYPE, // A UserRRef<py::object> fetches
+                                             // value from owner
+  SCRIPT_RREF_FETCH_RET = 0x09 |
+      MessageTypeFlags::RESPONSE_TYPE, // An OwnerRRef sends ivalue to user
+  PYTHON_RREF_FETCH_RET = 0x0a |
+      MessageTypeFlags::RESPONSE_TYPE, // An OwnerRRef sends py::object to user
+  RREF_USER_DELETE = 0x0b |
+      MessageTypeFlags::REQUEST_TYPE, // A UserRRef tells the owner to deref
+  RREF_FORK_REQUEST =
+      0x0c | MessageTypeFlags::REQUEST_TYPE, // A child UserRRef tells the owner
+                                             // about itself
+  RREF_CHILD_ACCEPT =
+      0x0d | MessageTypeFlags::REQUEST_TYPE, // A child UserRRef tells parent
+                                             // that owner knows it
+  RREF_ACK =
+      0x0e | MessageTypeFlags::RESPONSE_TYPE, // ACK to internal RRef messages
+
+  // Messages with autograd info
+  FORWARD_AUTOGRAD_REQ = 0x0f | MessageTypeFlags::REQUEST_TYPE,
+  FORWARD_AUTOGRAD_RESP = 0x10 | MessageTypeFlags::RESPONSE_TYPE,
+
+  // Messages to propagate gradients on the backward pass.
+  BACKWARD_AUTOGRAD_REQ = 0x11 | MessageTypeFlags::REQUEST_TYPE,
+  BACKWARD_AUTOGRAD_RESP = 0x12 | MessageTypeFlags::RESPONSE_TYPE,
+
+  // Messages to tell workers to clean up their autograd context.
+  CLEANUP_AUTOGRAD_CONTEXT_REQ = 0x13 | MessageTypeFlags::REQUEST_TYPE,
+  CLEANUP_AUTOGRAD_CONTEXT_RESP = 0x14 | MessageTypeFlags::RESPONSE_TYPE,
+
+  // Messages that tell workers to run requests with profiling enabled.
+  RUN_WITH_PROFILING_REQ = 0x15 | MessageTypeFlags::REQUEST_TYPE,
+  RUN_WITH_PROFILING_RESP = 0x16 | MessageTypeFlags::RESPONSE_TYPE,
+
+  // Messages to support RRef.backward().
+  RREF_BACKWARD_REQ = 0x17 | MessageTypeFlags::REQUEST_TYPE,
+  RREF_BACKWARD_RESP = 0x18 | MessageTypeFlags::RESPONSE_TYPE,
+
+  // Other internal message types
+  EXCEPTION = 0x37 | MessageTypeFlags::RESPONSE_TYPE,
+  UNKNOWN = 0x3c
+};
+
+// A message to be sent/received by an RpcAgent.
+//
+// A Message object contains 4 fields:
+//    payload (std::vector<char>): a binary chunk of data.
+//    tensors (std::vector<torch::Tensor>): all tensors. Tensor data are not
+//        included in the payload, and it is up to the RpcAgent implementation
+//        to determine how to serialize them. This design is helpful for
+//        communicating super large tensors where serializing all the data at
+//        once leads to excessively large memory footprint. An implementation
+//        can then serialize and send tensors chunk-by-chunk, in the streaming
+//        fashion.
+//    type (MessageType): type of the message.
+//    id (int64_t): message id, this is used to match request and response.
+//               Other implementation can ignore it if they have their own
+//               ways to do matching.
+//
+// Layers above ``RpcAgent`` only converts ScriptCall, ScriptResp, PythonCall,
+// and PythonResp into a Message, and it is up to the RpcAgent
+// implementation to determine how to serialize a message.
+class TORCH_API Message final : public torch::CustomClassHolder {
+ private:
+  // Keep these private in order to force users to go through make_intrusive and
+  // thus prevent creating a Message that's not held by an intrusive_ptr.
+  Message();
+
+  Message(
+      std::vector<char>&& payload,
+      std::vector<torch::Tensor>&& tensors,
+      MessageType type);
+
+  Message(
+      std::vector<char>&& payload,
+      std::vector<torch::Tensor>&& tensors,
+      MessageType type,
+      int64_t id);
+
+  friend c10::intrusive_ptr<Message>;
+
+ public:
+  Message(const Message& other) = delete;
+  Message(Message&& other) = delete;
+  Message& operator=(Message const& rhs) = delete;
+  Message& operator=(Message&& rhs) = delete;
+
+  // Destructively retrieves the payload.
+  std::vector<char>&& movePayload() &&;
+  std::vector<torch::Tensor>&& moveTensors() &&;
+
+  std::vector<char>& payload();
+  const std::vector<char>& payload() const;
+  std::vector<torch::Tensor>& tensors();
+  const std::vector<torch::Tensor>& tensors() const;
+  MessageType type() const;
+
+  bool isRequest() const;
+  bool isResponse() const;
+  bool isShutdown() const;
+
+  // id is an optional field to match request/response. If an RpcAgent
+  // implementation is able to do the matching without using this id, it can be
+  // dropped during message serialization.
+  int64_t id() const;
+  void setId(int64_t id);
+
+  std::vector<c10::weak_intrusive_ptr<c10::StorageImpl>> getStorages() const;
+
+ private:
+  std::vector<char> payload_;
+  std::vector<torch::Tensor> tensors_;
+  MessageType type_ = MessageType::UNKNOWN;
+  int64_t id_ = -1;
+};
+
+// Create a response Message of type Exception.
+// The exception string representation will be used as the message's payload.
+// A message ID corresponding to the request that resulted in this response can
+// be provided for matching requests/responses.
+TORCH_API c10::intrusive_ptr<Message> createExceptionResponse(
+    const std::exception& e,
+    int64_t id);
+
+// Create a response Message of type Exception.
+// The passed in string representation will be used as the message's payload.
+// A message ID corresponding to the request that resulted in this response can
+// be provided for matching requests/responses.
+TORCH_API c10::intrusive_ptr<Message> createExceptionResponse(
+    const std::string& exceptionStr,
+    int64_t id);
+
+inline std::tuple<
+    c10::intrusive_ptr<Message>,
+    std::vector<c10::weak_intrusive_ptr<c10::StorageImpl>>>
+withStorages(c10::intrusive_ptr<Message> message) {
+  auto storages = message->getStorages();
+  return std::make_tuple(std::move(message), std::move(storages));
+}
+
+using JitFuture = c10::ivalue::Future;
+
+} // namespace rpc
+} // namespace distributed
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/distributed/rpc/python_functions.h

@@ -0,0 +1,70 @@
+#pragma once
+
+#include <torch/csrc/distributed/rpc/py_rref.h>
+#include <torch/csrc/distributed/rpc/rpc_agent.h>
+#include <torch/csrc/jit/python/pybind_utils.h>
+#include <torch/csrc/utils/pybind.h>
+
+namespace torch {
+namespace distributed {
+namespace rpc {
+
+// Converts an internal ivalue::Future of Message into a user-facing
+// ivalue::Future of py::object type by creating a new ivalue::Future and call
+// its  markCompleted as a callback in the given ivalue::Future.
+// If hasValue is true, the Message will be converted into a py::object and then
+// wrap it with an IValue. If hasValue is false, this ivalue::Future is only
+// used for signaling and launching callbacks. In this case, the message will be
+// discarded and then set the ivalue::Future using an empty IValue or the given
+// FutureError if there is an error.
+c10::intrusive_ptr<JitFuture> toPyJitFuture(
+    const c10::intrusive_ptr<JitFuture>& messageJitFuture,
+    bool hasValue = true);
+
+c10::intrusive_ptr<JitFuture> pyRpcBuiltin(
+    const WorkerInfo& dst,
+    const std::string& opName,
+    const py::args& args,
+    const py::kwargs& kwargs,
+    const float rpcTimeoutSeconds);
+
+c10::intrusive_ptr<JitFuture> pyRpcPythonUdf(
+    const WorkerInfo& dst,
+    std::string& pickledPythonUDF,
+    std::vector<torch::Tensor>& tensors,
+    const float rpcTimeoutSeconds,
+    const bool isAsyncExecution);
+
+c10::intrusive_ptr<JitFuture> pyRpcTorchscript(
+    const std::string& dstWorkerName,
+    const std::string& qualifiedNameStr,
+    const py::tuple& argsTuple,
+    const py::dict& kwargsDict,
+    const float rpcTimeoutSeconds,
+    const bool isAsyncExecution);
+
+PyRRef pyRemoteBuiltin(
+    const WorkerInfo& dst,
+    const std::string& opName,
+    const float rpcTimeoutSeconds,
+    const py::args& args,
+    const py::kwargs& kwargs);
+
+PyRRef pyRemotePythonUdf(
+    const WorkerInfo& dst,
+    std::string& pickledPythonUDF,
+    std::vector<torch::Tensor>& tensors,
+    const float rpcTimeoutSeconds,
+    const bool isAsyncExecution);
+
+PyRRef pyRemoteTorchscript(
+    const std::string& dstWorkerName,
+    const std::string& qualifiedNameStr,
+    const float rpcTimeoutSeconds,
+    const bool isAsyncExecution,
+    const py::args& args,
+    const py::kwargs& kwargs);
+
+} // namespace rpc
+} // namespace distributed
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/distributed/rpc/request_callback_no_python.h

@@ -0,0 +1,119 @@
+#pragma once
+
+#include <torch/csrc/distributed/rpc/message.h>
+#include <torch/csrc/distributed/rpc/request_callback.h>
+#include <torch/csrc/distributed/rpc/rpc_command_base.h>
+#include <torch/csrc/distributed/rpc/rref_impl.h>
+#include <torch/csrc/distributed/rpc/script_call.h>
+#include <torch/csrc/distributed/rpc/script_remote_call.h>
+
+namespace torch {
+namespace distributed {
+namespace rpc {
+
+// RequestCallback implementation with no Python dependencies.
+class TORCH_API RequestCallbackNoPython : public RequestCallback {
+ public:
+  c10::intrusive_ptr<JitFuture> processMessage(
+      Message& request,
+      std::vector<c10::Stream> streams) const override;
+
+ protected:
+  virtual std::unique_ptr<RpcCommandBase> deserializePythonRpcCommand(
+      std::unique_ptr<RpcCommandBase> rpc,
+      const MessageType& messageType) const;
+
+  virtual c10::intrusive_ptr<JitFuture> processScriptCall(
+      RpcCommandBase& rpc,
+      std::vector<c10::Stream> streams) const;
+
+  virtual c10::intrusive_ptr<JitFuture> processPythonCall(
+      RpcCommandBase& rpc,
+      std::vector<c10::Stream> streams) const;
+
+  c10::intrusive_ptr<JitFuture> assignOwnerRRef(
+      const RRefId& rrefId,
+      const RRefId& forkId,
+      c10::intrusive_ptr<JitFuture> valueFuture) const;
+
+  virtual c10::intrusive_ptr<JitFuture> processScriptRemoteCall(
+      RpcCommandBase& rpc,
+      std::vector<c10::Stream> streams) const;
+
+  virtual c10::intrusive_ptr<JitFuture> processPythonRemoteCall(
+      RpcCommandBase& rpc,
+      std::vector<c10::Stream> streams) const;
+
+  c10::intrusive_ptr<JitFuture> retrieveOwnerRRef(const RRefId& rrefId) const;
+
+  c10::intrusive_ptr<JitFuture> processScriptRRefFetchCall(
+      RpcCommandBase& rpc) const;
+
+  virtual c10::intrusive_ptr<JitFuture> processPythonRRefFetchCall(
+      RpcCommandBase& rpc) const;
+
+  c10::intrusive_ptr<JitFuture> processRRefUserDelete(
+      RpcCommandBase& rpc) const;
+
+  c10::intrusive_ptr<JitFuture> processRRefChildAccept(
+      RpcCommandBase& rpc) const;
+
+  c10::intrusive_ptr<JitFuture> processRRefForkRequest(
+      RpcCommandBase& rpc) const;
+
+  c10::intrusive_ptr<JitFuture> processForwardAutogradReq(
+      RpcCommandBase& rpc,
+      std::vector<c10::Stream> streams) const;
+
+  c10::intrusive_ptr<JitFuture> processBackwardAutogradReq(
+      RpcCommandBase& rpc,
+      std::vector<c10::Stream> streams) const;
+
+  c10::intrusive_ptr<JitFuture> processCleanupAutogradContextReq(
+      RpcCommandBase& rpc) const;
+
+  c10::intrusive_ptr<JitFuture> processRunWithProfilingReq(
+      RpcCommandBase& rpc) const;
+
+  virtual void handleRRefDelete(c10::intrusive_ptr<RRef>& rref) const;
+
+  c10::intrusive_ptr<JitFuture> processRpc(
+      RpcCommandBase& rpc,
+      const MessageType& messageType,
+      std::vector<c10::Stream> streams) const;
+
+  virtual c10::intrusive_ptr<JitFuture> processRpcWithErrors(
+      RpcCommandBase& rpc,
+      const MessageType& messageType,
+      std::vector<c10::Stream> streams) const;
+
+  c10::intrusive_ptr<Message> handleError(
+      const std::exception& e,
+      const MessageType messageType,
+      int64_t messageId) const;
+
+  virtual bool cudaAvailable() const;
+
+  virtual c10::intrusive_ptr<JitFuture> processRRefBackward(
+      RpcCommandBase& rpc) const;
+
+  // Helpers to run user-defined functions, operators and other computations.
+
+  c10::intrusive_ptr<JitFuture> runJitOperator(
+      const jit::Operator& op,
+      std::vector<at::IValue>& stack,
+      std::vector<c10::Stream> streams) const;
+
+  // Helpers to convert various kinds of objects into already-completed futures.
+
+  c10::intrusive_ptr<JitFuture> asFuture(IValue value, TypePtr type) const;
+
+  c10::intrusive_ptr<JitFuture> asFuture(
+      c10::intrusive_ptr<Message> message) const;
+
+  c10::intrusive_ptr<JitFuture> asFuture(std::exception_ptr err) const;
+};
+
+} // namespace rpc
+} // namespace distributed
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/distributed/rpc/rpc_agent.h

@@ -0,0 +1,341 @@
+#pragma once
+
+#include <torch/csrc/distributed/rpc/message.h>
+#include <torch/csrc/distributed/rpc/request_callback.h>
+#include <torch/csrc/distributed/rpc/types.h>
+
+#include <algorithm>
+#include <cctype>
+#include <chrono>
+#include <condition_variable>
+#include <mutex>
+#include <thread>
+
+namespace torch {
+namespace distributed {
+namespace rpc {
+
+using DeviceMap = std::unordered_map<c10::Device, c10::Device>;
+
+// Default RPC timeout
+constexpr float kDefaultRpcTimeoutSeconds = 60;
+// Unset RPC timeout. This is the value agent::send() will have if user does not
+// pass in a specific timeout, and indicates that we must use the default
+// timeout for RPCs.
+constexpr float kUnsetRpcTimeout = -1;
+constexpr auto kDefaultInitMethod = "env://";
+constexpr float kSecToMsConversion = 1000;
+constexpr auto kRpcTimeoutErrorStr =
+    "RPC ran for more than set timeout ({} ms) and will now be marked with an error";
+
+using steady_clock_time_point =
+    std::chrono::time_point<std::chrono::steady_clock>;
+// Input is qualified name string, output is JIT StrongTypePtr
+// Same as jit::TypeResolver, did not import jit::TypeResolver to here
+// because it could introduce cyclic dependencies.
+using TypeResolver =
+    std::function<c10::StrongTypePtr(const c10::QualifiedName&)>;
+
+struct TORCH_API RpcBackendOptions {
+  RpcBackendOptions()
+      : RpcBackendOptions(kDefaultRpcTimeoutSeconds, kDefaultInitMethod) {}
+
+  RpcBackendOptions(float rpcTimeoutSeconds, std::string initMethod)
+      : rpcTimeoutSeconds(rpcTimeoutSeconds),
+        initMethod(std::move(initMethod)) {
+    TORCH_CHECK(rpcTimeoutSeconds >= 0, "RPC Timeout must be non-negative");
+  }
+
+  float rpcTimeoutSeconds;
+  std::string initMethod;
+};
+
+// A globally unique ID to identify an RpcAgent
+struct TORCH_API WorkerInfo : torch::CustomClassHolder {
+  WorkerInfo(std::string name, int64_t id);
+
+  WorkerInfo(std::string name, worker_id_t id);
+
+  bool operator==(const WorkerInfo& rhs) {
+    return (id_ == rhs.id_) && (name_ == rhs.name_);
+  }
+
+  static constexpr size_t MAX_NAME_LEN = 128;
+
+  const std::string name_;
+  const worker_id_t id_;
+};
+
+struct TORCH_API RegisterWorkerInfoOnce {
+  RegisterWorkerInfoOnce();
+};
+
+TORCH_API std::ostream& operator<<(
+    std::ostream& os,
+    const WorkerInfo& workerInfo);
+
+// Struct for options to configure the RPC Retry protocol.
+struct TORCH_API RpcRetryOptions {
+  // Using a default constructor like all other Options structs in the RPC
+  // codebase. TORCH_CHECKs for input validation are done in the
+  // sendWithRetries function.
+  RpcRetryOptions() = default;
+  // Maximum number of times we will retry the RPC
+  int maxRetries{5};
+  // Initial duration between consecutive RPC send attempts
+  std::chrono::milliseconds rpcRetryDuration{std::chrono::milliseconds(1000)};
+  // Constant for exponential backoff used while calculating future wait
+  // durations
+  float retryBackoff{1.5};
+};
+
+// Struct that stores all the metadata needed to retry a given RPC.
+struct TORCH_API RpcRetryInfo {
+  RpcRetryInfo(
+      const WorkerInfo& to,
+      c10::intrusive_ptr<Message> message,
+      c10::intrusive_ptr<JitFuture> originalFuture,
+      int retryCount,
+      RpcRetryOptions options)
+      : to_(to),
+        message_(std::move(message)),
+        originalFuture_(std::move(originalFuture)),
+        retryCount_(retryCount),
+        options_(options) {}
+
+  const WorkerInfo& to_;
+  c10::intrusive_ptr<Message> message_;
+  // Future that is returned to the caller of sendWithRetries().
+  c10::intrusive_ptr<JitFuture> originalFuture_;
+  // Number of send attempts completed so far.
+  int retryCount_;
+  RpcRetryOptions options_;
+};
+
+// ``RpcAgent`` is the base class for sending and receiving RPC messages. It
+// provides a unified ``send`` API for both request and response messages, and
+// will invoke the given ``RequestCallback`` to process received requests. It
+// should immediately become ready to serve request and accept response after
+// construction.
+class TORCH_API RpcAgent {
+ public:
+  // `WorkerInfo` is the globally unique identifier for this RpcAgent instance.
+  // It contains a ``name_`` field and an ``id_`` field. ``name_`` is the
+  // globally unique name for this ``RpcAgent``. It is up to the ``RpcAgent``
+  // implementation to determine how to resolve names. ``id_`` is the globally
+  // unique ID for this ``RpcAgent``. This should be determined by the
+  // ``RpcAgent`` implementation.
+  // The ``RequestCallback`` will be invoked to handle received requests. This
+  // ``RpcAgent`` base class makes no assumption on the thread-safeness of the
+  // ``RequestCallback``. ``RpcAgent`` implementations need to make sure that
+  // its threading model conform to ``RequestCallback``'s requirement.
+  // NB: RpcAgent implementations should not start serving requests until
+  // ``start()`` is called, as there could be other contexts that have not been
+  // initialized yet at this time.
+  RpcAgent(
+      WorkerInfo id,
+      std::unique_ptr<RequestCallback> cb,
+      std::chrono::milliseconds rpcTimeout);
+
+  virtual ~RpcAgent();
+
+  // Send a message to the ``RpcAgent`` of id ``to`` and returns a
+  // ``JitFuture`` ptr. The implementation must be asynchronous, i.e., it
+  // cannot block until it receives the response.
+  //
+  // If ``message.isRequest()`` is true, the ``JitFuture`` will be
+  // completed when the response arrives. For other message types, the Future
+  // should be ignored by the caller.
+  virtual c10::intrusive_ptr<JitFuture> send(
+      const WorkerInfo& to,
+      c10::intrusive_ptr<Message> message,
+      const float rpcTimeoutSeconds = kUnsetRpcTimeout,
+      const DeviceMap& deviceMap = {}) = 0;
+
+  // Retries sending the message up to maxRetries times until an ACK is
+  // received. The duration between consecutive sends is increased over
+  // time using an exponential backoff algorithm.
+  //
+  // Sends ``message`` to the ``RpcAgent`` of id ``to`` and returns a
+  // ``JitFuture`` ptr, just like send(). Caller can specify the maximum
+  // number of retries for this RPC (default is 5), initial duration between
+  // sends (default is 1000ms), and backoff constant (default is 1.5) by
+  // passing in the RpcRetryOptions struct. This API might end up
+  // executing a method twice on the remote end (it does not guarantee
+  // exactly-once semantics). Therefore, the user must ensure their requests
+  // are idempotent.
+  c10::intrusive_ptr<JitFuture> sendWithRetries(
+      const WorkerInfo& to,
+      c10::intrusive_ptr<Message> message,
+      RpcRetryOptions retryOptions = RpcRetryOptions());
+
+  // Return a reference to the ``WorkerInfo`` of this RpcAgent.
+  // NB: not using ``c10::optional<const std::string&>`` here because we might
+  // need to create a separate RPC API lib and avoid forcing all ``RpcAgent``
+  // implementations to depend on libtorch.
+  const WorkerInfo& getWorkerInfo() const;
+
+  // Return a reference to the ``WorkerInfo`` of the given ``workerName``.
+  virtual const WorkerInfo& getWorkerInfo(
+      const std::string& workerName) const = 0;
+
+  virtual const WorkerInfo& getWorkerInfo(worker_id_t id) const = 0;
+
+  virtual std::vector<WorkerInfo> getWorkerInfos() const = 0;
+
+  // Retrieve the timeout for all RPCs.
+  inline std::chrono::milliseconds getRpcTimeout() const {
+    return rpcTimeout_.load();
+  }
+
+  // Set the timeout for all RPCs
+  inline void setRpcTimeout(const std::chrono::milliseconds& rpcTimeout) {
+    rpcTimeout_.store(rpcTimeout);
+  }
+
+  // Call sync and join all internal threads. This method should be called
+  // before every RPC process exits.
+  virtual void join(bool shutdown = false, float timeout = 0) = 0;
+
+  // Synchronize the this process with other ``RpcAgent`` processes. Block until
+  // all ``RpcAgent``s reach this method and send all pending messages.
+  virtual void sync() = 0;
+
+  // Sets up backend-agnostic state for accepting requests. Currently, this
+  // entails setting rpcAgentRunning_ to true, creating the retry thread, and
+  // calling the backend's startImpl.
+  void start();
+
+  // Derived classes must override this function to start accepting requests.
+  // This is used to initialize any backend-specific state. Users must call
+  // start, not startImpl, to initialize the RPC Agent.
+  virtual void startImpl() = 0;
+
+  // Stop accepting requests and shutdown the RPC framework as soon as possible
+  // by terminating all RPC threads.
+  void shutdown();
+
+  // Derived classes must override this function to start accepting requests.
+  // THis is used to clean up any backend-specific state. Users must call
+  // shutdown, not shutdownImpl, to shutdown the RPC Agent.
+  virtual void shutdownImpl() = 0;
+
+  // Check if current RPC agent is set.
+  static bool isCurrentRpcAgentSet();
+
+  // Retrieve the valid current RPC agent.
+  static std::shared_ptr<RpcAgent> getCurrentRpcAgent();
+
+  // Set the current RPC agent.
+  static void setCurrentRpcAgent(std::shared_ptr<RpcAgent> rpcAgent);
+
+  // Retrieve metrics as KV map
+  virtual std::unordered_map<std::string, std::string> getMetrics() = 0;
+
+  // Retrieve debug info in addition to metrics as KV map
+  virtual std::unordered_map<std::string, std::string> getDebugInfo();
+
+  // Flag to control whether GIL wait times
+  // should be profiled or not.
+  void enableGILProfiling(bool flag);
+
+  // Retrieve wheher we should profile GIL wait times or not.
+  bool isGILProfilingEnabled();
+
+  // Set type resolver that will be passed to JIT pickler to resolver type Ptr
+  // based on type str.
+  void setTypeResolver(std::shared_ptr<TypeResolver> typeResolver);
+
+  // Get the type resolver
+  std::shared_ptr<TypeResolver> getTypeResolver();
+
+  // Retrieves the device map for the provided destination worker.
+  virtual DeviceMap getDeviceMap(const WorkerInfo& dst) const;
+
+  // Retrieve the (non-CPU) devices that are supported by the agent.
+  virtual const std::vector<c10::Device>& getDevices() const;
+
+ protected:
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  const WorkerInfo workerInfo_;
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  const std::unique_ptr<RequestCallback> cb_;
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  std::atomic<std::chrono::milliseconds> rpcTimeout_;
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  std::atomic<bool> profilingEnabled_;
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  std::shared_ptr<TypeResolver> typeResolver_;
+  // Atomic boolean indicating whether this agent is running. It controls
+  // whether several background threads should be running. It is set in
+  // RpcAgent::start() and unset in the derived class shutdown().
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  std::atomic<bool> rpcAgentRunning_;
+
+ private:
+  static std::shared_ptr<RpcAgent> currentRpcAgent_;
+  // Add GIL wait time data point to metrics
+  virtual void addGilWaitTime(const std::chrono::microseconds gilWaitTime) = 0;
+  friend class PythonRpcHandler;
+
+  // Map that stores metadata for RPC's that may need to be re-tried as well as
+  // the timepoint at which we should re-try them.
+  std::map<
+      steady_clock_time_point,
+      std::unordered_set<std::shared_ptr<RpcRetryInfo>>>
+      rpcRetryMap_;
+
+  // Thread that checks for retryable RPC's in the rpcRetryMap_ and sleeps until
+  // the next unACKed RPC's timeout has expired.
+  std::thread rpcRetryThread_;
+
+  // Function that rpcRetryThread_ calls in a loop as long as RpcAgent is
+  // running.
+  void retryExpiredRpcs();
+
+  // This is the callback attached to futures corresponding to send retries.
+  // This handles 3 cases: 1). send was completed, 2). send failed with an
+  // error and we've done maxRetries failed send attempts, and 3). send
+  // failed with an error and we have more retries to go. In case 1, we mark
+  // the original future as complete. In case 2, we mark the future with an
+  // error and do not retry again. In case 3, we move the RpcRetryInfo struct
+  // to another time point in the map to schedule the RPC for a future send.
+  void rpcRetryCallback(
+      JitFuture& message,
+      steady_clock_time_point newTime,
+      std::shared_ptr<RpcRetryInfo> earliestRpc);
+
+  // Function that uses the exponential backoff algorithm to compute the next
+  // time point to retry a given RPC.
+  inline steady_clock_time_point computeNewRpcRetryTime(
+      RpcRetryOptions& options,
+      int retryCount) {
+    // The exponential backoff algorithm being used here is:
+    // newTime = timeNow + (retryDuration * (backoffConstant ^ retryCount)).
+    std::chrono::milliseconds timedelta =
+        std::chrono::duration_cast<std::chrono::milliseconds>(
+            options.rpcRetryDuration * pow(options.retryBackoff, retryCount));
+    return std::chrono::time_point_cast<std::chrono::milliseconds>(
+        std::chrono::steady_clock::now() + timedelta);
+  }
+
+  // Condition Variable to signal when the rpcRetryMap_ has been populated.
+  std::condition_variable rpcRetryMapCV_;
+
+  // Mutex to protect RpcRetryMap_.
+  std::mutex rpcRetryMutex_;
+};
+
+} // namespace rpc
+} // namespace distributed
+} // namespace torch
+
+namespace std {
+template <>
+struct hash<torch::distributed::rpc::WorkerInfo> {
+  std::size_t operator()(
+      const torch::distributed::rpc::WorkerInfo& worker_info) const noexcept {
+    return worker_info.id_;
+  }
+};
+} // namespace std

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/distributed/rpc/rref_impl.h

@@ -0,0 +1,420 @@
+#pragma once
+
+#include <ATen/core/jit_type.h>
+#include <ATen/core/rref_interface.h>
+#include <c10/core/Event.h>
+#include <c10/util/Optional.h>
+#include <torch/csrc/distributed/rpc/message.h>
+#include <torch/csrc/distributed/rpc/rpc_agent.h>
+#include <torch/csrc/distributed/rpc/types.h>
+
+#include <atomic>
+
+namespace torch {
+namespace distributed {
+namespace rpc {
+
+class RRef;
+class RRefContext;
+class UserRRef;
+
+constexpr int OWNER_IDX = 0; // index of ownerId in the tuple
+constexpr int RREFID_ON_IDX = 1; // index of RRefId.createdOn_ in the tuple
+constexpr int RREFID_ID_IDX = 2; // index of RRefId.localId_ in the tuple
+constexpr int FORKID_ON_IDX = 3; // index of ForkId.createdOn_ in the tuple
+constexpr int FORKID_ID_IDX = 4; // index of ForkId.localId_ in the tuple
+constexpr int PARENT_IDX = 5; // index of parent in the tuple
+constexpr int TYPE_IDX = 6; // index of parent in the tuple
+
+// NB: if more fields are added, make sure this field is also bumped
+constexpr int RFD_TUPLE_SIZE = 7; // number of RRefForkData fields in py::tuple
+
+// Represents fork of an RRef to be sent over the wire.
+struct TORCH_API RRefForkData {
+  const worker_id_t ownerId_;
+  const RRefId rrefId_;
+  const ForkId forkId_;
+  const worker_id_t parent_;
+  const std::string typeStr_;
+
+  RRefForkData(
+      worker_id_t ownerId,
+      const RRefId& rrefId,
+      const ForkId& forkId,
+      worker_id_t parent,
+      std::string typeStr);
+};
+
+// Note [RRef Protocol]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~
+//
+// [Background]
+//
+// RRef stands for Remote REFerence. Each RRef is owned by a single worker
+// (i.e., owner) and can be used by multiple users. The owner stores the real
+// data referenced by its RRefs. RRef needs to support fast and scalable RPC.
+// Hence, in the design, we avoid using a single global master to keep RRef
+// states, instead owners will keep track of the global reference counts
+// for its RRefs. Every RRef can be uniquely identified by a global RRefId,
+// which is assigned at the time it is first created either on a user or on the
+// owner.
+//
+// On the owner worker, there is only one OwnerRRef instance, which contains the
+// real data, while on user workers, there can be as many UserRRefs as
+// necessary, and UserRRef does not hold the data. All usage on the OwnerRRef
+// should retrieve the unique OwnerRRef instance using the globally unique
+// RRefId. //A UserRRef will be created when it is used as an argument or return
+// value in dist.rpc or dist.remote call, but RRef forking and reference
+// counting (RC) are completely transparent to applications. Every UserRRef will
+// also have its globally unique ForkId.
+//
+// [Assumptions]
+//
+// 1. Transient Network Failures
+//
+// TODO: current RRef implementation does not tolerate failures
+//
+// The RRef design handles transient network failures by retrying
+// messages. Node crashes or permanent network partition is beyond the scope.
+// When those incidents occur, the application may take down all workers, revert
+// to the previous checkpoint, and resume training.
+//
+// 2. Non-idempotent UDFs
+//
+// We assume UDFs are not idempotent and therefore cannot be retried. However,
+// internal RRef control messages are idempotent and retried upon message
+// failure.
+//
+// TODO: RRef internal messages are not yet idempotent
+//
+// 3. Out of Order Message Delivery
+//
+// We do not assume message delivery order between any pair of nodes, because
+// both sender and receiver are using multiple threads. There is no guarantee on
+// which message will be processed first.
+//
+// [RRef Lifetime]
+//
+// The goal of the protocol is to delete an OwnerRRef at an appropriate time.
+// The right time to delete an OwnerRRef is when there are no living UserRRefs
+// and Python GC also agrees to delete the OwnerRRef instance on the owner. The
+// tricky part is to determine if there are any living UserRRefs.
+//
+// A user can get a UserRRef in three situations:
+//
+// (1). Receiving a UserRRef from the owner.
+// (2). Receiving a UserRRef from another user.
+// (3). Creating a new UserRRef owned by another worker.
+//
+// (1) is the simplest case where the owner initiates the fork, and hence it can
+// easily increment local RC. The only requirement is that any UserRRef must
+// notify the owner before destruction. Hence, we need the first guarantee:
+//
+// G1. The owner will be notified when any UserRRef is deleted.
+//
+// As messages might come delayed or out-of-order, we need more one guarantee to
+// make sure the delete message is not sent out too soon. Let us first introduce
+// a new concept. If A sends an RPC to B that involves an RRef, we call the RRef
+// on A the parent RRef and the RRef on B the child RRef.
+//
+// G2. Parent RRef cannot be deleted until the child RRef is confirmed by the
+//     owner.
+//
+// Under (1), where the caller is UserRRef and callee is OwnerRRef, it simply
+// means that the user will not send out the delete message until all previous
+// messages are ACKed. Note that ACKed does not mean the owner finishes
+// executing the function, instead, it only means the owner has retrieved its
+// local OwnerRRef and about to pass it to the function, which is sufficient to
+// keep the OwnerRRef alive even if the delete message from the user arrives at
+// the owner before the function finishes execution.
+//
+// With (2) and (3), it is possible that the owner only partially knows the RRef
+// fork graph or not even knowing it at all. For example, the RRef could be
+// constructed on a user, and before the owner receives the RPC call, the
+// creator user might have already shared the RRef with other users, and those
+// users could further share the RRef. One invariant is that the fork graph of
+// any RRef is always a tree rooted at the owner, because forking an RRef always
+// creates a new RRef instance, and hence every RRef has a single parent. One
+// nasty detail is that when an RRef is created on a user, technically the owner
+// is not its parent but we still consider it that way and it does not break the
+// argument below.
+//
+// The owner's view on any node (fork) in the tree has three stages:
+//
+//       1) unknown -> 2) known -> 3) deleted.
+//
+// The owner's view on the entire tree keeps changing. The owner deletes its
+// OwnerRRef instance when it thinks there are no living UserRRefs, i.e., when
+// OwnerRRef is deleted, all UserRRefs could be either indeed deleted or
+// unknown. The dangerous case is when some forks are unknown and others are
+// deleted.
+//
+// G2 trivially guarantees that no parent UserRRef Y can be deleted before the
+// owner knows all of Y's children UserRRefs.
+//
+// However, it is possible that the child UserRRef Z may be deleted before the
+// owner knows its parent Y. More specifically, this can happen when all of Z's
+// messages are processed by the owner before all messages from Y, including the
+// delete message. Nevertheless, this does not cause any problem. Because, at
+// least one of Y's ancestor will be alive, and it will prevent the owner from
+// deleting the OwnerRRef. Consider the following example: (NB: this scenario
+// will no longer relevant when we block UDF until all RRefs are confirmed by
+// the owner)
+//
+//     OwnerRRef -> A -> Y -> Z
+//
+// OwnerRRef forks to A, then A forks to Y, and Y forks to Z. Z can be deleted
+// without OwnerRRef knowing Y. However, the OwnerRRef will at least know A, as
+// the owner directly forks the RRef to A. A won't die before the owner knows Y.
+//
+// Things get a little trickier if the RRef is created on a user:
+//
+//  OwnerRRef
+//      ^
+//      |
+//      A -> Y -> Z
+//
+// If Z calls to_here on the UserRRef, the owner at least knows A when Z is
+// deleted, because otherwise to_here wouldn't finish. If Z does not call
+// to_here, it is possible that the owner receives all messages from Z before
+// any message from A and Y. In this case, as the real data of the OwnerRRef has
+// not been created yet, there is nothing to be deleted either. It is the same
+// as Z does not exist at all Hence, it's still OK.
+//
+// See #26759 for more details and discussions.
+//
+// TODO: make RRef an IValue, and edit createStackForSchema accordingly
+// TODO: make RRef system messages idempotent and retry on failures.
+//
+// ``RRef`` is the base type for both ``UserRRef`` and ``OwnerRRef``.
+// Each ``RRef`` has a globally unique ``RRefId``.
+class TORCH_API RRef : public RRefInterface {
+ public:
+  // RRef is made NOT copyable NOT movable to prevent messing up reference
+  // counting.
+  explicit RRef(const RRef& other) = delete;
+  explicit RRef(RRef&& other) = delete;
+  RRef& operator=(RRef&& other) = delete;
+
+  ~RRef() override = default;
+
+  // returns the worker id of the owner
+  inline worker_id_t owner() const override {
+    return ownerId_;
+  }
+
+  // returns the worker name of the owner
+  inline std::string ownerName() const override {
+    return RpcAgent::getCurrentRpcAgent()->getWorkerInfo(ownerId_).name_;
+  }
+
+  // returns the worker info of the owner
+  inline WorkerInfo ownerWorkerInfo() const {
+    return RpcAgent::getCurrentRpcAgent()->getWorkerInfo(ownerId_);
+  }
+
+  // Returns the globally unique RRefId of this RRef
+  inline const RRefId& rrefId() const {
+    return rrefId_;
+  }
+
+  inline bool isPyObj() const {
+    return type_ == PyObjectType::get();
+  }
+  inline const TypePtr type() const override {
+    return type_;
+  }
+
+  // Save the future corresponding to the creation of this RRef on a remote
+  // node. Note that this is only set when processing requests invoked with
+  // rpc.remote. This is only used to get the future corresponding to the rref
+  // for profiling use cases.
+  inline void registerOwnerCreationFuture(c10::intrusive_ptr<JitFuture> fut) {
+    ownerCreationFuture_ = std::move(fut);
+  }
+
+  // Get the future corresponding to the creation of this rref.
+  inline c10::intrusive_ptr<JitFuture> getOwnerCreationFuture() const {
+    return ownerCreationFuture_;
+  }
+
+  // Check if creation of this RRef on owner node has timed out.
+  inline bool getTimedOut() const {
+    return timedOut_.load();
+  }
+
+  // Dispatches an error to the correct handler based on its RPCErrorType.
+  void handleError(RPCErrorType errorType, const JitFuture& JitFuture);
+
+  // Send delete UserRRef request to Owner,
+  // if the request hasn't been sent yet.
+  // There are 2 cases to call it,
+  // 1, Python GC decides end of UserRRef lifetime, calling destructor.
+  // 2, RPC module graceful shutdown calls it on all UserRRefs tracked
+  //    in the RRefContext.
+  virtual void tryDel() {}
+
+ protected:
+  // Indicates that the creation of this RRef on owner node has timed out.
+  inline void setTimedOut() {
+    timedOut_ = true;
+  }
+  friend class RRefContext;
+
+  RRef(worker_id_t ownerId, const RRefId& rrefId, TypePtr type);
+
+  virtual RRefForkData fork() const;
+
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  const worker_id_t ownerId_;
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  const RRefId rrefId_;
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  std::atomic<bool> timedOut_{false};
+
+  // type field to denote the type of the element that the RRef is holding
+  // it could be any TypePtr that JIT support, including PyObjectType
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  const TypePtr type_;
+  // Future corresponding to request to create RRef on remote node.
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  c10::intrusive_ptr<JitFuture> ownerCreationFuture_;
+};
+
+// ``UserRRef`` represents a user of an RRef. Besides the ``RRefId``, each user
+// also has a globally unique ``ForkId`` to identify this user. ``UserRRef``
+// never owns the real value, the only way to get the value of the ``RRef`` is
+// to call ``to_here()`` and get a copy..
+class TORCH_API UserRRef final : public RRef {
+ public:
+  UserRRef(const UserRRef& other) = delete;
+  UserRRef(UserRRef&& other) = delete;
+  UserRRef& operator=(const UserRRef& other) = delete;
+  UserRRef& operator=(UserRRef&& other) = delete;
+
+  UserRRef(
+      worker_id_t ownerId,
+      const RRefId& rrefId,
+      const ForkId& forkId,
+      TypePtr type);
+
+  inline bool isOwner() const override {
+    return false;
+  }
+
+  inline bool confirmedByOwner() const override {
+    return confirmedByOwner_;
+  }
+
+  // Returns the globally unique ForkId of this RRef
+  const ForkId& forkId() const;
+
+  // Get of copy of the value from the ``OwnerRRef``. If the value is not ready
+  // yet, this call will block.
+  IValue toHere(
+      const float timeoutSeconds =
+          torch::distributed::rpc::kUnsetRpcTimeout) const;
+
+  void tryDel() override;
+
+  // Will be called when refcount reaches 0.
+  // Upon destruction, this ``UserRRef`` will tell the owner to deref.
+  void release_resources() override;
+
+  // Will be called when both refcount and weakcount reach 0. See
+  // https://github.com/pytorch/pytorch/blob/9116f02bebf3a5260feef5732d36c54ecb3b4033/c10/util/intrusive_ptr.h#L204
+  // This is called on destructing the wrapping intrusive_ptr_target instance
+  // and it's data members.
+  ~UserRRef() override;
+
+ private:
+  friend class RRefContext;
+
+  RRefForkData fork() const override;
+  inline void confirm() {
+    confirmedByOwner_ = true;
+  }
+
+  const ForkId forkId_;
+
+  // Indicates if this user has sent delete message to it's owner.
+  // Note, thread safety is needed because delete message could be sent by
+  // either the destructor called by Python garbage collection or RRefContext
+  // proactive cleanup on RPC graceful shutdown.
+  std::mutex deletedOnOwnerMutex_;
+  bool deletedOnOwner_{false};
+  // Indicating whether this UserRRef has been confirmed by its owner.
+  std::atomic<bool> confirmedByOwner_;
+};
+
+// Keep the template only on the derived class because ``RRefContext`` needs to
+// erase the type on ``RRef`` and keep them in one map.
+class TORCH_API OwnerRRef final : public RRef {
+ public:
+  OwnerRRef(const OwnerRRef& other) = delete;
+  OwnerRRef(OwnerRRef&& other) = delete;
+  OwnerRRef& operator=(const OwnerRRef& other) = delete;
+  OwnerRRef& operator=(OwnerRRef&& other) = delete;
+
+  OwnerRRef(
+      worker_id_t ownerId,
+      const RRefId& rrefId,
+      TypePtr type,
+      std::vector<c10::Device> devices);
+
+  OwnerRRef(
+      worker_id_t ownerId,
+      const RRefId& rrefId,
+      TypePtr type,
+      c10::optional<IValue> value,
+      std::vector<c10::Device> devices);
+
+  inline bool isOwner() const override {
+    return true;
+  }
+
+  // OwnerRRef is always confirmed, while UserRRef is only confirmed when the
+  // owner knows about it.
+  inline bool confirmedByOwner() const override {
+    return true;
+  }
+
+  // Get a constant reference of the real value. This method will block if the
+  // value is not ready. This method does not need GIL as it does not create
+  // any new py::object. It will throw if there is an error.
+  const IValue& getValue() const;
+
+  // Set the value of this ``OwnerRRef``. This method does not need GIL as it
+  // does not create any new py::object.
+  void setValue(IValue&& value);
+  // Sets the value of this ``OwnerRRef`` to contain an exception.
+  void setError(std::exception_ptr eptr);
+
+  // Has a value or error been set?
+  bool hasValue() const;
+  // Gets a future that is satisfied when the value or error is set.
+  c10::intrusive_ptr<JitFuture> getFuture();
+
+ private:
+  friend class RRefContext;
+
+  c10::intrusive_ptr<JitFuture> future_;
+};
+
+TORCH_API std::ostream& operator<<(std::ostream& os, const RRef& rref);
+
+// Helper function that casts from c10::RRefInterface to OwnerRRef
+inline TORCH_API c10::intrusive_ptr<OwnerRRef> fromRRefInterface(
+    const c10::intrusive_ptr<c10::RRefInterface>& rrefInterface) {
+  return c10::static_intrusive_pointer_cast<OwnerRRef>(rrefInterface);
+}
+
+// Helper function that casts from OwnerRRef to c10::RRefInterface
+inline TORCH_API c10::intrusive_ptr<c10::RRefInterface> fromOwnerRRef(
+    const c10::intrusive_ptr<RRef>& ownerRRef) {
+  return c10::static_intrusive_pointer_cast<c10::RRefInterface>(ownerRRef);
+}
+
+} // namespace rpc
+} // namespace distributed
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/distributed/rpc/script_resp.h

@@ -0,0 +1,26 @@
+#pragma once
+
+#include <torch/csrc/distributed/rpc/message.h>
+#include <torch/csrc/distributed/rpc/rpc_command_base.h>
+#include <torch/csrc/jit/serialization/pickler.h>
+
+namespace torch {
+namespace distributed {
+namespace rpc {
+
+// Return value of a builtin operator or a TorchScript function.
+class TORCH_API ScriptResp final : public RpcCommandBase {
+ public:
+  explicit ScriptResp(at::IValue&& values);
+
+  const at::IValue& value();
+  c10::intrusive_ptr<Message> toMessageImpl() && override;
+  static std::unique_ptr<ScriptResp> fromMessage(const Message& message);
+
+ private:
+  const at::IValue value_;
+};
+
+} // namespace rpc
+} // namespace distributed
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/distributed/rpc/tensorpipe_agent.h

@@ -0,0 +1,495 @@
+#pragma once
+
+#ifdef USE_TENSORPIPE
+
+#include <atomic>
+#include <thread>
+
+#include <c10/core/thread_pool.h>
+#include <torch/csrc/distributed/c10d/PrefixStore.hpp>
+#include <torch/csrc/distributed/c10d/Store.hpp>
+#include <torch/csrc/distributed/rpc/rpc_agent.h>
+
+// Forward-declare the TensorPipe classes we need, to avoid including its
+// headers in PyTorch's ones and thus have it become a public dependency.
+
+namespace tensorpipe {
+
+class Context;
+class Error;
+class Listener;
+class Message;
+class Pipe;
+
+namespace transport {
+class Context;
+} // namespace transport
+
+namespace channel {
+class Context;
+} // namespace channel
+
+} // namespace tensorpipe
+
+namespace torch {
+namespace distributed {
+namespace rpc {
+
+// These priorities instruct TensorPipe on which transport/channel to pick
+// during handshake. Higher priorities will take precedence over lower ones.
+// The transport with lowest priority will be the one used to bootstrap pipes.
+
+constexpr int64_t kShmTransportPriority = 200;
+constexpr int64_t kIbvTransportPriority = 100;
+// The UV transport just uses TCP and should work everywhere, thus keep it last.
+constexpr int64_t kUvTransportPriority = 0;
+
+constexpr int64_t kCmaChannelPriority = 1200;
+constexpr int64_t kMultiplexedUvChannelPriority = 1100;
+// The basic channel reuses a transport as a channel, and is thus our fallback.
+constexpr int64_t kBasicChannelPriority = 1000;
+
+// CPU channel have higher priority than CUDA channels, since the latter might
+// handle CPU-to-CPU transfers, but will always be less efficient than their
+// CPU-only counterparts.
+constexpr int64_t kCudaIpcChannelPriority = 300;
+constexpr int64_t kCudaGdrChannelPriority = 200;
+constexpr int64_t kCudaXthChannelPriority = 400;
+constexpr int64_t kCudaBasicChannelPriority = 0;
+
+using steady_clock_time_point =
+    std::chrono::time_point<std::chrono::steady_clock>;
+
+struct TORCH_API TransportRegistration {
+  std::shared_ptr<tensorpipe::transport::Context> transport;
+  int64_t priority;
+  std::string address;
+};
+
+C10_DECLARE_REGISTRY(TensorPipeTransportRegistry, TransportRegistration);
+
+struct TORCH_API ChannelRegistration {
+  std::shared_ptr<tensorpipe::channel::Context> channel;
+  int64_t priority;
+};
+
+C10_DECLARE_REGISTRY(TensorPipeChannelRegistry, ChannelRegistration);
+
+constexpr auto kDefaultNumWorkerThreads = 16;
+
+struct TORCH_API TensorPipeRpcBackendOptions : public RpcBackendOptions {
+  TensorPipeRpcBackendOptions(
+      int numWorkerThreads,
+      optional<std::vector<std::string>> transports,
+      optional<std::vector<std::string>> channels,
+      float rpc_timeout,
+      std::string init_method,
+      std::unordered_map<std::string, DeviceMap> device_maps = {},
+      std::vector<c10::Device> devices = {})
+      : RpcBackendOptions(rpc_timeout, init_method),
+        numWorkerThreads(numWorkerThreads),
+        transports(std::move(transports)),
+        channels(std::move(channels)),
+        deviceMaps(std::move(device_maps)),
+        devices(std::move(devices)) {
+    TORCH_CHECK(
+        numWorkerThreads > 0,
+        "num_worker_threads must be positive, got ",
+        numWorkerThreads);
+
+    if (this->transports.has_value()) {
+      for (const std::string& transportName : this->transports.value()) {
+        TORCH_CHECK(
+            TensorPipeTransportRegistry()->Has(transportName),
+            "Unknown transport: ",
+            transportName);
+      }
+    }
+
+    if (this->channels.has_value()) {
+      for (const std::string& channelName : this->channels.value()) {
+        TORCH_CHECK(
+            TensorPipeChannelRegistry()->Has(channelName),
+            "Unknown channel: ",
+            channelName);
+      }
+    }
+  }
+
+  void setDeviceMap(const std::string& workerName, const DeviceMap& deviceMap) {
+    auto iter = deviceMaps.find(workerName);
+    if (iter == deviceMaps.end()) {
+      deviceMaps[workerName] = deviceMap;
+    } else {
+      for (auto& entry : deviceMap) {
+        // c10::Device has no default constructor, hence map[device] dosn't work
+        // In C++-17 we can use insert_or_assign.
+        auto entryIter = iter->second.find(entry.first);
+        if (entryIter == iter->second.end()) {
+          iter->second.emplace(entry.first, entry.second);
+        } else {
+          entryIter->second = entry.second;
+        }
+      }
+    }
+  }
+
+  int numWorkerThreads;
+  const optional<std::vector<std::string>> transports;
+  const optional<std::vector<std::string>> channels;
+  std::unordered_map<std::string, DeviceMap> deviceMaps;
+  std::vector<c10::Device> devices;
+};
+
+// Struct to track the network source metrics
+struct TORCH_API NetworkSourceInfo {
+  worker_id_t srcRank;
+  std::vector<uint8_t> srcMachineAddr;
+};
+
+// Struct to track aggregated network metrics
+struct TORCH_API AggregatedNetworkData {
+  uint64_t numCalls{0};
+  uint64_t totalSentBytes{0};
+  uint64_t totalRecvBytes{0};
+  uint64_t totalErrors{0};
+};
+
+// TensorPipeAgent leverages TensorPipe (https://github.com/pytorch/tensorpipe)
+// to transparently move tensors and payloads through the fastest available
+// transport or channel. It acts like a hybrid RPC transport, providing shared
+// memory (linux) and TCP (linux & mac) support. CUDA support is in progress.
+class TORCH_API TensorPipeAgent : public RpcAgent {
+ public:
+  TensorPipeAgent(
+      const c10::intrusive_ptr<::c10d::Store>& store,
+      std::string selfName,
+      worker_id_t selfId,
+      optional<int> worldSize,
+      TensorPipeRpcBackendOptions opts,
+      std::unordered_map<std::string, DeviceMap> reverseDeviceMaps,
+      std::vector<c10::Device> devices,
+      std::unique_ptr<RequestCallback> cb);
+
+  TensorPipeAgent(const TensorPipeAgent&) = delete;
+  TensorPipeAgent& operator=(const TensorPipeAgent&) = delete;
+
+  c10::intrusive_ptr<JitFuture> send(
+      const WorkerInfo& to,
+      c10::intrusive_ptr<Message> message,
+      const float rpcTimeoutSeconds = kUnsetRpcTimeout,
+      const DeviceMap& deviceMap = {}) override;
+
+  // join() and sync() would be deprecated -
+  // https://github.com/pytorch/pytorch/issues/27647
+  void join(bool shutdown = false, float timeout = 0) override;
+  void sync() override{};
+  void startImpl() override;
+  void shutdownImpl() override;
+
+  ~TensorPipeAgent() override;
+
+  const WorkerInfo& getWorkerInfo(const std::string& workerName) const override;
+  const WorkerInfo& getWorkerInfo(worker_id_t workerId) const override;
+  std::vector<WorkerInfo> getWorkerInfos() const override;
+  void updateGroupMembership(
+      const WorkerInfo& workerInfo,
+      const std::vector<c10::Device>& devices,
+      const std::unordered_map<std::string, DeviceMap>& reverseDeviceMaps,
+      bool isJoin);
+
+  std::unordered_map<std::string, std::string> getMetrics() override;
+
+  void addGilWaitTime(const std::chrono::microseconds gilWaitTime) override;
+
+  TensorPipeRpcBackendOptions getBackendOptions() const;
+
+  const c10::intrusive_ptr<::c10d::Store> getStore() const;
+
+  DeviceMap getDeviceMap(const WorkerInfo& dest) const override;
+
+  const std::vector<c10::Device>& getDevices() const override;
+
+  using NetworkDataDict =
+      std::unordered_map<std::string, AggregatedNetworkData>;
+
+  // Returns metrics tracked by the NetworkDataDict
+  NetworkDataDict getNetworkData();
+  // Returns NetworkSourceInfo struct
+  NetworkSourceInfo getNetworkSourceInfo();
+
+  static const std::string& guessAddress();
+
+  // For testing purposes.
+  size_t timeoutMapSize();
+  size_t numPendingResponses();
+  size_t messageIdToTimeoutMapSize();
+
+  const bool isStaticGroup_;
+
+ protected:
+  // TensorPipe write function that could be used to write response
+  // messages by server, and write request messages by client. This
+  // is a protected method since it is overwritten by FaultyTensorPipeAgent
+  virtual void pipeWrite(
+      const std::shared_ptr<tensorpipe::Pipe>&,
+      c10::intrusive_ptr<Message> message,
+      std::vector<c10::Device>&& devices,
+      std::vector<c10::Stream> streams,
+      std::function<void(const tensorpipe::Error&)>) noexcept;
+
+ private:
+  // Removes the given messageId with the given expirationTime from the
+  // timeoutMap_.
+  void removeFromTimeoutMap(uint64_t messageId);
+
+  // Populates workerIdToInfo_ and workerNameToInfo_ using addressStore_
+  void prepareNames(bool isStaticGroup);
+
+  // Check the static group attribute with the value set in store
+  void checkAndSetStaticGroup(const c10::intrusive_ptr<::c10d::Store>& store);
+
+  const std::string& findWorkerURL(const WorkerInfo& worker) const;
+
+  // Only use for Dynamic RPC groups, method to have worker leave group
+  void leaveGroup();
+
+  // TensorPipe read function that could be used to read response messages
+  // by client, and read request messages by server.
+  void pipeRead(
+      const std::shared_ptr<tensorpipe::Pipe>&,
+      std::function<void(
+          const tensorpipe::Error&,
+          c10::intrusive_ptr<Message>,
+          std::vector<c10::Stream>)>) noexcept;
+
+  // Callback of listener accept()
+  void onListenerAccepted(
+      const tensorpipe::Error& error,
+      std::shared_ptr<tensorpipe::Pipe>& pipe);
+
+  // Respond to a call from a peer
+  void respond(std::shared_ptr<tensorpipe::Pipe>& pipe);
+
+  void sendCompletedResponseMessage(
+      std::shared_ptr<tensorpipe::Pipe>& pipe,
+      JitFuture& futureResponseMessage,
+      uint64_t messageId,
+      std::vector<c10::Stream> stream);
+
+  // Collects metrics from successful RPC calls
+  void trackNetworkData(
+      uint64_t requestSize,
+      uint64_t responseSize,
+      const std::string& destWorkerName);
+
+  // Collects metrics from failed RPC calls
+  void trackNetworkError(
+      uint64_t requestSize,
+      const std::string& destWorkerName);
+
+  inline std::vector<c10::Device> getDevicesForRemote(
+      const std::string& remoteName,
+      const Message& message) const;
+
+  // When a request+response completes, we need to mark the future message as
+  // complete. However, if its timeout has already expired, it already has an
+  // error set. There is no atomic "test-and-set" way to mark a future complete
+  // only if it isn't yet. It does exist for errors (setErrorIfNeeded) but, even
+  // then, it ends up printing a log message, which may worry the user. To solve
+  // both issues we use a separate atomic flag to know the status of the future.
+  struct AtomicJitFuture {
+    explicit AtomicJitFuture(const std::vector<c10::Device>& devices) {
+      jitFuture = c10::make_intrusive<at::ivalue::Future>(
+          at::AnyClassType::get(), devices);
+    }
+
+    std::atomic_flag isComplete = ATOMIC_FLAG_INIT;
+    c10::intrusive_ptr<JitFuture> jitFuture;
+  };
+
+  // Maintains state per client pipe to track pending response messages and
+  // error states. pendingResponseMessage_ should be protected by a mutex since
+  // it can be raced with user send() call.
+  // TODO: To achieve better performance we can have a pipe pool per
+  // client that can be configured using RpcBackendOptions.
+  struct ClientPipe {
+    explicit ClientPipe(std::shared_ptr<tensorpipe::Pipe> pipe)
+        : pipe_(std::move(pipe)) {}
+    std::shared_ptr<tensorpipe::Pipe> pipe_;
+    mutable std::mutex mutex_;
+    bool inError_{false};
+    // Map from Message Request ID's to corresponding futures.
+    std::unordered_map<uint64_t, std::shared_ptr<AtomicJitFuture>>
+        pendingResponseMessage_;
+  };
+
+  const c10::intrusive_ptr<::c10d::Store> store_;
+
+  const TensorPipeRpcBackendOptions opts_;
+  // For dynamic RPC, the reverse device maps are updated whenever a new rank
+  // joins or leaves the group
+  std::unordered_map<std::string, DeviceMap> reverseDeviceMaps_;
+  // Local devices used by this agent. If application didn't specify this
+  // field, it will be initialized using corresponding local devices in
+  // opts_.deviceMaps and reverseDeviceMaps_;
+  std::vector<c10::Device> devices_;
+
+  ThreadPool threadPool_;
+  std::shared_ptr<tensorpipe::Context> context_;
+  std::shared_ptr<tensorpipe::Listener> listener_;
+
+  mutable std::mutex connectedPipesMutex_;
+  std::unordered_map<worker_id_t, ClientPipe> connectedPipes_;
+
+  // Maps keyed on name and id for easy WorkerInfo lookup.
+  std::unordered_map<worker_id_t, WorkerInfo> workerIdToInfo_;
+  std::unordered_map<std::string, WorkerInfo> workerNameToInfo_;
+  std::unordered_map<std::string, std::string> workerNameToURL_;
+
+  ::c10d::PrefixStore rankToNameStore_;
+  ::c10d::PrefixStore nameToAddressStore_;
+  // Store keys that will used to count joined processes and active calls during
+  // the shutdown process
+  ::c10d::PrefixStore shutdownStore_;
+  int worldSize_ = 0;
+  std::atomic<uint64_t> nextMessageID_{0};
+
+  // Metadata used for tracking of whether certain RPCs have timed out or not.
+  struct TimeoutMessageMetadata {
+    TimeoutMessageMetadata(
+        uint64_t messageId_,
+        std::shared_ptr<AtomicJitFuture> responseFuture_,
+        std::chrono::milliseconds timeout_)
+        : messageId(messageId_),
+          responseFuture(std::move(responseFuture_)),
+          timeout(timeout_) {}
+    uint64_t messageId;
+    std::shared_ptr<AtomicJitFuture> responseFuture;
+    std::chrono::milliseconds timeout;
+  };
+
+  // Map to store the expiration times for each message.
+  std::map<steady_clock_time_point, std::vector<TimeoutMessageMetadata>>
+      timeoutMap_;
+
+  // Map to store the messageId to expiry time.
+  std::unordered_map<uint64_t, steady_clock_time_point> messageIdToTimeout_;
+
+  // Thread that will poll the timeoutMap_ for timed out messages and mark them
+  // with an error accordingly
+  std::thread timeoutThread_;
+
+  // Function run by the timeoutThread_ to check for timed out RPCs
+  void pollTimeoutRpcs();
+
+  // Mutex to guard the timeoutMap_
+  std::mutex timeoutMapMutex_;
+
+  // Condition Variable to signal population of the timeoutMap_
+  std::condition_variable timeoutThreadCV_;
+
+  // Returns the expiration time for an RPC by adding the current time to the
+  // passed in timeout.
+  inline steady_clock_time_point computeRpcMessageExpiryTime(
+      std::chrono::milliseconds timeout) const {
+    return std::chrono::time_point_cast<std::chrono::milliseconds>(
+        std::chrono::steady_clock::now() + timeout);
+  }
+
+  // Handle error on an outgoing pipe
+  void handleClientError(
+      ClientPipe& clientPipe,
+      const tensorpipe::Error& error);
+
+  // This is a generic struct for capturing Time-Series Metrics. It keeps a
+  // running sum and count of data points (observations), and can return an
+  // average of the data points seen so far. This is currently only used for
+  // tracking the GIL Wait Time in RPC Agents, but can be used for other metrics
+  // as well.
+  struct TimeSeriesMetricsTracker {
+    // Running sum of the data points seen so far
+    uint64_t currentSum_;
+    // Running count of the data points seen so far
+    uint64_t currentCount_;
+
+    explicit TimeSeriesMetricsTracker(
+        uint64_t currentSum = 0,
+        uint64_t currentCount = 0);
+
+    // Adds a data point (which is basically one observation for the metric
+    // being tracked) to the running sum and count.
+    void addData(uint64_t dataPoint);
+    // Returns the average of all the data points seen so far.
+    float computeAverage() const;
+  };
+
+  // Map of Time-Series metrics tracked by the RPC Agent
+  std::unordered_map<std::string, TimeSeriesMetricsTracker> timeSeriesMetrics_;
+  // Mutex to guard timeSeriesMetrics_
+  std::mutex metricsMutex_;
+
+  // Custom lock guard used to check if the RPC group is dynamic and lock the
+  // mutex if so
+  struct GroupMembershipLockGuard {
+    GroupMembershipLockGuard(std::mutex& mutex, bool isStaticGroup)
+        : ref_(mutex), isStaticGroup_(isStaticGroup) {
+      if (isStaticGroup_) {
+        ref_.lock();
+      }
+    }
+
+    ~GroupMembershipLockGuard() {
+      if (isStaticGroup_) {
+        ref_.unlock();
+      }
+    }
+
+    GroupMembershipLockGuard(const GroupMembershipLockGuard&) = delete;
+
+   private:
+    std::mutex& ref_;
+    bool isStaticGroup_;
+  };
+  // Mutex to guard access to group membership data
+  // e.g. updates to (workerIdToInfo_, workerNameToInfo_, workerNameToURL_)
+  mutable std::mutex groupMembershipMutex_;
+
+  // Map to Track Network Data
+  NetworkDataDict networkData_;
+  // Mutex to guard networkData_
+  std::mutex networkDataMutex_;
+
+  // A mutex and a cv to guard access to the call counts and watch for changes.
+  std::mutex callCountMutex_;
+  std::condition_variable callCountCV_;
+  // Running total of un-processed, un-errored RPC calls sent
+  int32_t clientActiveCalls_{0};
+  // Running total of un-processed RPC requests received
+  int32_t serverActiveCalls_{0};
+  // Running total of RPC requests that will be completed asynchronously
+  int32_t serverActiveAsyncCalls_{0};
+
+  // Whether a global graceful shutdown has begun, in which case we'll silence
+  // error messages due to remote workers closing their pipes.
+  std::atomic<bool> shuttingDown_{false};
+
+  // Helpers to modify the counts while correctly dealing with the mutex and cv.
+  void increaseCallCount(int32_t& count);
+  void decreaseCallCount(int32_t& count);
+
+  // Helpers to set the state of the requests.
+  void markFutureAsComplete(
+      std::shared_ptr<AtomicJitFuture> atomicFuture,
+      c10::intrusive_ptr<Message> message,
+      std::vector<c10::Stream> streams);
+  void markFutureWithError(
+      std::shared_ptr<AtomicJitFuture> atomicFuture,
+      std::string errorMsg);
+};
+
+} // namespace rpc
+} // namespace distributed
+} // namespace torch
+
+#endif // USE_TENSORPIPE

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/distributed/rpc/utils.h

@@ -0,0 +1,90 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/Event.h>
+#include <c10/core/Stream.h>
+#include <torch/csrc/autograd/profiler.h>
+#include <torch/csrc/distributed/rpc/rpc_command_base.h>
+#include <torch/csrc/jit/serialization/pickle.h>
+#include <torch/csrc/utils/byte_order.h>
+
+namespace torch {
+namespace distributed {
+namespace rpc {
+
+// Parse error message and return RPCErrorType based on the message.
+TORCH_API RPCErrorType getRPCErrorType(const JitFuture& jitFuture);
+// Create an error string given the error description and error type
+TORCH_API std::string makeRPCError(
+    const std::string& rpcErrorStr,
+    RPCErrorType errorType);
+
+// Given an RPC message received as a request over the wire, deserialize it into
+// the appropriate 'RpcCommandBase' type.
+TORCH_API std::unique_ptr<RpcCommandBase> deserializeRequest(
+    const Message& request);
+
+// Given an RPC message received as a response over the wire, deserialize it
+// into the appropriate 'RpcCommandBase' type, if the response is
+// FORWARD_AUTOGRAD_RESP type, unwrap it, attach recvBackward() functions
+// to received tensors and set the wrappedMsgType to its wrapped message type.
+TORCH_API std::unique_ptr<RpcCommandBase> deserializeResponse(
+    const Message& response,
+    MessageType& wrappedMsgType);
+
+// Given an RPC message received as a response over the wire, deserialize it
+// into the valid IValue if the message is for a script rpc result,
+// otherwise deserialize it into dummy none ivalue that will never be used.
+// In this deserialization, we also attach recv rpc backward functions if
+// needed.
+IValue deserializeResptoIValueInternal(
+    RpcCommandBase& rpc,
+    MessageType messageType);
+TORCH_API IValue deserializeRespToIValue(const Message& message);
+
+// Note: format is subject to change and intended for RPCs.
+// For saving persistently to disk, use torch::save().
+TORCH_API std::string wireSerialize(
+    const std::vector<char>& payload,
+    const std::vector<at::Tensor>& tensors);
+
+TORCH_API std::pair<std::vector<char>, std::vector<at::Tensor>> wireDeserialize(
+    const void* data,
+    size_t data_size);
+
+// We use vector<char> as the type of blobs because it's what rpc::Message uses
+// for its payload, even though it has the disadvantage that it cannot be
+// allocated with uninitialized memory: it is always zeroed out.
+
+// Some Tensors are effectively views of larger Tensors, where only a small
+// subset of the Storage data is referenced. This normally is good and avoids
+// copies when kept locally, but if we naively push the whole Storage over the
+// wire, we'll end up with excess network traffic. This change clones tensors if
+// we'd save at least half the data, and over a minimum hurdle.
+TORCH_API c10::List<at::Tensor> cloneSparseTensors(
+    const std::vector<at::Tensor>& tensors);
+
+// Combines an original payload and wrapped payload into the original payload.
+// Used to generate the overall payload for the wrapped RPC.
+TORCH_API void writeWrappedPayload(
+    std::vector<char>& originalPayload,
+    std::vector<char>& additionalPayload);
+
+// Reads the additional, wrapped payload from a wrapped RPC off of the input
+// payload. After this, payload will contain the payload of the original,
+// un-wrapped RPC.
+TORCH_API std::vector<at::IValue> readWrappedPayload(
+    std::vector<char>& payload,
+    const rpc::Message& message);
+
+// Takes a list of events from autograd profiler and populates them into
+// profiledEvents to be carried over RPC.
+TORCH_API void populateRemoteProfiledEvents(
+    std::vector<torch::autograd::profiler::LegacyEvent>& profiledEvents,
+    const torch::autograd::profiler::ProfilerConfig& profilerConfig,
+    const std::vector<std::vector<torch::autograd::profiler::LegacyEvent>>&
+        eventLists);
+
+} // namespace rpc
+} // namespace distributed
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/frontend/function_schema_parser.h

@@ -0,0 +1,17 @@
+#pragma once
+
+#include <ATen/core/function_schema.h>
+#include <c10/macros/Macros.h>
+#include <string>
+#include <variant>
+
+namespace torch {
+namespace jit {
+
+TORCH_API std::variant<c10::OperatorName, c10::FunctionSchema> parseSchemaOrName(
+    const std::string& schemaOrName);
+TORCH_API c10::FunctionSchema parseSchema(const std::string& schema);
+TORCH_API c10::OperatorName parseName(const std::string& name);
+
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/frontend/name_mangler.h

@@ -0,0 +1,27 @@
+#pragma once
+
+#include <ATen/core/qualified_name.h>
+#include <torch/csrc/Export.h>
+
+namespace torch {
+namespace jit {
+
+/**
+ * class NameMangler
+ *
+ * Utility to mangle qualified names in order to make them unique. We use this
+ * in various places where we to de-duplicate qualified names.
+ */
+class TORCH_API NameMangler {
+ public:
+  // Given a qualified name, return a mangled version that is guaranteed to be
+  // unique with respect to previous/future calls of `mangled()` on this name
+  // mangler instance.
+  c10::QualifiedName mangle(const c10::QualifiedName& name);
+
+ private:
+  size_t mangleIndex_ = 0;
+};
+
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/frontend/parser.h

@@ -0,0 +1,33 @@
+#pragma once
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/frontend/tree.h>
+#include <torch/csrc/jit/frontend/tree_views.h>
+#include <memory>
+
+namespace torch {
+namespace jit {
+
+struct Decl;
+struct ParserImpl;
+struct Lexer;
+
+TORCH_API Decl mergeTypesFromTypeComment(
+    const Decl& decl,
+    const Decl& type_annotation_decl,
+    bool is_method);
+
+struct TORCH_API Parser {
+  explicit Parser(const std::shared_ptr<Source>& src);
+  TreeRef parseFunction(bool is_method);
+  TreeRef parseClass();
+  Decl parseTypeComment();
+  Expr parseExp();
+  Lexer& lexer();
+  ~Parser();
+
+ private:
+  std::unique_ptr<ParserImpl> pImpl;
+};
+
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/frontend/schema_type_parser.h

@@ -0,0 +1,40 @@
+#pragma once
+
+#include <ATen/core/alias_info.h>
+#include <ATen/core/jit_type.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/FunctionRef.h>
+#include <torch/csrc/jit/frontend/lexer.h>
+
+namespace torch {
+namespace jit {
+
+using TypePtr = c10::TypePtr;
+
+struct TORCH_API SchemaTypeParser {
+  TypePtr parseBaseType();
+  c10::optional<c10::AliasInfo> parseAliasAnnotation();
+  std::pair<TypePtr, c10::optional<c10::AliasInfo>> parseType();
+  std::tuple</*fake*/ TypePtr, /*real*/ TypePtr, c10::optional<c10::AliasInfo>>
+  parseFakeAndRealType();
+  c10::optional<at::ScalarType> parseTensorDType(const std::string& dtype);
+  TypePtr parseRefinedTensor();
+
+  SchemaTypeParser(Lexer& L, bool parse_complete_tensor_types)
+      : complete_tensor_types(parse_complete_tensor_types), L(L) {}
+
+ private:
+  c10::optional<bool> tryToParseRequiresGrad();
+  c10::optional<c10::Device> tryToParseDeviceType();
+  void parseList(
+      int begin,
+      int sep,
+      int end,
+      c10::function_ref<void()> callback);
+
+  bool complete_tensor_types;
+  Lexer& L;
+  size_t next_id = 0;
+};
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/passes/utils/check_alias_annotation.h

@@ -0,0 +1,22 @@
+#pragma once
+
+#include <ATen/core/ivalue.h>
+#include <torch/csrc/jit/ir/ir.h>
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace torch {
+namespace jit {
+
+// Verify that alias annotations are correct. See impl for definition of
+// "correct".
+//
+// This function expects a graph with a single op with `unqualifiedOpName`, plus
+// the inputs that you would otherwise have passed to the graph executor.
+TORCH_API void checkAliasAnnotation(
+    const std::shared_ptr<Graph>& graph,
+    std::vector<IValue> pythonInputs,
+    const std::string& unqualifiedOpName);
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/passes/utils/memory_dag.h

@@ -0,0 +1,175 @@
+#pragma once
+
+#include <ATen/core/jit_type.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Optional.h>
+#include <c10/util/flat_hash_map.h>
+#include <c10/util/sparse_bitset.h>
+#include <torch/csrc/jit/ir/ir.h>
+#include <torch/csrc/jit/ir/type_hashing.h>
+#include <memory>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#include <torch/csrc/Export.h>
+
+// Uses a compressed index representation for faster comparisons
+typedef c10::SparseBitVector<256> MemoryLocations;
+namespace torch {
+namespace jit {
+
+struct Element;
+struct Value;
+class MemoryDAG;
+
+using AliasTypeSet = std::vector<TypePtr>;
+
+/**
+ * Helper to build up the points-to graph.
+ *
+ * We separate the "building" into a different class because it allows us to
+ * cache internally to MemoryDAG without worrying about how the DAG structure
+ * is mutated.
+ */
+class TORCH_API MemoryDAGBuilder {
+ public:
+  MemoryDAGBuilder() = default;
+  MemoryDAGBuilder(const MemoryDAGBuilder&) = delete;
+  MemoryDAGBuilder& operator=(const MemoryDAGBuilder&) = delete;
+
+  // Make `from` point at `to`.
+  void makePointerTo(Element* from, Element* to);
+
+  void addToContainedElements(Element* contained, Element* container);
+
+  // Make a fresh Element (i.e. an Element that doesn't point to anything) and
+  // return it.
+  Element* makeFreshValue(const Value* v);
+
+  friend MemoryDAG;
+
+ private:
+  // `MemoryDAGBuilder` builds up `indexToElementMap_`, then uses
+  // the map to construct the `MemoryDAG`
+  std::vector<std::unique_ptr<Element>> indexToElementMap_;
+};
+
+// class MemoryDAG
+//
+// This class tracks the "A points to B" graph for all values. It is used by
+// AliasDb to provide a higher-level API.
+//
+// We maintain a DAG where:
+//   - Vertices (called "Elements") represent Values and
+//     other aliasing entities (e.g. the stuff inside a list)
+//   - Edges represent a "points-to" relationship.
+//
+// Leaves in this DAG are entities that don't point to anything, and thus
+// correspond to unique "memory locations".
+//
+// So, by traversing the "points-to" graph to the leaves, you can determine
+// which memory locations an element may point to.
+class TORCH_API MemoryDAG {
+ public:
+  explicit MemoryDAG(std::unique_ptr<MemoryDAGBuilder> builder)
+      : indexToElementMap_(std::move(builder->indexToElementMap_)) {}
+  // explicitly delete copy constructor because otherwise windows build is
+  // confused for an exported class see
+  // https://stackoverflow.com/a/51033485/105137
+  MemoryDAG(const MemoryDAG&) = delete;
+  MemoryDAG& operator=(const MemoryDAG&) = delete;
+
+  // Return the unique memory locations that `Element` might represent.
+  const MemoryLocations& getMemoryLocations(const Element* e) const;
+
+  // Do `a` and `b` potentially share a memory location?
+  bool mayAlias(const Element* a, const Element* b) const;
+
+  // Does `a` hold reference to any memory that is stored in `b`, or vice versa?
+  bool mayContainAlias(const Element* a, const Element* b) const;
+
+  bool mayContainAlias(const Element* a, const at::ArrayRef<Element*> b) const;
+
+  bool mayContainAlias(
+      const at::ArrayRef<Element*> a,
+      const at::ArrayRef<Element*> b) const;
+
+  // Converts from the compressed index representation
+  const Element* fromIndex(unsigned x) const;
+  Element* fromIndex(unsigned x);
+  void collectAllContainedMemoryLocations(
+      const Element* elem,
+      MemoryLocations& cont) const;
+
+  /**
+   * The following methods are special cases where we need to mutate the
+   * internals of MemoryDAG for efficiency reasons. Don't call them unless you
+   * know what you're doing! In particular, don't add new mutating methods
+   * without ensuring that you are maintaining cache consistency for memory
+   * locations.
+   */
+
+  // Adding wildcards can trigger extremely expensive cache invalidations. This
+  // method adds them in a more efficient cache-aware way.
+  void setWildcards(
+      const std::unordered_set<const Value*>& wildcards,
+      const ska::flat_hash_map<const Value*, Element*>& elementMap,
+      const std::function<Element*(const Value*)>& getWildcardElement);
+  Element* unsafeMakeFreshValue(const Value* v);
+
+ private:
+  const MemoryLocations& getAllContainedMemoryLocations(
+      const Element* elem) const;
+  void collectAllContainedMemoryLocationsImpl(
+      const Element* elem,
+      MemoryLocations& cont) const;
+  std::vector<std::unique_ptr<Element>> indexToElementMap_;
+};
+
+// `Element` represents a vertex in the points-to graph. It represents
+// anything that could have an aliasing relationship--mostly IR
+// `Value`s, but also wildcards or the type inside a container (e.g. `T`
+// in `List[T]`)
+struct Element {
+  Element(const Value* value_, unsigned index_);
+  // wildcard constructor
+  explicit Element(unsigned index_);
+
+  // Index into the owning DAG's bit vector that represents this element.
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  unsigned index;
+
+  // All elements that this element *may* point to. It's possible to have
+  // multiple elements that you might point to due to control flow/complex ops
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  MemoryLocations pointsTo;
+  // Backreference for points-to.
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  MemoryLocations pointedFrom;
+
+  // Elements can contain other elements (e.g. List[Tensor])
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  MemoryLocations containedElements;
+
+  // The values that this element corresponds to. May be empty if this element
+  // doesn't represent a first-class value.
+  // This is for debug information only.
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  std::unordered_set<const Value*> values;
+
+ private:
+  // Make `from` point at `to`.
+  void makePointerTo(Element* from, Element* to);
+
+  friend class MemoryDAG;
+  // We memoize the results of `getMemoryLocations` to speed up queries.
+  // A nullopt means that this cache is not yet populated. Since `MemoryDAG` is
+  // immutable, this cache should never need to be invalidated.
+  mutable c10::optional<MemoryLocations> cachedMemoryLocations_;
+
+  mutable c10::optional<MemoryLocations> cachedAllContainedMemoryLocations_;
+};
+
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/analysis.h

@@ -0,0 +1,406 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/stmt.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+#include <utility>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+class HasRand : public IRVisitor {
+ public:
+  HasRand(StmtPtr stmt) : stmt_(std::move(stmt)) {
+    stmt_->accept(this);
+  }
+
+  bool has_rand() const {
+    return has_rand_;
+  }
+
+ private:
+  void visit(IntrinsicsPtr v) override {
+    if (v->op_type() == IntrinsicsOp::kRand) {
+      has_rand_ = true;
+    } else {
+      IRVisitor::visit(std::move(v));
+    }
+  }
+  StmtPtr stmt_;
+  bool has_rand_ = false;
+};
+
+template <typename Op>
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+class NodeFinder : public IRVisitor {
+ public:
+  void visit(NodePtr<Op> v) override {
+    nodes.push_back((NodePtr<Op>)v);
+    IRVisitor::visit(v);
+  }
+
+  static std::vector<NodePtr<Op>> find(StmtPtr s) {
+    NodeFinder<Op> nf;
+    s->accept(&nf);
+    return nf.nodes;
+  }
+
+  static std::vector<NodePtr<Op>> find(ExprPtr e) {
+    NodeFinder<Op> nf;
+    e->accept(&nf);
+    return nf.nodes;
+  }
+
+  std::vector<NodePtr<Op>> nodes;
+};
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+class VarFinder : public IRVisitor {
+ public:
+  void visit(VarPtr v) override {
+    vars_.insert(v);
+    IRVisitor::visit(std::move(v));
+  }
+
+  static std::unordered_set<VarPtr> find(StmtPtr s) {
+    VarFinder nf;
+    s->accept(&nf);
+    return nf.vars();
+  }
+
+  static std::unordered_set<VarPtr> find(ExprPtr e) {
+    VarFinder nf;
+    e->accept(&nf);
+    return nf.vars();
+  }
+
+  const std::unordered_set<VarPtr>& vars() {
+    return vars_;
+  }
+
+ private:
+  std::unordered_set<VarPtr> vars_;
+};
+
+class BufFinder : public IRVisitor {
+ public:
+  void visit(BufPtr v) override {
+    bufs_.insert(v);
+    IRVisitor::visit(std::move(v));
+  }
+
+  static std::unordered_set<BufPtr> find(StmtPtr s) {
+    BufFinder nf;
+    s->accept(&nf);
+    return nf.bufs();
+  }
+
+  static std::unordered_set<BufPtr> find(ExprPtr e) {
+    BufFinder nf;
+    e->accept(&nf);
+    return nf.bufs();
+  }
+
+  const std::unordered_set<BufPtr>& bufs() {
+    return bufs_;
+  }
+
+ private:
+  std::unordered_set<BufPtr> bufs_;
+};
+
+// Finds all kinds of write operations to the provided Buf.
+class WritesToBuf : public IRVisitor {
+ public:
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  WritesToBuf(BufPtr target) : target_(std::move(target)) {}
+
+  std::vector<StmtPtr> writes() {
+    return writes_;
+  }
+
+  static std::vector<StmtPtr> find(StmtPtr s, BufPtr b) {
+    WritesToBuf finder(std::move(b));
+    s->accept(&finder);
+    return finder.writes();
+  }
+
+ private:
+  void visit(StorePtr v) override {
+    if (v->buf() == target_) {
+      writes_.push_back(v);
+    }
+  }
+
+  void visit(AtomicAddPtr v) override {
+    if (v->buf() == target_) {
+      writes_.push_back(v);
+    }
+  }
+
+  BufPtr target_;
+  std::vector<StmtPtr> writes_;
+};
+
+class StmtsReadingBuf : public IRVisitor {
+ public:
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  StmtsReadingBuf(BufPtr target) : target_(std::move(target)) {}
+
+  std::vector<StmtPtr> reads() {
+    return reads_;
+  }
+
+  static std::vector<StmtPtr> find(StmtPtr s, BufPtr b) {
+    StmtsReadingBuf finder(std::move(b));
+    s->accept(&finder);
+    return finder.reads();
+  }
+
+ private:
+  bool readsBuffer(StmtPtr s) {
+    auto loads = NodeFinder<Load>::find(std::move(s));
+    for (const auto& l : loads) {
+      if (l->buf() == target_) {
+        return true;
+      }
+    }
+    return false;
+  }
+
+  void visit(StorePtr v) override {
+    if (readsBuffer(v)) {
+      reads_.push_back(v);
+    }
+  }
+
+  void visit(LetPtr v) override {
+    if (readsBuffer(v)) {
+      reads_.push_back(v);
+    }
+  }
+
+  void visit(CondPtr v) override {
+    if (readsBuffer(v)) {
+      reads_.push_back(v);
+    }
+  }
+
+  void visit(AtomicAddPtr v) override {
+    if (readsBuffer(v)) {
+      reads_.push_back(v);
+    }
+  }
+
+  BufPtr target_;
+  std::vector<StmtPtr> reads_;
+};
+
+class ExternalAllocBufFinder : public IRVisitor {
+ public:
+  void visit(ExternalCallWithAllocPtr v) override {
+    const auto& bufs_out = v->buf_out_args();
+    bufs_.insert(bufs_out.begin(), bufs_out.end());
+    IRVisitor::visit(std::move(v));
+  }
+
+  static std::unordered_set<BufPtr> find(StmtPtr s) {
+    ExternalAllocBufFinder f;
+    s->accept(&f);
+    return f.bufs();
+  }
+
+  static std::unordered_set<BufPtr> find(ExprPtr e) {
+    ExternalAllocBufFinder f;
+    e->accept(&f);
+    return f.bufs();
+  }
+
+  const std::unordered_set<BufPtr>& bufs() {
+    return bufs_;
+  }
+
+ private:
+  std::unordered_set<BufPtr> bufs_;
+};
+
+// Traverses the IR to determine if a particular Var is modified within it.
+class ModifiesVarChecker : public IRVisitor {
+ public:
+  ModifiesVarChecker(VarPtr v) : var_(std::move(v)) {}
+
+  static bool check(StmtPtr s, VarPtr v) {
+    ModifiesVarChecker checker(std::move(v));
+    s->accept(&checker);
+    return checker.found();
+  }
+
+  bool found() {
+    return found_;
+  }
+
+ private:
+  void visit(StorePtr v) override {
+    if (v->buf()->base_handle() == var_) {
+      found_ = true;
+      return;
+    }
+    IRVisitor::visit(std::move(v));
+  }
+
+  void visit(AtomicAddPtr v) override {
+    if (v->buf()->base_handle() == var_) {
+      found_ = true;
+      return;
+    }
+    IRVisitor::visit(std::move(v));
+  }
+
+  void visit(LetPtr v) override {
+    if (v->var() == var_) {
+      found_ = true;
+      return;
+    }
+    IRVisitor::visit(std::move(v));
+  }
+
+  void visit(ForPtr v) override {
+    if (v->var() == var_) {
+      found_ = true;
+      return;
+    }
+    IRVisitor::visit(std::move(v));
+  }
+
+  VarPtr var_;
+  bool found_{false};
+};
+
+// Traverse the Block stmt to identify the live range of the specified buf. The
+// live range, indicated by a pair of integers, specifies the first and last
+// stmt in block stmts that access to the buf.
+class BufLiveRange : public IRVisitor {
+ public:
+  BufLiveRange(BufPtr b) : buf_(std::move(b)) {}
+
+  static std::tuple<int32_t, int32_t> liveRange(StmtPtr s, BufPtr b) {
+    BlockPtr block = to<Block>(std::move(s));
+    // We Only analyze buffer live ranges for block stmts.
+    if (!block) {
+      return std::make_tuple(0, 0);
+    }
+
+    BufLiveRange analyzer(std::move(b));
+    block->accept(&analyzer);
+    return analyzer.getLiveRange();
+  }
+
+ private:
+  std::tuple<int32_t, int32_t> getLiveRange() {
+    return std::make_tuple(begin_, end_);
+  }
+
+  bool hasBufReads(StmtPtr s) {
+    auto loads1 = NodeFinder<Load>::find(s);
+    for (const auto& l : loads1) {
+      if (l->buf() == buf_) {
+        return true;
+      }
+    }
+    auto loads2 = NodeFinder<ExternalCall>::find(s);
+    for (const auto& l : loads2) {
+      for (const auto& lb : l->buf_args()) {
+        if (lb == buf_) {
+          return true;
+        }
+      }
+    }
+    auto loads3 = NodeFinder<ExternalCallWithAlloc>::find(std::move(s));
+    for (const auto& l : loads3) {
+      for (const auto& lb : l->buf_args()) {
+        if (lb == buf_) {
+          return true;
+        }
+      }
+    }
+    return false;
+  }
+
+  bool hasBufWrites(StmtPtr s) {
+    auto writes1 = NodeFinder<Store>::find(s);
+    for (const auto& w : writes1) {
+      if (w->buf() == buf_) {
+        return true;
+      }
+    }
+    auto writes2 = NodeFinder<ExternalCall>::find(s);
+    for (const auto& w : writes2) {
+      if (w->buf() == buf_) {
+        return true;
+      }
+    }
+    auto writes3 = NodeFinder<ExternalCallWithAlloc>::find(std::move(s));
+    for (const auto& w : writes3) {
+      for (const auto& wb : w->buf_out_args()) {
+        if (wb == buf_) {
+          return true;
+        }
+      }
+    }
+    return false;
+  }
+
+  void findAccAndUpdateLiveRange(StmtPtr s) {
+    bool has_reads = hasBufReads(s), has_writes = hasBufWrites(std::move(s));
+    if (has_reads || has_writes) {
+      if (begin_ == -1) {
+        begin_ = curr_index_;
+      };
+      end_ = curr_index_;
+    }
+  }
+
+  void visit(BlockPtr v) override {
+    for (const StmtPtr& s : *v) {
+      curr_index_ += 1;
+      findAccAndUpdateLiveRange(s);
+    }
+  }
+
+  BufPtr buf_;
+  int32_t begin_ = -1;
+  int32_t end_ = -1;
+  int32_t curr_index_ = -1;
+};
+
+// A class that analyzes the given program relevant for Block backend
+// It creates a map of multi dim buffers and their flat versions
+class CreateBufferMap : public IRVisitor {
+ public:
+  const std::unordered_map<std::string, BufPtr>& getBufferMap() const {
+    return map_input_to_tensor_bufs_;
+  }
+
+ private:
+  void visit(StorePtr v) override {
+    auto load_node = to<Load>(v->value());
+    if (load_node) {
+      auto t_buf = load_node->buf();
+      map_input_to_tensor_bufs_.emplace(t_buf->name_hint(), v->buf());
+    } else {
+      auto add_node = to<Add>(v->value());
+      auto mul_node = to<Mul>(v->value());
+      // This means for now, v->value() can be Add or Mul
+      TORCH_INTERNAL_ASSERT(add_node || mul_node, buildErrorMessage());
+      map_input_to_tensor_bufs_.emplace(v->buf()->name_hint(), v->buf());
+    }
+    v->value()->accept(this);
+  }
+  std::unordered_map<std::string, BufPtr> map_input_to_tensor_bufs_;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/block_codegen.h

@@ -0,0 +1,150 @@
+#pragma once
+
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+
+#include <ATen/ATen.h>
+#include <torch/csrc/jit/resource_guard.h>
+#include <torch/csrc/jit/tensorexpr/analysis.h>
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/unique_name_manager.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// A class that analyzes the given program relevant for Block backend.
+class BlockAnalysis : public IRVisitor {
+ public:
+  bool is_buf_store_target(BufPtr buf) const {
+    return store_targets_.count(buf) > 0;
+  }
+
+  const std::unordered_set<BufPtr>& loads() const {
+    return loads_;
+  }
+
+  const std::unordered_set<BufPtr>& stores() const {
+    return store_targets_;
+  }
+
+  int block_size() const {
+    return block_size_;
+  }
+
+  bool areBufsInMap(const std::unordered_set<BufPtr>& bufs) const;
+
+  BufPtr getMultiDimBuf(BufPtr buf) const;
+
+  std::string getInputName(BufPtr buf) const;
+
+  std::string getFlatInputName(BufPtr buf) const {
+    return getInputName(std::move(buf)) + "_flat";
+  }
+
+  std::unordered_map<std::string, BufPtr> getBufferMap() const {
+    return map_input_to_tensor_bufs_;
+  }
+
+ private:
+  void visit(StorePtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(ForPtr v) override;
+
+  std::unordered_map<std::string, BufPtr> map_input_to_tensor_bufs_;
+  std::unordered_set<BufPtr> store_targets_;
+  std::unordered_set<BufPtr> loads_;
+  int block_size_ = 32;
+};
+
+// A class that overrides the underlying IRPrinter to produce Block.
+class BlockPrinter : public IRPrinter {
+ public:
+  BlockPrinter(std::ostream* os, BlockAnalysis* block_analysis)
+      : IRPrinter(*os), block_analysis_(block_analysis) {}
+
+  using IRPrinter::name_manager;
+  using IRPrinter::visit;
+
+ private:
+  BlockAnalysis* block_analysis_;
+  std::unordered_map<std::string, int> dim_values_map;
+  std::vector<std::string> dim_names = {"N", "H", "W", "C"};
+  std::vector<std::string> flat_dim_names = {"N", "NH", "NHW", "NHWC"};
+  void PrintTensorInfo(const std::unordered_set<BufPtr>& bufs);
+  void PrintArguments(const std::unordered_set<BufPtr>& bufs);
+  void PrintBufferInfo(const std::unordered_set<BufPtr>& bufs);
+  void PrintDistribution(const std::unordered_set<BufPtr>& bufs);
+  void PrintLoop(const std::unordered_set<BufPtr>& bufs, bool block_idx = true);
+  void PrintReshapeInfo(
+      const std::unordered_set<BufPtr>& bufs,
+      bool reverse = false);
+  void PrintDMAs(const std::unordered_set<BufPtr>& bufs);
+  void PrintAdjustBuffers(const std::unordered_set<BufPtr>& bufs);
+
+  void visit(ForPtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(StorePtr v) override;
+  void visit(BlockPtr v) override;
+  void visit(AddPtr v) override;
+  void visit(MulPtr v) override;
+};
+
+class TORCH_API BlockCodeGen : public CodeGen {
+ public:
+  template <typename... Ts>
+  /* implicit */
+  BlockCodeGen(StmtPtr stmt, Ts... ts)
+      : CodeGen(
+            stmt,
+            std::vector<BufferArg>({BufferArg(ts)...}),
+            at::Device(at::kCPU)) {
+    Initialize();
+  }
+
+  BlockCodeGen(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& buffer_args,
+      at::Device device = at::Device(at::kCPU),
+      const std::string& kernel_func_name = "func")
+      : CodeGen(stmt, buffer_args, device, kernel_func_name) {
+    Initialize();
+  }
+
+  ~BlockCodeGen() override;
+
+  void call(const std::vector<CallArg>& args) override;
+  void call_raw(const std::vector<void*>& args) override;
+
+  void Initialize();
+
+  std::string getCodeText(const std::string& attr = "") override {
+    return oss_.str();
+  }
+
+ private:
+  UniqueNameManager* name_manager() {
+    if (!printer_) {
+      throw std::runtime_error("Null IRPrinter is not expected");
+    }
+    return printer_->name_manager();
+  }
+
+  std::ostream& os() {
+    return printer_->os();
+  }
+
+  std::ostringstream oss_;
+  std::unique_ptr<BlockPrinter> printer_;
+  std::unique_ptr<BlockAnalysis> block_analysis_;
+
+  std::string GetUniqueFuncName(const std::string& func_prefix);
+};
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/bounds_inference.h

@@ -0,0 +1,80 @@
+#pragma once
+
+#include <map>
+#include <unordered_map>
+#include <vector>
+
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/tensorexpr/mem_dependency_checker.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class Expr;
+class Buf;
+class Stmt;
+
+enum C10_API_ENUM TensorAccessKind { kLoad, kStore, kMutate };
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+struct TORCH_API TensorAccessBoundsInfo {
+  TensorAccessKind kind;
+  std::vector<ExprPtr> start;
+  std::vector<ExprPtr> stop;
+};
+
+using BoundsInfo =
+    std::unordered_map<BufPtr, std::vector<TensorAccessBoundsInfo>>;
+
+TORCH_API BoundsInfo inferBounds(StmtPtr s, bool distinctAccessKinds = true);
+
+// Bounds inference caching the analysis. The MemDependencyChecker must already
+// have been run.
+TORCH_API BoundsInfo getInferredBounds(
+    analysis::MemDependencyChecker& analyzer,
+    StmtPtr s,
+    bool distinctAccessKinds = true);
+TORCH_API BoundsInfo getInferredBounds(
+    analysis::MemDependencyChecker& analyzer,
+    ExprPtr e,
+    bool distinctAccessKinds = true);
+
+TORCH_API void printBoundsInfo(const BoundsInfo& v);
+
+TORCH_API std::vector<ExprPtr> getBoundExtents(
+    const std::vector<TensorAccessBoundsInfo>& infos);
+
+// The kind of dependency found, in increasing order of exclusivity.
+enum class HazardKind {
+  ReadAfterWrite,
+  WriteAfterRead,
+  WriteAfterWrite,
+  NoDependency,
+};
+TORCH_API HazardKind getPotentialHazards(
+    analysis::MemDependencyChecker& analyzer,
+    StmtPtr A,
+    StmtPtr B);
+
+// Returns true if there is a conflicting overlap between accesses in
+// statements A and B. A conflicting overlap is an overlap in buffer accesses
+// where at least one of the accesses is a Store.
+TORCH_API bool hasConflictingOverlap(
+    analysis::MemDependencyChecker& analyzer,
+    StmtPtr A,
+    StmtPtr B);
+// Same as above, between accesses in stores S1 and S2.
+TORCH_API bool isOverlapping(
+    analysis::MemDependencyChecker& analyzer,
+    StorePtr S1,
+    StorePtr S2);
+// Same as above, between accesses in store S and load L.
+TORCH_API bool isOverlapping(
+    analysis::MemDependencyChecker& analyzer,
+    StorePtr S,
+    LoadPtr L);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/bounds_overlap.h

@@ -0,0 +1,128 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/expr.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+
+#include <deque>
+#include <utility>
+#include <vector>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+namespace analysis {
+
+// A simple class containing the start and end of a range in a single dimension.
+struct TORCH_API Bound {
+  ExprPtr start{nullptr};
+  ExprPtr end{nullptr};
+
+  // This stores whether or not the start and end of this Bound have previously
+  // been swapped. This occurs when the bound is in a loop with a negative
+  // stride.
+  bool swapped{false};
+
+  Bound() = default;
+  Bound(ExprPtr s, ExprPtr e) : start(std::move(s)), end(std::move(e)) {}
+
+  void print() const;
+  bool equals(const Bound& other) const;
+
+  // The comparison operators are conservative. If the compare operator returns
+  // true, it means that all the elements satisfy the logical expression. But
+  // the false does not mean the opposite comparison is satisfied. It could be
+  // but not always.
+  bool operator==(const Bound& other) const;
+  bool operator!=(const Bound& other) const;
+  bool operator<(const Bound& other) const;
+  bool operator<=(const Bound& other) const;
+  bool operator>(const Bound& other) const;
+  bool operator>=(const Bound& other) const;
+
+  void swap() {
+    std::swap(start, end);
+    swapped = !swapped;
+  }
+};
+
+struct BoundHash {
+  size_t operator()(const Bound& b) const {
+    return std::hash<ExprPtr>()(b.start) ^ std::hash<ExprPtr>()(b.end);
+  }
+};
+
+// The type of overlap found. Each condition is true only if none of the
+// previous conditions hold.
+//     ContainedOrEqual: All elements in the Bound A are in the Bound B (this
+//                       includes the case where the bounds are equal).
+//     Contains: All elements in the Bound B are in the Bound B.
+//     PartialOverlap: Any elements in the Bound B are in the Bound A.
+//     NoOverlap: No elements in the Bound A are in the bound B.
+enum class OverlapKind {
+  ContainedOrEqual,
+  Contains,
+  PartialOverlap,
+  NoOverlap
+};
+
+// The Bound comparison result.
+//     True: Every Bound element always satisfies the given comparison operator
+//     False: Every Bound element always does NOT satisfy the given comparison
+//     operator
+//     NotDetermined: Some elements satisfy the given comparison operator and
+//     some elements not
+enum class CmpEvalResult { True, False, NotDetermined };
+
+// Returns the kind of overlap between Bound A and Bound A in a single
+// dimension.
+OverlapKind TORCH_API boundOverlap(Bound A, Bound B);
+
+// The comparison is conservative and the compare result is deterministic.
+// It means that every element of the Bound to be compared needs to satisfy
+// the given comparison operator.
+CmpEvalResult TORCH_API compareBound(
+    const Bound& a,
+    const Bound& b,
+    const CompareSelectOperation& cmp_op);
+
+// A multi dimensional bound representing the bound of a set of indices.
+using IndexBounds = std::vector<Bound>;
+
+// Returns true if two IndexBounds are equivalent.
+bool TORCH_API indexBoundsEquals(const IndexBounds& A, const IndexBounds& B);
+
+// Flattens a multi dimensional bound to a single dimension. The IndexBounds "a"
+// *must* encapsulate the entire range of the buffer.
+Bound TORCH_API flattenBounds(const IndexBounds& a);
+
+// Determines the kind of overlap in X dimensions.
+OverlapKind TORCH_API overlaps(const IndexBounds& a, const IndexBounds& b);
+
+// Returns the Bound slices created by subtracing bound B from bound A.
+// Multiple Bounds can be returned in the case where B slices A into two
+// distinct regions with no overlap.
+//
+// For example:
+//    subtractBound((0, 10), (2, 4)) => [(0, 1), (5, 10)]
+//       bound A: (0, 10)
+//       bound B: (2, 4)
+//       If we remove slice (2, 4) from the slice (0, 10), we will be left
+//       with 2 slices, one at the start (0, 1), and one at the end (5, 10).
+//       So, the result of this subtraction is [(0, 1), (5, 10)].
+//
+// Note: this doesn't use IndexBounds because the Bounds returned do not
+// represent multiple different dimensions.
+std::vector<Bound> TORCH_API subtractBound(Bound a, Bound b);
+
+// Returns the bound slices created by subtracting the IndexBounds B from A.
+std::vector<IndexBounds> TORCH_API subtractIndicesBounds(
+    const IndexBounds& A,
+    const IndexBounds& B,
+    OverlapKind overlap);
+std::vector<IndexBounds> TORCH_API
+subtractIndicesBounds(const IndexBounds& A, const IndexBounds& B);
+
+} // namespace analysis
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/codegen.h

@@ -0,0 +1,283 @@
+#pragma once
+
+#include <ATen/ATen.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+#include <utility>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+template <typename T>
+class PaddedBuffer;
+
+class TORCH_API CodeGen {
+ public:
+  class BufferArg;
+  class CallArg;
+
+  template <typename... Ts>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CodeGen(StmtPtr stmt, Ts... ts)
+      : stmt_(std::move(stmt)), buffer_args_({BufferArg(ts)...}) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CodeGen(
+      StmtPtr stmt,
+      std::vector<BufferArg> buffer_args,
+      at::Device device = at::kCPU,
+      std::string kernel_func_name = "func");
+
+  virtual ~CodeGen() = default;
+
+  StmtPtr stmt() const {
+    return stmt_;
+  }
+
+  void set_stmt(StmtPtr s) {
+    stmt_ = s;
+  }
+
+  void apply_mutator(IRMutator* mutator) {
+    stmt_ = stmt_->accept_mutator(mutator);
+  }
+
+  void apply_visitor(IRVisitor* visitor) {
+    stmt_->accept(visitor);
+  }
+
+  std::vector<BufferArg>& buffer_args() {
+    return buffer_args_;
+  }
+
+  const std::vector<BufferArg>& buffer_args() const {
+    return buffer_args_;
+  }
+
+  at::Device device() {
+    return device_;
+  }
+
+  // This function returns the generated code as
+  // a string.
+  virtual std::string getCodeText(const std::string& attr = "") {
+    return ("");
+  }
+
+  // TODO: Figure out how to unify these call interfaces.
+
+  /// Call a function with a vector of CallArgs, which are tagged
+  /// unions that properly type the arguments.
+  virtual void call(const std::vector<CallArg>& args) = 0;
+
+  /// Call a function faster than a regular `call` by assuming that
+  /// the generated kernel already knows the type of the arguments, so
+  /// they can be type-punned with `void*`s.
+  virtual void call_raw(const std::vector<void*>& args) = 0;
+
+  /// Call a function even faster than a regular call, by assuming
+  /// that the number of thread blocks can be derived from `numel` via
+  /// a simple division, rather than evaluating an expression.
+  virtual void call_with_numel(void** args, int64_t numel);
+
+  virtual at::Tensor empty_strided(
+      c10::IntArrayRef size,
+      c10::IntArrayRef stride,
+      c10::optional<c10::ScalarType> dtype_opt,
+      c10::optional<c10::Layout> layout_opt,
+      c10::optional<c10::Device> device_opt,
+      c10::optional<bool> pin_memory_opt) {
+    return at::empty_strided(
+        size, stride, dtype_opt, layout_opt, device_opt, pin_memory_opt);
+  }
+
+  const std::string& kernel_func_name() const {
+    return kernel_func_name_;
+  }
+
+  void allocIntermediateBufs();
+
+ protected:
+  static void* argToPtr(const BufferArg& bufferArg, const CallArg& callArg);
+
+ private:
+  StmtPtr stmt_;
+  std::vector<BufferArg> buffer_args_;
+  at::Device device_ = at::kCPU;
+  std::string kernel_func_name_ = "func";
+};
+
+class TORCH_API ExtCallMemoryReuse : public IRMutator {
+  static std::unordered_map<std::string, std::string> makeExtCallFuncNameMap();
+  static const std::unordered_map<std::string, std::string> extCallFuncNameMap_;
+
+ public:
+  explicit ExtCallMemoryReuse(
+      const std::vector<CodeGen::BufferArg>& bufferArgs);
+  ~ExtCallMemoryReuse() override = default;
+  StmtPtr mutate(ExternalCallPtr v) override;
+
+ private:
+  std::unordered_set<BufPtr> bufferArgs_;
+};
+
+class CodeGen::BufferArg {
+ public:
+  BufferArg(const Tensor& tensor) : buf_(tensor.buf()) {}
+  BufferArg(const VarHandle& var) : var_(var.node()), isVar_(true) {}
+  BufferArg(const BufHandle& buf) : buf_(buf.node()) {}
+  BufferArg(BufPtr buf) : buf_(std::move(buf)) {}
+
+  VarPtr var() const {
+    return isVar_ ? var_ : buf_->base_handle();
+  }
+
+  BufPtr buf() const {
+    return buf_;
+  }
+
+  bool isVar() const {
+    return isVar_;
+  }
+
+  Dtype dtype() const {
+    return isVar_ ? var_->dtype() : buf_->dtype();
+  }
+
+ private:
+  VarPtr var_ = nullptr;
+  BufPtr buf_ = nullptr;
+  bool isVar_ = false;
+};
+
+class CodeGen::CallArg {
+ public:
+  template <typename T>
+  CallArg(const PaddedBuffer<T>& buffer);
+
+  template <typename T>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init,cppcoreguidelines-pro-type-const-cast)
+  CallArg(const std::vector<T>& buffer)
+      // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+      : data_(const_cast<T*>(buffer.data())) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CallArg(void* ptr) : data_(ptr) {}
+
+#define ARG_TYPE_CTOR(Type, Name)      \
+  CallArg(Type v) {                    \
+    memcpy(buffer_, &v, sizeof(Type)); \
+    data_ = (void*)buffer_;            \
+  }
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, ARG_TYPE_CTOR);
+#undef ARG_TYPE_CTOR
+
+  void* data() const {
+    return data_;
+  }
+
+  CallArg(const CallArg& rhs) {
+    if (rhs.data_ == rhs.buffer_) {
+      memcpy(this->buffer_, rhs.buffer_, sizeof(rhs.buffer_));
+      this->data_ = (void*)(this->buffer_);
+    } else {
+      this->data_ = rhs.data_;
+    }
+  }
+
+  CallArg& operator=(const CallArg& rhs) {
+    if (rhs.data_ == rhs.buffer_) {
+      memcpy(this->buffer_, rhs.buffer_, sizeof(rhs.buffer_));
+      this->data_ = (void*)(this->buffer_);
+    } else {
+      this->data_ = rhs.data_;
+    }
+    return *this;
+  }
+
+#define ARG_PTR_DEFINE(Type, Name)                  \
+  Type* Name##Ptr() const {                         \
+    TORCH_INTERNAL_ASSERT(data_ == (void*)buffer_); \
+    return (Type*)data_;                            \
+  }
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, ARG_PTR_DEFINE);
+#undef ARG_PTR_DEFINE
+
+ private:
+  void* data_;
+  // Regarding a scalar value, CallArg uses void**=&data_ to store it. But the
+  // bit width of a pointer is 32bit on a 32bit platform. It cannot store the
+  // scalar if the bit width of the scalar is larger than 32bit, such as double
+  // and long. Hence, we add 8 bytes buffer dedicated to storing the scalar
+  // value regardless its bit width is less or greater than 32bits.
+  char buffer_[8] = {0}; // 64bits
+};
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+class RegisterCodeGenList {
+ public:
+  TORCH_API static RegisterCodeGenList& GetInstance() {
+    static RegisterCodeGenList codegen_list;
+    return codegen_list;
+  }
+
+  using StmtFactoryMethod = std::function<std::unique_ptr<CodeGen>(
+      StmtPtr stmt,
+      const std::vector<CodeGen::BufferArg>&,
+      at::Device device,
+      const std::string& kernel_func_name)>;
+
+  TORCH_API StmtFactoryMethod FindStmtFactoryMethod(const std::string& name);
+  RegisterCodeGenList(const RegisterCodeGenList&) = delete;
+  RegisterCodeGenList& operator=(const RegisterCodeGenList&) = delete;
+
+ private:
+  template <class CodeGenType>
+  friend class RegisterCodeGen;
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  RegisterCodeGenList() = default;
+  TORCH_API void AddStmtFactoryMethod(
+      const std::string& name,
+      const StmtFactoryMethod& stmt_factory_method);
+
+  std::unordered_map<std::string, StmtFactoryMethod> stmt_factory_methods_;
+};
+
+template <class CodeGenType>
+class RegisterCodeGen {
+ public:
+  explicit RegisterCodeGen(const std::string& name) {
+    RegisterCodeGenList& codegen_list = RegisterCodeGenList::GetInstance();
+    codegen_list.AddStmtFactoryMethod(
+        name,
+        [](StmtPtr stmt,
+           const std::vector<CodeGen::BufferArg>& params,
+           at::Device device,
+           const std::string& kernel_func_name) {
+          // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+          std::unique_ptr<CodeGen> method(
+              new CodeGenType(stmt, params, device, kernel_func_name));
+          return method;
+        });
+  }
+};
+
+TORCH_API std::unique_ptr<CodeGen> CreateCodeGen(
+    const std::string& name,
+    StmtPtr stmt,
+    const std::vector<CodeGen::BufferArg>& params,
+    at::Device device = at::kCPU,
+    const std::string& kernel_func_name = "func");
+
+class TORCH_API GenericIntrinsicsExpander : public IRMutator {
+ protected:
+  ExprPtr mutate(IntrinsicsPtr v) override;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/cpp_codegen.h

@@ -0,0 +1,102 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class CppVarNameRewriter;
+
+// Generates C++ code from the IR.
+//
+// Vector operations are unrolled.
+// For example:
+// C[Ramp(0, 1, 3)] = A[Ramp(0, 2, 3)] + B[Ramp(0, 3, 3)];
+// is unrolled into:
+// C[0] = A[0] + B[0];
+// C[1] = A[2] + B[3];
+// C[2] = A[4] + B[6];
+class TORCH_API CppPrinter : public IRPrinter {
+ public:
+  explicit CppPrinter(std::ostream* os);
+  ~CppPrinter() override;
+
+  void printPrologue();
+
+  using IRPrinter::visit;
+
+  // Binary expressions.
+  void visit(ModPtr) override;
+  void visit(MaxPtr) override;
+  void visit(MinPtr) override;
+
+  // Conditional expressions.
+  void visit(CompareSelectPtr) override;
+  void visit(IfThenElsePtr) override;
+
+  // Tensor operations.
+  void visit(AllocatePtr) override;
+  void visit(FreePtr) override;
+  void visit(LoadPtr) override;
+  void visit(StorePtr) override;
+
+  // Casts.
+  void visit(CastPtr) override;
+  void visit(BitCastPtr) override;
+
+  // Calls.
+  void visit(IntrinsicsPtr) override;
+  void visit(ExternalCallPtr) override;
+
+  // Vars.
+  void visit(LetPtr) override;
+  void visit(VarPtr) override;
+
+  // Vector data types.
+  void visit(RampPtr) override;
+  void visit(BroadcastPtr) override;
+
+ private:
+  int lane_;
+  std::unordered_map<VarPtr, ExprPtr> vector_vars_;
+};
+
+class TORCH_API CppCodeGen : public CodeGen {
+ public:
+  CppCodeGen(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& buffer_args,
+      at::Device device = at::kCPU,
+      const std::string& kernel_func_name = "func");
+
+  ~CppCodeGen() override;
+
+  void call(const std::vector<CallArg>& args) override;
+  void call_raw(const std::vector<void*>& args) override;
+
+  template <typename... Ts>
+  void operator()(const Ts&... ts) {
+    call(std::vector<CallArg>({CallArg(ts)...}));
+  }
+
+  std::string getCodeText(const std::string& attr = "") override {
+    return oss_.str();
+  }
+
+ private:
+  void init();
+
+  std::ostream& os() {
+    return printer_->os();
+  }
+
+  std::ostringstream oss_;
+  std::unique_ptr<CppPrinter> printer_;
+  std::unique_ptr<CppVarNameRewriter> var_name_rewriter_;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/cpp_intrinsics.h

@@ -0,0 +1,36 @@
+#pragma once
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+constexpr auto cpp_intrinsics_definition = R"(
+namespace std {
+
+template <typename T,
+          typename std::enable_if<std::is_floating_point<T>::value, int>::type = 0>
+T rsqrt(T v) {
+  return 1.0f / std::sqrt(v);
+}
+
+template <typename T,
+          typename std::enable_if<std::is_floating_point<T>::value, int>::type = 0>
+T frac(T v) {
+  T intpart;
+  return std::modf(v, &intpart);
+}
+
+template <typename From, typename To>
+To bitcast(const From& v) {
+  assert(sizeof(To) == sizeof(From));
+  To res;
+  std::memcpy(&res, &v, sizeof(From));
+  return res;
+}
+
+} // namespace std
+)";
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/cuda_codegen.h

@@ -0,0 +1,295 @@
+#pragma once
+
+#include <unordered_map>
+#include <unordered_set>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <ATen/cuda/nvrtc_stub/ATenNVRTC.h>
+#include <c10/cuda/CUDACachingAllocator.h>
+#include <c10/cuda/CUDAGuard.h>
+#include <torch/csrc/jit/resource_guard.h>
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/eval.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/llvm_codegen.h>
+#include <torch/csrc/jit/tensorexpr/unique_name_manager.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// A class that analyzes the given program relevant for Cuda backends.
+class CudaAnalysis : public IRVisitor {
+ public:
+  CudaAnalysis() {
+    gpu_block_extents_ = {alloc<IntImm>(1), alloc<IntImm>(1), alloc<IntImm>(1)};
+    gpu_thread_extents_ = {
+        alloc<IntImm>(1), alloc<IntImm>(1), alloc<IntImm>(1)};
+  }
+  bool is_buf_store_target(BufPtr buf) const {
+    return store_targets_.count(buf) > 0;
+  }
+
+  const std::unordered_set<VarPtr>& thread_local_bufs() const {
+    return thread_local_bufs_;
+  }
+
+  const std::unordered_set<VarPtr>& cross_block_bufs() const {
+    return cross_block_bufs_;
+  }
+
+  const std::vector<ExprPtr>& gpu_block_extents() const {
+    return gpu_block_extents_;
+  }
+
+  const std::vector<ExprPtr>& gpu_thread_extents() const {
+    return gpu_thread_extents_;
+  }
+
+ private:
+  void visit(StorePtr v) override {
+    store_targets_.insert(v->buf());
+  }
+
+  void visit(AllocatePtr v) override;
+  void visit(FreePtr v) override;
+  void visit(PlacementAllocatePtr v) override;
+  void visit(ForPtr v) override;
+
+  std::unordered_set<BufPtr> store_targets_;
+  std::unordered_set<VarPtr> thread_local_bufs_;
+  std::unordered_set<VarPtr> cross_block_bufs_;
+
+  std::vector<ExprPtr> gpu_block_extents_;
+  std::vector<ExprPtr> gpu_thread_extents_;
+};
+
+// An IRMutator that replaces binding loop options with Cuda metavars, and masks
+// statements blocks which should execute with less reach than the launch
+// parameter extent.
+//
+// We do this by segmenting each block into chunks which should have the same
+// execution parameters, then if those params differ from the max mask each dim.
+class GPUMetaVarRewriter : public IRMutator {
+ public:
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  explicit GPUMetaVarRewriter(const CudaAnalysis* cuda_analysis)
+      : cuda_analysis_(cuda_analysis) {
+    gpu_block_vars_ = {
+        alloc<Var>("blockIdx.x", kInt),
+        alloc<Var>("blockIdx.y", kInt),
+        alloc<Var>("blockIdx.z", kInt)};
+    gpu_thread_vars_ = {
+        alloc<Var>("threadIdx.x", kInt),
+        alloc<Var>("threadIdx.y", kInt),
+        alloc<Var>("threadIdx.z", kInt)};
+
+    current_block_reach_ = {
+        alloc<IntImm>(1), alloc<IntImm>(1), alloc<IntImm>(1)};
+    current_thread_reach_ = {
+        alloc<IntImm>(1), alloc<IntImm>(1), alloc<IntImm>(1)};
+  }
+
+  StmtPtr mutate(ForPtr v) override;
+  StmtPtr mutate(BlockPtr v) override;
+
+  const std::vector<VarPtr>& gpu_block_vars() const {
+    return gpu_block_vars_;
+  }
+
+  const std::vector<VarPtr>& gpu_thread_vars() const {
+    return gpu_thread_vars_;
+  }
+
+  const std::vector<ExprPtr>& gpu_block_extents() const {
+    return cuda_analysis_->gpu_block_extents();
+  }
+
+  const std::vector<ExprPtr>& gpu_thread_extents() const {
+    return cuda_analysis_->gpu_thread_extents();
+  }
+
+ private:
+  // When processing a block, stores the contents of each sub-segment.
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  class Segment {
+   public:
+    void reset(bool mask) {
+      stmts_.clear();
+      mask_ = mask;
+    }
+
+    bool empty() const {
+      return stmts_.empty();
+    }
+
+    std::vector<StmtPtr>& stmts() {
+      return stmts_;
+    }
+    bool mask() {
+      return mask_;
+    }
+
+   private:
+    std::vector<StmtPtr> stmts_;
+    bool mask_{true};
+  };
+
+  // Returns true if the current execution scope is equivalent to the launch
+  // parameters.
+  bool isFullExtent();
+
+  std::vector<VarPtr> gpu_block_vars_;
+  std::vector<VarPtr> gpu_thread_vars_;
+
+  std::vector<ExprPtr> current_block_reach_;
+  std::vector<ExprPtr> current_thread_reach_;
+
+  const CudaAnalysis* cuda_analysis_;
+};
+
+// A class that overrides the underlying IRPrinter to produce Cuda C.
+class CudaPrinter : public IRPrinter {
+ public:
+  explicit CudaPrinter(
+      std::ostream* os,
+      const CudaAnalysis* cuda_analysis,
+      bool has_random)
+      : IRPrinter(*os), cuda_analysis_(cuda_analysis) {
+    if (has_random) {
+      rand_func_ = alloc<Var>("rand", kHandle);
+    }
+  }
+
+  void visit(CastPtr v) override;
+  void visit(IntrinsicsPtr v) override;
+  void visit(ForPtr v) override;
+
+  void visit(LoadPtr v) override;
+  void visit(StorePtr v) override;
+  void visit(AtomicAddPtr v) override;
+  void visit(MaxPtr v) override;
+  void visit(MinPtr v) override;
+  void visit(IfThenElsePtr v) override;
+  void visit(BlockPtr v) override;
+  void visit(AllocatePtr v) override;
+  void visit(FreePtr v) override;
+  void visit(LetPtr v) override;
+
+  void visit(ExternalCallPtr v) override;
+
+  VarPtr rand_func() const {
+    return rand_func_;
+  }
+
+  std::string dtypeToCppString(const Dtype& dtype) override;
+
+  using IRPrinter::name_manager;
+  using IRPrinter::visit;
+
+ private:
+  VarPtr rand_func_;
+  const CudaAnalysis* cuda_analysis_;
+
+  void print_flat_alloc(AllocatePtr alloc);
+};
+
+// Construct Cuda C from the buffer and tensor input, and invoke the kernel
+// when real arguments are provided.
+class TORCH_CUDA_CU_API CudaCodeGen : public CodeGen {
+ public:
+  template <typename... Ts>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CudaCodeGen(StmtPtr stmt, Ts... ts)
+      : CodeGen(
+            stmt,
+            std::vector<BufferArg>({BufferArg(ts)...}),
+            at::Device(at::kCUDA, at::cuda::current_device())) {
+    Initialize();
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CudaCodeGen(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& buffer_args,
+      at::Device device = at::Device(at::kCUDA, at::cuda::current_device()),
+      const std::string& kernel_func_name = "func")
+      : CodeGen(stmt, buffer_args, device, kernel_func_name) {
+    Initialize();
+  }
+
+  ~CudaCodeGen() override;
+
+  void call(const std::vector<CallArg>& args) override;
+  void call_raw(const std::vector<void*>& args) override;
+  void call_with_numel(void** args, int64_t numel) override;
+
+  template <typename... Ts>
+  void operator()(const Ts&... ts) {
+    call(std::vector<CallArg>({CallArg(ts)...}));
+  }
+
+  at::Tensor empty_strided(
+      c10::IntArrayRef size,
+      c10::IntArrayRef stride,
+      c10::optional<c10::ScalarType> dtype_opt,
+      c10::optional<c10::Layout> layout_opt,
+      c10::optional<c10::Device> device_opt,
+      c10::optional<bool> pin_memory_opt) override;
+
+  const std::vector<ExprPtr>& gpu_block_extents() const {
+    return cuda_analysis_->gpu_block_extents();
+  }
+
+  const std::vector<ExprPtr>& gpu_thread_extents() const {
+    return cuda_analysis_->gpu_thread_extents();
+  }
+
+  std::string getCodeText(const std::string& attr = "") override {
+    return oss_.str();
+  }
+
+ private:
+  void Initialize();
+
+  void CompileToNVRTC(const std::string& code, const std::string& func_name);
+
+  UniqueNameManager* name_manager() {
+    if (!printer_) {
+      throw std::runtime_error("Null IRPrinter is not expected");
+    }
+    return printer_->name_manager();
+  }
+
+  std::ostream& os() {
+    return printer_->os();
+  }
+
+  std::ostringstream oss_;
+  std::unique_ptr<CudaPrinter> printer_;
+  std::unique_ptr<CudaAnalysis> cuda_analysis_;
+  std::unique_ptr<GPUMetaVarRewriter> metavar_rewriter_;
+  std::unordered_set<std::string> taken_func_names;
+  std::mutex eval_lock_;
+  CUfunction function_;
+  bool has_random_ = false;
+  int thread_block_size_ = -1;
+
+  std::vector<bool> arg_pos_in_extents_;
+#ifdef TORCH_ENABLE_LLVM
+  std::vector<ExprEval<LLVMCodeGen>> block_extents_eval_;
+  std::vector<ExprEval<LLVMCodeGen>> thread_extents_eval_;
+#else
+  std::vector<ExprEval<SimpleIREvaluator>> block_extents_eval_;
+  std::vector<ExprEval<SimpleIREvaluator>> thread_extents_eval_;
+#endif
+
+  std::string GetUniqueFuncName(const std::string& func_prefix);
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/cuda_random.h

@@ -0,0 +1,104 @@
+#pragma once
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+constexpr auto philox_random_string = R"(
+
+class Philox {
+public:
+  __device__ inline Philox(unsigned long long seed,
+                           unsigned long long subsequence,
+                           unsigned long long offset) {
+    key.x = (unsigned int)seed;
+    key.y = (unsigned int)(seed >> 32);
+    counter = make_uint4(0, 0, 0, 0);
+    counter.z = (unsigned int)(subsequence);
+    counter.w = (unsigned int)(subsequence >> 32);
+    STATE = 0;
+    incr_n(offset / 4);
+  }
+
+  __device__ inline unsigned long operator()() {
+    if(STATE == 0) {
+      uint4 counter_ = counter;
+      uint2 key_ = key;
+      for(int i = 0; i < 9; i++) {
+        counter_ = single_round(counter_, key_);
+        key_.x += (kPhilox10A); key_.y += (kPhilox10B);
+      }
+      output = single_round(counter_, key_);
+      incr();
+    }
+    unsigned long ret;
+    switch(STATE) {
+      case 0: ret = output.x; break;
+      case 1: ret = output.y; break;
+      case 2: ret = output.z; break;
+      case 3: ret = output.w; break;
+    }
+    STATE = (STATE + 1) % 4;
+    return ret;
+  }
+
+private:
+  uint4 counter;
+  uint4 output;
+  uint2 key;
+  unsigned int STATE;
+  __device__ inline void incr_n(unsigned long long n) {
+    unsigned int nlo = (unsigned int)(n);
+    unsigned int nhi = (unsigned int)(n >> 32);
+    counter.x += nlo;
+    if (counter.x < nlo)
+      nhi++;
+    counter.y += nhi;
+    if (nhi <= counter.y)
+      return;
+    if (++counter.z)
+      return;
+    ++counter.w;
+  }
+  __device__ inline void incr() {
+    if (++counter.x)
+      return;
+    if (++counter.y)
+      return;
+    if (++counter.z)
+      return;
+    ++counter.w;
+  }
+  __device__ unsigned int mulhilo32(unsigned int a, unsigned int b,
+                                    unsigned int *result_high) {
+    *result_high = __umulhi(a, b);
+    return a*b;
+  }
+
+  __device__ inline uint4 single_round(uint4 ctr, uint2 key) {
+    unsigned int hi0;
+    unsigned int hi1;
+    unsigned int lo0 = mulhilo32(kPhiloxSA, ctr.x, &hi0);
+    unsigned int lo1 = mulhilo32(kPhiloxSB, ctr.z, &hi1);
+
+    uint4 ret = {hi1 ^ ctr.y ^ key.x, lo1, hi0 ^ ctr.w ^ key.y, lo0};
+    return ret;
+  }
+
+  static const unsigned long kPhilox10A = 0x9E3779B9;
+  static const unsigned long kPhilox10B = 0xBB67AE85;
+  static const unsigned long kPhiloxSA = 0xD2511F53;
+  static const unsigned long kPhiloxSB = 0xCD9E8D57;
+};
+
+// Inverse of 2^32.
+#define M_RAN_INVM32 2.3283064e-10f
+__device__  __inline__ float Uint32ToFloat(unsigned int x) {
+  return x * M_RAN_INVM32;
+}
+
+)";
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/eval.h

@@ -0,0 +1,347 @@
+#pragma once
+
+#include <cmath>
+#include <cstring>
+#include <type_traits>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include <c10/macros/Macros.h>
+#include <c10/util/Logging.h>
+#include <c10/util/math_compat.h>
+#include <c10/util/string_utils.h>
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/exceptions.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+#include <torch/csrc/jit/tensorexpr/types.h>
+#include <torch/csrc/jit/tensorexpr/var_substitutor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class InterpValue {
+ public:
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  InterpValue() : dtype_(kInt) {
+    Intvalues.push_back(0);
+  }
+
+  template <typename T>
+  InterpValue(Dtype dtype, T v) : dtype_(dtype) {
+#define TYPE_CASE(Type, Name)  \
+  if (dtype == k##Name) {      \
+    Name##values.push_back(v); \
+    return;                    \
+  }
+    AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+    throw unsupported_dtype();
+  }
+
+#define VALUE_CTOR(Type, Name)            \
+  InterpValue(Type v) : dtype_(k##Name) { \
+    Name##values.push_back(v);            \
+  }
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_CTOR);
+#undef VALUE_CTOR
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  explicit InterpValue(c10::quint8 v) : dtype_(kQUInt8) {
+    QUInt8values.emplace_back(v.val_);
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  explicit InterpValue(c10::qint8 v) : dtype_(kQInt8) {
+    QInt8values.emplace_back(v.val_);
+  }
+
+#define VALUE_VEC_CTOR(Type, Name)        \
+  InterpValue(const std::vector<Type>& v) \
+      : dtype_(Dtype(k##Name, v.size())), Name##values(v) {}
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_VEC_CTOR);
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  VALUE_VEC_CTOR(c10::quint8, QUInt8)
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  VALUE_VEC_CTOR(c10::qint8, QInt8)
+#undef VALUE_VEC_CTOR
+
+  template <typename T>
+  T as() const;
+
+  template <typename T>
+  const std::vector<T>& as_vec() const;
+
+  int64_t intValue() const;
+
+  Dtype dtype() const {
+    return dtype_;
+  }
+
+ private:
+  Dtype dtype_;
+
+#define VALUE_STORAGE(Type, Name) std::vector<Type> Name##values;
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_STORAGE);
+  VALUE_STORAGE(c10::qint8, QInt8);
+  VALUE_STORAGE(c10::quint8, QUInt8);
+#undef VALUE_STORAGE
+  void* ptr;
+};
+
+#define VALUE_AS_DISPATCH(Type, Name)         \
+  template <>                                 \
+  inline Type InterpValue::as<Type>() const { \
+    if (dtype_ != k##Name) {                  \
+      throw unsupported_dtype();              \
+    }                                         \
+    return Name##values[0];                   \
+  }
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_AS_DISPATCH);
+VALUE_AS_DISPATCH(c10::quint8, QUInt8);
+VALUE_AS_DISPATCH(c10::qint8, QInt8);
+#undef VALUE_AS_DISPATCH
+
+#define VALUE_AS_VEC_DISPATCH(Type, Name)                             \
+  template <>                                                         \
+  inline const std::vector<Type>& InterpValue::as_vec<Type>() const { \
+    if (dtype_.scalar_type() != ScalarType::Name) {                   \
+      throw unsupported_dtype();                                      \
+    }                                                                 \
+    return Name##values;                                              \
+  }
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_AS_VEC_DISPATCH);
+VALUE_AS_VEC_DISPATCH(c10::quint8, QUInt8);
+VALUE_AS_VEC_DISPATCH(c10::qint8, QInt8);
+#undef VALUE_AS_VEC_DISPATCH
+
+template <typename Type>
+auto underlyingValue(Type x) {
+  return x;
+}
+
+template <>
+inline auto underlyingValue<c10::quint8>(c10::quint8 x) {
+  return x.val_;
+}
+
+template <>
+inline auto underlyingValue<c10::qint8>(c10::qint8 x) {
+  return x.val_;
+}
+
+template <typename To, typename From>
+To raw_bitcast(const From& src) {
+  TORCH_CHECK(sizeof(To) == sizeof(From), "Invalid bitcast invocation");
+  To storage;
+  std::memcpy(&storage, &src, sizeof(To));
+  return reinterpret_cast<To&>(storage);
+}
+
+class SimpleIREvaluatorImpl;
+class TORCH_API SimpleIREvaluator : public CodeGen {
+ public:
+  SimpleIREvaluator(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& buffer_args,
+      at::Device device = at::kCPU,
+      const std::string& kernel_func_name = "func");
+
+  ~SimpleIREvaluator() override;
+
+  void call(const std::vector<CallArg>& args) override;
+  void call_raw(const std::vector<void*>& args) override;
+
+  template <typename... Ts>
+  void operator()(const Ts&... ts) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<CallArg> args({CallArg(ts)...});
+    call(args);
+  }
+
+  void bindVar(VarPtr v, ExprPtr e);
+  InterpValue value() const;
+
+ private:
+  void bindArg(const BufferArg& buf, void* data);
+  void expand_intrinsics() {
+    GenericIntrinsicsExpander intrinsics_expander;
+    apply_mutator(&intrinsics_expander);
+  }
+
+  std::unique_ptr<SimpleIREvaluatorImpl> impl_;
+};
+
+template <class CodeGenType>
+class ExprEval {
+ public:
+  using BufferArg = CodeGen::BufferArg;
+  using CallArg = CodeGen::CallArg;
+
+  template <typename... Ts>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  ExprEval(const ExprHandle& expr, Ts... ts)
+      : ExprEval(expr, {BufferArg(ts)...}) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  ExprEval(const ExprHandle& expr, const std::vector<BufferArg>& buffer_args)
+      : dtype_(expr.dtype()) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<BufferArg> buffer_args_extended = buffer_args;
+    BufHandle ret_buf("ret_val", {1}, dtype_);
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<ExprHandle> indices;
+    ExprHandle zero = IntImm::make(0);
+    for (size_t i = 0; i < ret_buf.ndim(); i++) {
+      indices.push_back(zero);
+    }
+    StmtPtr store_stmt = Store::make(ret_buf, indices, expr);
+    buffer_args_extended.emplace_back(ret_buf);
+    codegen_.reset(new CodeGenType(store_stmt, buffer_args_extended));
+  }
+
+  template <typename... Ts>
+  void operator()(Ts... ts) {
+    call(ts...);
+  }
+
+  void operator()(const std::vector<CallArg>& call_args) {
+    call(call_args);
+  }
+
+  void bindVar(VarPtr v, ExprPtr e) {
+    codegen_->bindVar(v, e);
+  }
+
+  void bindVar(const VarHandle& v, const ExprHandle& e) {
+    codegen_->bindVar(v.node(), e.node());
+  }
+
+  template <typename... Ts>
+  void call(Ts... ts) {
+    call({CallArg(ts)...});
+  }
+
+  void call(const std::vector<CallArg>& call_args) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<CallArg> call_args_extended = call_args;
+    switch (dtype_.scalar_type()) {
+#define TYPE_CASE(Type, Name)                           \
+  case ScalarType::Name: {                              \
+    std::vector<Type> ret_val_arg(1);                   \
+    call_args_extended.push_back(CallArg(ret_val_arg)); \
+    codegen_->call(call_args_extended);                 \
+    ret_value_ = InterpValue(ret_val_arg[0]);           \
+  } break;
+      // NOLINTNEXTLINE(modernize-use-emplace)
+      AT_FORALL_SCALAR_TYPES_AND2(Half, BFloat16, TYPE_CASE);
+      // NOLINTNEXTLINE(modernize-use-emplace)
+      TYPE_CASE(c10::quint8, QUInt8);
+      // NOLINTNEXTLINE(modernize-use-emplace)
+      TYPE_CASE(c10::qint8, QInt8);
+#undef TYPE_CASE
+      case ScalarType::Bool: {
+        // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+        std::vector<unsigned char> ret_val_arg(1);
+        call_args_extended.emplace_back(ret_val_arg.data());
+        codegen_->call(call_args_extended);
+        ret_value_ = InterpValue((bool)ret_val_arg[0]);
+      } break;
+      default:
+        throw unsupported_dtype();
+    }
+  }
+
+  void call_raw(const std::vector<void*>& args) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<void*> args_extended = args;
+    switch (dtype_.scalar_type()) {
+#define TYPE_CASE(Type, Name)                    \
+  case ScalarType::Name: {                       \
+    std::vector<Type> ret_val_arg(1);            \
+    args_extended.push_back(ret_val_arg.data()); \
+    codegen_->call_raw(args_extended);           \
+    ret_value_ = InterpValue(ret_val_arg[0]);    \
+  } break;
+      AT_FORALL_SCALAR_TYPES_AND2(Half, BFloat16, TYPE_CASE);
+      TYPE_CASE(c10::quint8, QUInt8);
+      TYPE_CASE(c10::qint8, QInt8);
+#undef TYPE_CASE
+      case ScalarType::Bool: {
+        // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+        std::vector<unsigned char> ret_val_arg(1);
+        args_extended.push_back(ret_val_arg.data());
+        codegen_->call_raw(args_extended);
+        ret_value_ = InterpValue((bool)ret_val_arg[0]);
+      } break;
+      default:
+        throw unsupported_dtype();
+    }
+  }
+
+  template <typename T>
+  T value(const std::vector<void*>& args) {
+    call_raw(args);
+    return ret_value_.as<T>();
+  }
+
+  template <typename T, typename... Ts>
+  T value(Ts... ts) {
+    call(std::forward<Ts>(ts)...);
+    return ret_value_.as<T>();
+  }
+
+  Dtype dtype() {
+    return dtype_;
+  }
+
+ private:
+  Dtype dtype_;
+  std::unique_ptr<CodeGenType> codegen_;
+  InterpValue ret_value_;
+};
+
+// Evaluates the given expression and returns an int64_t value if the result of
+// the given expression is int64_t.
+c10::optional<int64_t> evalInt(ExprPtr e);
+
+// Substitutes the given vars with their corresponding expressions in the input
+// expression.
+inline ExprPtr Substitute(ExprPtr expr, const VarMapping& var_mapping) {
+  VarSubMutator var_sub(var_mapping);
+  return expr->accept_mutator(&var_sub);
+}
+
+// Substitutes the given vars with their corresponding expressions in the input
+// statement.
+inline StmtPtr Substitute(StmtPtr stmt, const VarMapping& var_mapping) {
+  VarSubMutator var_sub(var_mapping);
+  return stmt->accept_mutator(&var_sub);
+}
+
+// Creates a clone of the input expression and substitutes the given vars with
+// their corresponding expressions in the clone.
+// NOTE: This works because cloning reuses variables and does not create new
+// ones, and `VarMapping` input has variables as the key.
+inline ExprPtr SubstituteInClone(ExprPtr expr, const VarMapping& var_mapping) {
+  VarSubMutator var_sub(var_mapping);
+  return Expr::clone(std::move(expr))->accept_mutator(&var_sub);
+}
+
+// Creates a clone of the input statement and substitutes the given vars with
+// their corresponding expressions in the clone.
+// NOTE: This works because cloning reuses variables and does not create new
+// ones, and `VarMapping` input has variables as the key.
+inline StmtPtr SubstituteInClone(StmtPtr stmt, const VarMapping& var_mapping) {
+  VarSubMutator var_sub(var_mapping);
+  return Stmt::clone(std::move(stmt))->accept_mutator(&var_sub);
+}
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/exceptions.h

@@ -0,0 +1,91 @@
+#pragma once
+
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+
+#include <sstream>
+#include <stdexcept>
+
+// Forward declarations of types
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+class Expr;
+class Stmt;
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+
+// Forward declarations of functions
+namespace std {
+TORCH_API std::string to_string(const torch::jit::tensorexpr::ExprPtr);
+TORCH_API std::string to_string(const torch::jit::tensorexpr::StmtPtr);
+} // namespace std
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class unsupported_dtype : public std::runtime_error {
+ public:
+  explicit unsupported_dtype() : std::runtime_error("UNSUPPORTED DTYPE") {}
+  explicit unsupported_dtype(const std::string& err)
+      : std::runtime_error("UNSUPPORTED DTYPE: " + err) {}
+};
+
+class out_of_range_index : public std::runtime_error {
+ public:
+  explicit out_of_range_index() : std::runtime_error("OUT OF RANGE INDEX") {}
+  explicit out_of_range_index(const std::string& err)
+      : std::runtime_error("OUT OF RANGE INDEX: " + err) {}
+};
+
+class unimplemented_lowering : public std::runtime_error {
+ public:
+  explicit unimplemented_lowering()
+      : std::runtime_error("UNIMPLEMENTED LOWERING") {}
+  explicit unimplemented_lowering(ExprPtr expr)
+      : std::runtime_error("UNIMPLEMENTED LOWERING: " + std::to_string(expr)) {}
+  explicit unimplemented_lowering(StmtPtr stmt)
+      : std::runtime_error("UNIMPLEMENTED LOWERING: " + std::to_string(stmt)) {}
+};
+
+class malformed_input : public std::runtime_error {
+ public:
+  explicit malformed_input() : std::runtime_error("MALFORMED INPUT") {}
+  explicit malformed_input(const std::string& err)
+      : std::runtime_error("MALFORMED INPUT: " + err) {}
+  explicit malformed_input(ExprPtr expr)
+      : std::runtime_error("MALFORMED INPUT: " + std::to_string(expr)) {}
+  explicit malformed_input(const std::string& err, ExprPtr expr)
+      : std::runtime_error(
+            "MALFORMED INPUT: " + err + " - " + std::to_string(expr)) {}
+  explicit malformed_input(StmtPtr stmt)
+      : std::runtime_error("MALFORMED INPUT: " + std::to_string(stmt)) {}
+  explicit malformed_input(const std::string& err, StmtPtr stmt)
+      : std::runtime_error(
+            "MALFORMED INPUT: " + err + " - " + std::to_string(stmt)) {}
+};
+
+class malformed_ir : public std::runtime_error {
+ public:
+  explicit malformed_ir() : std::runtime_error("MALFORMED IR") {}
+  explicit malformed_ir(const std::string& err)
+      : std::runtime_error("MALFORMED IR: " + err) {}
+  explicit malformed_ir(ExprPtr expr)
+      : std::runtime_error("MALFORMED IR: " + std::to_string(expr)) {}
+  explicit malformed_ir(const std::string& err, ExprPtr expr)
+      : std::runtime_error(
+            "MALFORMED IR: " + err + " - " + std::to_string(expr)) {}
+  explicit malformed_ir(StmtPtr stmt)
+      : std::runtime_error("MALFORMED IR: " + std::to_string(stmt)) {}
+  explicit malformed_ir(const std::string& err, StmtPtr stmt)
+      : std::runtime_error(
+            "MALFORMED IR: " + err + " - " + std::to_string(stmt)) {}
+};
+
+TORCH_API std::string buildErrorMessage(const std::string& s = "");
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/expr.h

@@ -0,0 +1,499 @@
+/**
+ * This file implements the core classes for Tensor Expressions.
+ *
+ * The structure of the expressions is inspired by Halide/TVM IR.
+ */
+#pragma once
+
+#include <c10/core/MemoryFormat.h>
+#include <c10/util/Optional.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/types.h>
+
+#include <utility>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+enum IRNodeType {
+  kPrimitive,
+  kAdd,
+  kSub,
+  kMul,
+  kDiv,
+  kMod,
+  kMax,
+  kMin,
+  kAnd,
+  kOr,
+  kLshift,
+  kRshift,
+  kXor,
+  kCompareSelect,
+  kCast,
+  kBitCast,
+  kOther,
+};
+
+// The common base between all expression node.
+class TORCH_API Expr : public std::enable_shared_from_this<Expr> {
+ public:
+  explicit Expr(Dtype dtype, IRNodeType expr_type = kOther)
+      : dtype_(dtype), expr_type_(expr_type) {}
+  virtual ~Expr() = default;
+  Dtype dtype() const {
+    return dtype_;
+  }
+  virtual void accept(IRVisitor* visitor) = 0;
+  virtual ExprPtr accept_mutator(IRMutator* mutator) = 0;
+
+  IRNodeType expr_type() const {
+    return expr_type_;
+  }
+  // Is this a fixed (constant) immediate value.
+  virtual bool isConstant() const {
+    return false;
+  }
+
+  void set_dtype(Dtype dtype) {
+    dtype_ = dtype;
+  }
+
+  /*
+   * Make a deep copy of the given expression.
+   *
+   * All sub-expressions inside the given expressions are also cloned. Note
+   * that the variables are not deep-copied since they are immutable.
+   */
+  static ExprPtr clone(ExprPtr s);
+
+ protected:
+  std::shared_ptr<Expr> getptr() {
+    return shared_from_this();
+  }
+
+ private:
+  Dtype dtype_;
+  IRNodeType expr_type_;
+};
+
+// A CRTP pattern to accept visitors for children class,
+// and dispatch back to the children.
+template <class Op, class Base = Expr>
+class ExprNode : public Base {
+ public:
+  using ExprNodeBase = ExprNode<Op>;
+  void accept(IRVisitor* visitor) override {
+    visitor->visit(static_to<Op>(Base::getptr()));
+  }
+  ExprPtr accept_mutator(IRMutator* mutator) override;
+  // pass the constructor to the base class
+  using Base::Base;
+};
+
+// A wrapper object to the underlying ExprNode.
+// Also serves the primary way to build and operate on other expressions.
+class TORCH_API ExprHandle {
+ public:
+  ExprHandle() = default;
+  explicit ExprHandle(ExprPtr node) : base_expr_node_(std::move(node)) {}
+
+  ExprPtr node() {
+    return base_expr_node_;
+  }
+
+  ExprPtr node() const {
+    return base_expr_node_;
+  }
+
+  bool empty() const {
+    return base_expr_node_ == nullptr;
+  }
+
+#define IMM_EXPR_DECLARE(Type, Name) ExprHandle(Type v);
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_EXPR_DECLARE);
+#undef IMM_EXPR_DECLARE
+
+  template <class Op>
+  NodePtr<Op> AsNode() {
+    return to<Op>(this->node());
+  }
+
+  template <class Op>
+  NodePtr<Op> AsNode() const {
+    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+    return const_cast<ExprHandle*>(this)->AsNode<Op>();
+  }
+
+  Dtype dtype() const {
+    return node()->dtype();
+  }
+
+  // Handling the math operators.
+  ExprHandle operator+(const ExprHandle& other) const;
+  ExprHandle operator-(const ExprHandle& other) const;
+  ExprHandle operator*(const ExprHandle& other) const;
+  ExprHandle operator/(const ExprHandle& other) const;
+  ExprHandle operator%(const ExprHandle& other) const;
+  ExprHandle operator==(const ExprHandle& other) const;
+  ExprHandle operator!=(const ExprHandle& other) const;
+  ExprHandle operator>(const ExprHandle& other) const;
+  ExprHandle operator>=(const ExprHandle& other) const;
+  ExprHandle operator<(const ExprHandle& other) const;
+  ExprHandle operator<=(const ExprHandle& other) const;
+  ExprHandle operator&(const ExprHandle& other) const;
+  ExprHandle operator|(const ExprHandle& other) const;
+  ExprHandle operator&&(const ExprHandle& other) const;
+  ExprHandle operator||(const ExprHandle& other) const;
+  ExprHandle operator^(const ExprHandle& other) const;
+  ExprHandle operator<<(const ExprHandle& other) const;
+  ExprHandle operator>>(const ExprHandle& other) const;
+
+ private:
+  ExprPtr base_expr_node_ = nullptr;
+};
+
+// The underlying representation node to a Var.
+// Currently, each Var object represents a unique variable, even though the
+// names might be the same. We should consider add a unique_name as well.
+class TORCH_API Var : public ExprNode<Var> {
+ public:
+  static ExprHandle make(const std::string& name_hint, Dtype dtype) {
+    return ExprHandle(alloc<Var>(name_hint, dtype));
+  }
+  static ExprHandle make(Dtype dtype) {
+    return ExprHandle(alloc<Var>("", dtype));
+  }
+
+  // TODO: unique_name
+  const std::string& name_hint() const {
+    return name_hint_;
+  }
+
+  void set_name_hint(const std::string& name) {
+    name_hint_ = name;
+  }
+
+  void set_name_hint(std::string&& name) {
+    name_hint_ = std::move(name);
+  }
+
+  Var(std::string name_hint, Dtype dtype)
+      : ExprNodeBase(dtype, kPrimitive), name_hint_(std::move(name_hint)) {}
+
+ private:
+  std::string name_hint_;
+};
+
+TORCH_API std::vector<ExprPtr> make_contiguous_strides(
+    const std::vector<ExprHandle>& dims);
+TORCH_API std::vector<ExprPtr> make_channels_last_strides(
+    const std::vector<ExprHandle>& dims);
+
+class TORCH_API Buf : public ExprNode<Buf> {
+ public:
+  static BufHandle make(const std::vector<ExprHandle>& dims, Dtype dtype);
+
+  static BufHandle make(
+      const std::string& name_hint,
+      const std::vector<ExprHandle>& dims,
+      const std::vector<ExprHandle>& strides,
+      Dtype dtype);
+
+  static BufHandle make(
+      const std::string& name_hint,
+      const std::vector<ExprHandle>& dims,
+      Dtype dtype,
+      c10::optional<ExprHandle> initializer = c10::nullopt,
+      c10::optional<std::vector<ExprHandle>> strides = c10::nullopt,
+      c10::optional<ExprHandle> qscale = c10::nullopt,
+      c10::optional<ExprHandle> qzero = c10::nullopt);
+
+  // TODO: unique_name
+  VarPtr base_handle() const {
+    return base_handle_;
+  }
+  void set_base_handle(VarPtr base_handle) {
+    base_handle_ = std::move(base_handle);
+  }
+
+  const std::string& name_hint() const {
+    return base_handle_->name_hint();
+  }
+  void set_name_hint(const std::string& name_hint) {
+    base_handle_->set_name_hint(name_hint);
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Buf(const std::string& name_hint,
+      const std::vector<ExprPtr>& dims,
+      Dtype dtype,
+      ExprPtr initializer = nullptr,
+      c10::optional<std::vector<ExprPtr>> strides = c10::nullopt,
+      ExprPtr qscale = nullptr,
+      ExprPtr qzero = nullptr)
+      : Buf(alloc<Var>(name_hint, kHandle),
+            dims,
+            dtype,
+            std::move(initializer),
+            std::move(strides),
+            std::move(qscale),
+            std::move(qzero)) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Buf(VarPtr var,
+      std::vector<ExprPtr> dims,
+      Dtype dtype,
+      ExprPtr initializer = nullptr,
+      c10::optional<std::vector<ExprPtr>> strides = c10::nullopt,
+      ExprPtr qscale = nullptr,
+      ExprPtr qzero = nullptr);
+
+  size_t ndim() const {
+    return dims_.size();
+  }
+  ExprPtr dim(size_t index) const {
+    if (index >= ndim()) {
+      throw out_of_range_index();
+    }
+    return dims_[index];
+  }
+  std::vector<ExprPtr> dims() const {
+    return dims_;
+  }
+  void set_dims(std::vector<ExprPtr> dims) {
+    dims_ = std::move(dims);
+  }
+
+  std::vector<ExprPtr> strides() const {
+    return strides_;
+  }
+
+  void set_strides(std::vector<ExprPtr> strides) {
+    strides_ = std::move(strides);
+  }
+
+  ExprPtr initializer() const {
+    return initializer_;
+  };
+
+  ExprPtr qzero() const {
+    return qzero_;
+  }
+
+  ExprPtr qscale() const {
+    return qscale_;
+  }
+
+  void set_qzero(ExprPtr qzero) {
+    qzero_ = std::move(qzero);
+  }
+
+  void set_qscale(ExprPtr qscale) {
+    qscale_ = std::move(qscale);
+  }
+
+  bool hasConstantDims() const {
+    for (const auto& d : dims_) {
+      if (!d->isConstant()) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  bool is_contiguous(
+      at::MemoryFormat memory_format = at::MemoryFormat::Contiguous) const;
+
+  // The channels-last 1d can benefit the performance of some operators like
+  // conv1d. But the MemoryFormat enum has not covered this layout yet. Hence,
+  // we abstract a dedicated function to check channels-last 1d contiguous.
+  //
+  // Channels-last 1d:
+  //   dims:              n   c    l
+  //   strides(nlc):    c*l   1    c
+  bool is_channels_last_1d_contiguous() const {
+    if (dims_.size() != 3) {
+      return false;
+    }
+    return is_stride_one(1) && is_cont_with(2, 1) && is_cont_with(0, 2);
+  }
+
+ private:
+  bool is_cont_with(int cur_dim, int adjacent_dim) const;
+  bool is_stride_one(int cur_dim) const;
+
+  VarPtr base_handle_;
+  std::vector<ExprPtr> dims_;
+  std::vector<ExprPtr> strides_;
+  ExprPtr initializer_;
+  // qscale_ and qzero_ are used only for quantized dtypes Bufs: kQUInt8, kQInt8
+  ExprPtr qscale_;
+  ExprPtr qzero_;
+};
+
+class TORCH_API BufHandle : public ExprHandle {
+ public:
+  BufHandle(
+      const std::string& name_hint,
+      const std::vector<ExprHandle>& dims,
+      Dtype dtype)
+      : ExprHandle(Buf::make(name_hint, dims, dtype)) {}
+
+  BufHandle(
+      const std::string& name_hint,
+      const std::vector<ExprHandle>& dims,
+      const std::vector<ExprHandle>& strides,
+      Dtype dtype)
+      : ExprHandle(Buf::make(name_hint, dims, strides, dtype)) {}
+
+  BufHandle(const std::vector<ExprHandle>& dims, Dtype dtype)
+      : ExprHandle(Buf::make("_", dims, dtype)) {}
+
+  explicit BufHandle(Dtype dtype) : ExprHandle(Buf::make("_", {}, dtype)) {}
+
+  explicit BufHandle(BufPtr node) : ExprHandle(std::move(node)) {}
+  BufPtr node() const {
+    return static_to<Buf>(ExprHandle::node());
+  }
+  BufPtr node() {
+    return static_to<Buf>(ExprHandle::node());
+  }
+
+  template <typename... Ts>
+  inline ExprHandle load(const Ts&... ts) const;
+
+  template <typename T>
+  inline ExprHandle load(const std::vector<T>& args) const;
+
+  inline ExprHandle load(const std::vector<ExprHandle>& args) const;
+
+  StorePtr store(const std::vector<ExprHandle>& args, const ExprHandle& val)
+      const;
+
+  bool operator==(const BufHandle& other) const {
+    return this->node() == other.node();
+  }
+  bool operator!=(const BufHandle& other) const {
+    return !(*this == other);
+  }
+
+  const std::string& name_hint() const {
+    return this->node()->name_hint();
+  }
+
+  bool empty() const {
+    return (this->node() == nullptr);
+  }
+
+  size_t ndim() const {
+    return node()->ndim();
+  }
+
+  std::vector<ExprHandle> dims() const;
+
+  ExprHandle dim(size_t index) const {
+    return ExprHandle(node()->dim(index));
+  }
+
+  bool is_contiguous(
+      at::MemoryFormat memory_format = at::MemoryFormat::Contiguous) const {
+    return node()->is_contiguous(memory_format);
+  }
+
+  bool is_channels_last_1d_contiguous() const {
+    return node()->is_channels_last_1d_contiguous();
+  }
+};
+
+// An expression to construct the underlying variable node.
+// Note: do not store any info here, since it is often possible to slice this
+// object. For example: VarHandle x('x'); ExprHandle x2 = x;
+class TORCH_API VarHandle : public ExprHandle {
+ public:
+  // Creates an empty VarHandle whose base Var is set to nullptr.
+  VarHandle() : ExprHandle() {}
+
+  explicit VarHandle(Dtype dtype) : ExprHandle(Var::make(dtype)) {}
+
+  VarHandle(const std::string& name_hint, Dtype dtype)
+      : ExprHandle(Var::make(name_hint, dtype)) {}
+
+  explicit VarHandle(VarPtr node) : ExprHandle(std::move(node)) {}
+
+  VarPtr node() const {
+    return static_to<Var>(ExprHandle::node());
+  }
+  bool operator==(const VarHandle& other) const {
+    return this->node() == other.node();
+  }
+  bool operator!=(const VarHandle& other) const {
+    return !(*this == other);
+  }
+
+  const std::string& name_hint() const {
+    return this->node()->name_hint();
+  }
+  bool empty() const {
+    return (this->node() == nullptr);
+  }
+};
+
+template <class Op, class Base>
+ExprPtr ExprNode<Op, Base>::accept_mutator(IRMutator* mutator) {
+  return mutator->mutate(static_to<Op>(Base::getptr()));
+}
+
+inline bool same_node(const ExprHandle& expr1, const ExprHandle& expr2) {
+  return expr1.AsNode<Expr>() == expr2.AsNode<Expr>();
+}
+
+TORCH_API ExprHandle sin(const ExprHandle& v);
+TORCH_API ExprHandle cos(const ExprHandle& v);
+TORCH_API ExprHandle tan(const ExprHandle& v);
+TORCH_API ExprHandle asin(const ExprHandle& v);
+TORCH_API ExprHandle acos(const ExprHandle& v);
+TORCH_API ExprHandle atan(const ExprHandle& v);
+TORCH_API ExprHandle sinh(const ExprHandle& v);
+TORCH_API ExprHandle cosh(const ExprHandle& v);
+TORCH_API ExprHandle tanh(const ExprHandle& v);
+TORCH_API ExprHandle sigmoid(const ExprHandle& v);
+TORCH_API ExprHandle exp(const ExprHandle& v);
+TORCH_API ExprHandle expm1(const ExprHandle& v);
+TORCH_API ExprHandle abs(const ExprHandle& v);
+TORCH_API ExprHandle log(const ExprHandle& v);
+TORCH_API ExprHandle fast_tanh(const ExprHandle& v);
+TORCH_API ExprHandle fast_sigmoid(const ExprHandle& v);
+TORCH_API ExprHandle fast_log(const ExprHandle& v);
+TORCH_API ExprHandle log_vml(const ExprHandle& v);
+TORCH_API ExprHandle log2(const ExprHandle& v);
+TORCH_API ExprHandle log10(const ExprHandle& v);
+TORCH_API ExprHandle log1p(const ExprHandle& v);
+TORCH_API ExprHandle erf(const ExprHandle& v);
+TORCH_API ExprHandle erfc(const ExprHandle& v);
+TORCH_API ExprHandle sqrt(const ExprHandle& v);
+TORCH_API ExprHandle rsqrt(const ExprHandle& v);
+TORCH_API ExprHandle ceil(const ExprHandle& v);
+TORCH_API ExprHandle floor(const ExprHandle& v);
+TORCH_API ExprHandle round(const ExprHandle& v);
+TORCH_API ExprHandle trunc(const ExprHandle& v);
+TORCH_API ExprHandle frac(const ExprHandle& v);
+TORCH_API ExprHandle lgamma(const ExprHandle& v);
+TORCH_API ExprHandle atan2(const ExprHandle& v1, const ExprHandle& v2);
+TORCH_API ExprHandle pow(const ExprHandle& v1, const ExprHandle& v2);
+TORCH_API ExprHandle fmod(const ExprHandle& v1, const ExprHandle& v2);
+TORCH_API ExprHandle remainder(const ExprHandle& v1, const ExprHandle& v2);
+TORCH_API ExprHandle isnan(const ExprHandle& v1);
+TORCH_API ExprHandle Relu(const ExprHandle& v1);
+
+TORCH_API ExprHandle
+ifThenElse(const ExprHandle& c, const ExprHandle& t, const ExprHandle& f);
+
+TORCH_API ExprHandle expr_to_vec(ExprHandle v, int lanes);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/external_functions.h

@@ -0,0 +1,115 @@
+#pragma once
+
+#include <ATen/Config.h>
+#include <ATen/Functions.h>
+#include <c10/macros/Macros.h>
+#include <torch/csrc/Export.h>
+#include <cstdint>
+#include <vector>
+
+#define FOR_ALL_EXTERNAL_FUNCTIONS(_)   \
+  _(nnc_aten_adaptive_avg_pool2d)       \
+  _(nnc_aten_addmm)                     \
+  _(nnc_aten_conv2d)                    \
+  _(nnc_aten_conv1d)                    \
+  _(nnc_aten_conv1d_out)                \
+  _(nnc_aten_dequantize)                \
+  _(nnc_aten_dequantize_out)            \
+  _(nnc_aten_embedding)                 \
+  _(nnc_aten_matmul)                    \
+  _(nnc_aten_mv)                        \
+  _(nnc_aten_mm)                        \
+  _(nnc_aten_mean)                      \
+  _(nnc_aten_max_red)                   \
+  _(nnc_aten_max_red_out)               \
+  _(nnc_aten_quantized_conv1d)          \
+  _(nnc_aten_quantized_conv1d_out)      \
+  _(nnc_aten_quantized_conv2d)          \
+  _(nnc_aten_quantized_conv2d_out)      \
+  _(nnc_aten_quantized_conv2d_relu)     \
+  _(nnc_aten_quantized_conv2d_relu_out) \
+  _(nnc_aten_quantized_linear)          \
+  _(nnc_aten_quantized_linear_out)      \
+  _(nnc_aten_quantized_linear_relu)     \
+  _(nnc_aten_quantized_add)             \
+  _(nnc_aten_quantized_cat)             \
+  _(nnc_aten_quantized_mul)             \
+  _(nnc_aten_quantized_mul_out)         \
+  _(nnc_aten_quantized_mul_scalar)      \
+  _(nnc_aten_quantized_mul_scalar_out)  \
+  _(nnc_aten_quantized_relu)            \
+  _(nnc_aten_quantized_sigmoid)         \
+  _(nnc_aten_quantized_sigmoid_out)     \
+  _(nnc_aten_quantize_per_tensor)       \
+  _(nnc_aten_quantize_per_tensor_out)   \
+  _(nnc_aten_triangular_solve)          \
+  _(nnc_aten_upsample_nearest2d)        \
+  _(nnc_aten_upsample_nearest2d_out)    \
+  _(nnc_prepacked_conv2d_clamp_run)     \
+  _(nnc_prepacked_linear_clamp_run)
+
+#define DECLARE_EXTERNAL_FUNCTION(NAME) \
+  TORCH_API void NAME(                  \
+      int64_t bufs_num,                 \
+      void** buf_data,                  \
+      int64_t* buf_ranks,               \
+      int64_t* buf_dims,                \
+      int64_t* buf_strides,             \
+      int8_t* buf_dtypes,               \
+      int64_t args_num,                 \
+      int64_t* extra_args);
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+struct QIData final {
+  double scale;
+  int64_t zero;
+  c10::ScalarType scalarType;
+};
+std::vector<at::Tensor> constructTensors(
+    int64_t bufs_num,
+    void** buf_data,
+    int64_t* buf_ranks,
+    int64_t* buf_dims,
+    int64_t* buf_strides,
+    int8_t* buf_dtypes,
+    c10::optional<std::vector<std::pair<size_t, QIData>>> qdataArg =
+        c10::nullopt);
+
+std::vector<at::Tensor> constructTensors2(
+    int64_t bufs_in_num,
+    void** buf_data,
+    int64_t* buf_ranks,
+    int64_t* buf_dims,
+    int64_t* buf_strides,
+    int8_t* buf_dtypes,
+    c10::optional<std::vector<std::pair<size_t, QIData>>> qdataArg =
+        c10::nullopt,
+    size_t bufs_out_num = 0);
+
+#ifdef C10_MOBILE
+extern "C" {
+#endif
+void DispatchParallel(
+    int8_t* func,
+    int64_t start,
+    int64_t stop,
+    int8_t* packed_data) noexcept;
+
+FOR_ALL_EXTERNAL_FUNCTIONS(DECLARE_EXTERNAL_FUNCTION)
+#if AT_MKLDNN_ENABLED()
+DECLARE_EXTERNAL_FUNCTION(nnc_mkldnn_prepacked_conv_run);
+#endif
+
+TORCH_API void nnc_aten_free(int64_t bufs_num, void** ptrs) noexcept;
+
+#ifdef C10_MOBILE
+} // extern "C"
+#endif
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+
+#undef DECLARE_EXTERNAL_FUNCTION

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/external_functions_core.h

@@ -0,0 +1,29 @@
+#pragma once
+
+#include <ATen/ATen.h>
+#include <ATen/Parallel.h>
+#include <torch/csrc/Export.h>
+#include <cstdint>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+#ifdef C10_MOBILE
+extern "C" {
+#endif
+void DispatchParallel(
+    int8_t* func,
+    int64_t start,
+    int64_t stop,
+    int8_t* packed_data) noexcept;
+
+TORCH_API void nnc_aten_free(int64_t bufs_num, void** ptrs) noexcept;
+
+#ifdef C10_MOBILE
+} // extern "C"
+#endif
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/external_functions_registry.h

@@ -0,0 +1,61 @@
+#pragma once
+
+#include <torch/csrc/Export.h>
+#include <cstdint>
+#include <string>
+#include <unordered_map>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// The external functions that could be called from NNC must have the same
+// signature defined by `NNCExternalFunction`.
+//
+// Why this signature?
+// It was picked for two reasons: 1) it should be generic enough to represent
+// most of the ops we might want to call, 2) it should be possible to generate a
+// code for this call in LLVM codegen.
+// The first 5 parameters allow to pass any number of contiguous CPU tensors in
+// case we need to run aten ops (TODO: support different devices). The first
+// buffer in the array is assumed to be the output buffer. We couldn't use
+// `at::Tensor` (or `c10::IValue`) type there directly as it would mean that
+// we'd need to declare it in LLVM codegen in LLVM IR form, which would be very
+// cumbersome and hard to maintain. Note that the dimensions of all tensors are
+// concatenated into a single array buf_dims. We do not need to pass its length,
+// since it can be deduced from total number of buffers and their ranks.
+//
+// The last 2 arguments allow to pass any non-tensor arguments encoded as an
+// array of int64_t values. The way they are encoded is not specified and could
+// be arbitrary - whatever the most convenient for the specific bridge function
+// is.
+//
+// The bridge functions must not throw exceptions - properly propagating them
+// from the generated code is too cumbersome, and thus all calls to functions
+// that could throw must be wrapped with try-catch blocks.
+using NNCExternalFunction = void (*)(
+    int64_t bufs_num,
+    void** buf_data,
+    int64_t* buf_ranks,
+    int64_t* buf_dims,
+    int64_t* buf_strides,
+    int8_t* buf_dtypes,
+    int64_t args_num,
+    int64_t* extra_args);
+
+// Return a global map "function-name" -> "function-pointer" for all registered
+// in NNC external functions
+TORCH_API std::unordered_map<std::string, NNCExternalFunction>&
+getNNCFunctionRegistry();
+
+// To register a new external function in NNC one needs to create an instance of
+// this struct
+struct RegisterNNCExternalFunction {
+  RegisterNNCExternalFunction(const std::string& name, NNCExternalFunction fn) {
+    getNNCFunctionRegistry()[name] = fn;
+  }
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/fwd_decls.h

@@ -0,0 +1,129 @@
+#pragma once
+#include <c10/core/ScalarType.h>
+#include <memory>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+template <typename Node>
+using NodePtr = std::shared_ptr<Node>;
+
+template <typename To, typename From>
+NodePtr<To> to(NodePtr<From> x) {
+  return std::dynamic_pointer_cast<To>(x);
+}
+
+template <typename To, typename From>
+NodePtr<To> static_to(NodePtr<From> x) {
+  return std::static_pointer_cast<To>(x);
+}
+
+template <typename Node, typename... Args>
+NodePtr<Node> alloc(Args&&... args) {
+  return std::make_shared<Node>(std::forward<Args>(args)...);
+}
+
+class Buf;
+class Expr;
+class Stmt;
+class Var;
+
+using BufPtr = NodePtr<Buf>;
+using ExprPtr = NodePtr<Expr>;
+using StmtPtr = NodePtr<Stmt>;
+using VarPtr = NodePtr<Var>;
+
+class ExprHandle;
+class VarHandle;
+class BufHandle;
+
+class Add;
+class And;
+class BitCast;
+class Broadcast;
+class Cast;
+class CompareSelect;
+class Div;
+class IfThenElse;
+class Intrinsics;
+class Let;
+class Load;
+class Lshift;
+class Max;
+class MaxTerm;
+class Min;
+class MinTerm;
+class Mod;
+class Mul;
+class Or;
+class Polynomial;
+class Ramp;
+class ReduceOp;
+class RoundOff;
+class Rshift;
+class Store;
+class Sub;
+class Term;
+class Xor;
+using AddPtr = NodePtr<Add>;
+using AndPtr = NodePtr<And>;
+using BitCastPtr = NodePtr<BitCast>;
+using BroadcastPtr = NodePtr<Broadcast>;
+using CastPtr = NodePtr<Cast>;
+using CompareSelectPtr = NodePtr<CompareSelect>;
+using DivPtr = NodePtr<Div>;
+using IfThenElsePtr = NodePtr<IfThenElse>;
+using IntrinsicsPtr = NodePtr<Intrinsics>;
+using LetPtr = NodePtr<Let>;
+using LoadPtr = NodePtr<Load>;
+using LshiftPtr = NodePtr<Lshift>;
+using MaxPtr = NodePtr<Max>;
+using MaxTermPtr = NodePtr<MaxTerm>;
+using MinPtr = NodePtr<Min>;
+using MinTermPtr = NodePtr<MinTerm>;
+using ModPtr = NodePtr<Mod>;
+using MulPtr = NodePtr<Mul>;
+using OrPtr = NodePtr<Or>;
+using PolynomialPtr = NodePtr<Polynomial>;
+using RampPtr = NodePtr<Ramp>;
+using ReduceOpPtr = NodePtr<ReduceOp>;
+using RoundOffPtr = NodePtr<RoundOff>;
+using RshiftPtr = NodePtr<Rshift>;
+using StorePtr = NodePtr<Store>;
+using SubPtr = NodePtr<Sub>;
+using TermPtr = NodePtr<Term>;
+using XorPtr = NodePtr<Xor>;
+
+class Allocate;
+class AtomicAdd;
+class Block;
+class Cond;
+class ExternalCall;
+class ExternalCallWithAlloc;
+class For;
+class Free;
+class FreeExt;
+class PlacementAllocate;
+class SyncThreads;
+using AllocatePtr = NodePtr<Allocate>;
+using AtomicAddPtr = NodePtr<AtomicAdd>;
+using BlockPtr = NodePtr<Block>;
+using CondPtr = NodePtr<Cond>;
+using ExternalCallPtr = NodePtr<ExternalCall>;
+using ExternalCallWithAllocPtr = NodePtr<ExternalCallWithAlloc>;
+using ForPtr = NodePtr<For>;
+using FreePtr = NodePtr<Free>;
+using FreeExtPtr = NodePtr<FreeExt>;
+using PlacementAllocatePtr = NodePtr<PlacementAllocate>;
+using SyncThreadsPtr = NodePtr<SyncThreads>;
+
+#define IMM_DECLARE(Type, Name) \
+  class Name##Imm;              \
+  using Name##ImmPtr = NodePtr<Name##Imm>;
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_DECLARE);
+#undef IMM_DECLARE
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/graph_opt.h

@@ -0,0 +1,115 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// Optimize aten::cat ops in the given subgraph.
+//
+// Moving users of cat to its inputs.
+//    Cat ops get lowered into multiple loops, one per input. When the result
+//    of cat is used by some other op, it results in a situation where inlining
+//    of cat does not happen. This in turn results in intermediate buffers
+//    being created for the result of cat, since it is not inlined.
+//
+//    For example, consider the following graph:
+//       graph(%x : Float(10, strides=[1], device=cpu),
+//             %y : Float(20, strides=[1], device=cpu)):
+//         %dim : int = prim::Constant[value=0]()
+//         %xy_list : Tensor[] = prim::ListConstruct(%x, %y)
+//         %cat : Float(60, strides=[1], device=cpu) = aten::cat(%xy_list, %dim)
+//         %5 : Float(60, strides=[1], device=cpu) = aten::log(%cat)
+//         return (%5))IR";
+//
+//     This will get lowered into:
+//         Allocate(aten_cat);
+//         for (...)
+//           aten_cat[...] = x[...]
+//         for (...)
+//           aten_cat[...] = y[...]
+//         for (...)
+//           aten_log[...] = log(aten_cat[...])
+//         Free(aten_cat);
+//     Note that aten_cat is not inlined into aten_log and it results in
+//     an intermediate buffer allocation as well.
+//
+//     Optimization:
+//        We move the ops that use the result of `cat` into its inputs whenever
+//     possible.
+//
+//     The graph above will be transformed to:
+//        graph(%x : Float(10, strides=[1], device=cpu),
+//              %y : Float(20, strides=[1], device=cpu)):
+//          %3 : int = prim::Constant[value=0]()
+//          %7 : Float(10, strides=[1], device=cpu) = aten::log(%x)
+//          %8 : Float(20, strides=[1], device=cpu) = aten::log(%y)
+//          %9 : Tensor[] = prim::ListConstruct(%7, %8)
+//          %10 : Float(60, strides=[1], device=cpu) = aten::cat(%9, %3)
+//          return (%10)
+//
+//     This will get lowered into:
+//         for (...)
+//           aten_cat[...] = log(x[...])
+//         for (...)
+//           aten_cat[...] = log(y[...])
+//     aten_cat is the output buffer here.
+
+bool OptimizeCat(const std::shared_ptr<Graph>& graph);
+
+TORCH_API void annotateInputShapes(
+    const std::shared_ptr<Graph>& graph,
+    const std::vector<c10::optional<at::Tensor>>& example_inputs);
+TORCH_API std::shared_ptr<Graph> removeUnusedSelfArgument(
+    const std::shared_ptr<Graph>& graph);
+TORCH_API std::shared_ptr<Graph> removeGraphOutput(
+    const std::shared_ptr<Graph>& graph,
+    size_t idx);
+TORCH_API std::shared_ptr<Graph> replaceListOutputWithTuple(
+    const std::shared_ptr<Graph>& graph);
+
+// Perform \p ITERS rounds of "trimming" for the given \p GRAPH.
+//
+// Trimming means that we try to remove a small portion of the graph while
+// keeping it valid. This is useful for debugging when we try to find a minimal
+// example reproducing the issue at hand. When ITERS is 0, the graph remains
+// unchanged, when ITERS is a big number, the graph usually becomes empty.
+TORCH_API std::shared_ptr<Graph> trimGraph(
+    const std::shared_ptr<Graph>& graph,
+    int64_t iters);
+
+// Scan all values in the given graph and replace each dimension with a size Xi
+// present in \p SIZES with a symbolic shape Yi. Return a vector of symbol
+// values [Y0, Y1, .., Yn].
+//
+// For example:
+// Input:
+// graph(%x : Float(10, 20, 30, 40)):
+//   %y : Float(10, 20, 30, 40) = aten::relu(%x)
+//   return %y
+//
+// If we run makeShapesSymbolic(graph, {20, 40}), then we'll get:
+//
+// graph(%x : Float(10, SS(-3), 30, SS(-5))):
+//   %y : Float(10, SS(-3), 30, SS(-5)) = aten::relu(%x)
+//   return %y
+//
+// and get {-3, -5} as the return value.
+TORCH_API std::vector<int64_t> makeShapesSymbolic(
+    std::shared_ptr<Graph>& graph,
+    const std::vector<int64_t>& sizes);
+
+// Inspect the graph and report whether it can be converted to TE IR.
+// TODO: add error reporting for graphs that can't be converted.
+TORCH_API bool isGraphCompilable(const std::shared_ptr<Graph>& graph);
+
+// Examine the graph and (hackily) fill in missing tensor type info, such as
+// scalar type, device, and strides. Ideally, this should be done by a proper
+// dtype/device/shape propagation passes, but until they are ready we can use
+// this, not always correct, workaround pass.
+TORCH_API void fixupMissingShapeInfo(const std::shared_ptr<Graph>& graph);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/half_support.h

@@ -0,0 +1,217 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// Walk the Statement looking for Half size loads/stores.
+class HalfChecker : public IRVisitor {
+ public:
+  HalfChecker(const std::vector<CodeGen::BufferArg>& args) {
+    for (const auto& BA : args) {
+      hasHalf_ |= BA.dtype().scalar_type() == ScalarType::Half;
+    }
+  }
+
+  bool hasHalf() const {
+    return hasHalf_;
+  }
+
+  bool hasBFloat16() const {
+    return hasBFloat16_;
+  }
+
+  void visit(LoadPtr v) override {
+    hasHalf_ |= v->dtype().scalar_type() == ScalarType::Half;
+    hasBFloat16_ |= v->dtype().scalar_type() == ScalarType::BFloat16;
+    IRVisitor::visit(v);
+  }
+
+  void visit(StorePtr v) override {
+    hasHalf_ |= v->buf()->dtype().scalar_type() == ScalarType::Half;
+    hasBFloat16_ |= v->buf()->dtype().scalar_type() == ScalarType::BFloat16;
+    IRVisitor::visit(v);
+  }
+
+  void visit(HalfImmPtr v) override {
+    hasHalf_ = true;
+  }
+
+  void visit(BFloat16ImmPtr v) override {
+    hasBFloat16_ = true;
+  }
+
+  void visit(CastPtr v) override {
+    hasHalf_ |= v->dtype().scalar_type() == ScalarType::Half;
+    hasBFloat16_ |= v->dtype().scalar_type() == ScalarType::BFloat16;
+    IRVisitor::visit(v);
+  }
+
+ private:
+  bool hasHalf_{false};
+  bool hasBFloat16_{false};
+};
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+class HalfRewriter : public IRMutator {
+  ExprPtr mutate(LoadPtr v) override {
+    ExprPtr child = IRMutator::mutate(v);
+    if (!isHalf(child)) {
+      return child;
+    }
+
+    ExprPtr ret = alloc<Cast>(
+        child->dtype().cloneWithScalarType(ScalarType::Float), child);
+
+    inserted_half_casts_.insert(ret);
+    return ret;
+  }
+
+  StmtPtr mutate(StorePtr v) override {
+    // Since mutation changes the `value()` expression in-place, we need to
+    // get the dtype of the `value()` before that is mutated.
+    auto newType = v->value()->dtype();
+    ExprPtr new_val = v->value()->accept_mutator(this);
+    auto bufType = v->buf()->dtype();
+
+    if (isHalf(newType.scalar_type())) {
+      new_val = alloc<Cast>(newType, new_val);
+      inserted_half_casts_.insert(new_val);
+    }
+
+    // The scalar_type of value is not Half while the buf is Half
+    if (!isHalf(newType.scalar_type()) && isHalf(bufType.scalar_type())) {
+      new_val = alloc<Cast>(
+          newType.cloneWithScalarType(bufType.scalar_type()), new_val);
+      inserted_half_casts_.insert(new_val);
+    }
+
+    v->set_value(new_val);
+    return v;
+  }
+
+  ExprPtr mutate(HalfImmPtr v) override {
+    return alloc<Cast>(kFloat, v);
+  }
+
+  ExprPtr mutate(BFloat16ImmPtr v) override {
+    return alloc<Cast>(kFloat, v);
+  }
+
+  ExprPtr mutate(CastPtr v) override {
+    ExprPtr child = v->src_value()->accept_mutator(this);
+
+    // just don't allow half casts we didn't insert.
+    if (isHalf(v)) {
+      if (inserted_half_casts_.count(v) < 1) {
+        v->set_src_value(child);
+        v->set_dtype(v->dtype().cloneWithScalarType(c10::kFloat));
+        return v;
+      }
+    }
+
+    // Remove Half(Float()) and friends.
+    CastPtr cast_child = to<Cast>(child);
+    if (cast_child) {
+      auto cast_to_double = v->dtype().scalar_type() == ScalarType::Double;
+      auto from_half = isHalf(cast_child->src_value());
+      // Cannot simplify the double(float(half)) to double(half) as NNC does
+      // not support cast BF16 to double directly.
+      auto not_cast_half_to_doulbe = !(cast_to_double && from_half);
+      if (v->dtype().is_floating_point() &&
+          cast_child->dtype().is_floating_point() && not_cast_half_to_doulbe) {
+        return alloc<Cast>(v->dtype(), cast_child->src_value());
+      }
+    }
+
+    if (child == v->src_value()) {
+      return v;
+    }
+
+    return alloc<Cast>(v->dtype(), child);
+  }
+
+  StmtPtr mutate(LetPtr v) override {
+    if (isHalf(v->var()->dtype().scalar_type())) {
+      VarPtr load_new_var = alloc<Var>(v->var()->name_hint(), kFloat);
+      ExprPtr new_value = alloc<Cast>(
+          v->var()->dtype().cloneWithScalarType(ScalarType::Float),
+          v->value()->accept_mutator(this));
+      var_map[v->var()] = load_new_var;
+
+      return alloc<Let>(load_new_var, new_value);
+    }
+
+    return IRMutator::mutate(v);
+  }
+
+  ExprPtr mutate(VarPtr v) override {
+    auto it = var_map.find(v);
+    if (it != var_map.end()) {
+      return it->second;
+    }
+
+    return v;
+  }
+
+  template <typename T>
+  ExprPtr mutateArithmetic(T v) {
+    IRMutator::mutate(v);
+    if (isHalf(v)) {
+      v->set_dtype(v->dtype().cloneWithScalarType(c10::kFloat));
+    }
+    return v;
+  }
+
+  ExprPtr mutate(AddPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(SubPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(MulPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(DivPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(MaxPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(MinPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(CompareSelectPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(BroadcastPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(IfThenElsePtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(IntrinsicsPtr v) override {
+    return mutateArithmetic(v);
+  }
+
+ private:
+  static bool isHalf(ScalarType st) {
+    return st == ScalarType::Half || st == ScalarType::BFloat16;
+  }
+
+  static bool isHalf(ExprPtr v) {
+    return isHalf(v->dtype().scalar_type());
+  }
+
+  std::unordered_set<ExprPtr> inserted_half_casts_;
+  std::unordered_map<VarPtr, VarPtr> var_map;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/hash_provider.h

@@ -0,0 +1,304 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+#include <utility>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+struct TORCH_API SimplifierHashType {
+  SimplifierHashType() = default;
+  explicit SimplifierHashType(size_t s) : _h(s) {}
+
+  bool operator==(const SimplifierHashType& other) const;
+  bool operator!=(const SimplifierHashType& other) const;
+  bool operator<(const SimplifierHashType& other) const;
+  bool operator==(const size_t other) const;
+  bool operator!=(const size_t other) const;
+
+  size_t _h{0};
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+
+namespace std {
+template <>
+struct hash<torch::jit::tensorexpr::SimplifierHashType> {
+  size_t operator()(const torch::jit::tensorexpr::SimplifierHashType& k) const {
+    return k._h;
+  }
+};
+
+} // namespace std
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+#define CACHE_GUARD()  \
+  if (cachedHash(v)) { \
+    return;            \
+  }
+
+class Term;
+class Polynomial;
+
+/* Expression hasher providing comparable values representing sub-exprs.
+ * Uses memoization to avoid excessive recursion. */
+class TORCH_API HashProvider : public IRVisitor {
+ public:
+  template <class T>
+  SimplifierHashType hash(T e) {
+    // NOLINTNEXTLINE(clang-analyzer-core.CallAndMessage)
+    e->accept(this);
+    return hashOf(e);
+  }
+
+  bool cachedHash(ExprPtr e) {
+    return exprToHash_.find(e) != exprToHash_.end();
+  }
+  bool cachedHash(StmtPtr s) {
+    return stmtToHash_.find(s) != stmtToHash_.end();
+  }
+
+  void clearCache() {
+    exprToHash_.clear();
+    stmtToHash_.clear();
+  }
+
+  void visit(AddPtr v) override;
+  void visit(SubPtr v) override;
+  void visit(MulPtr v) override;
+  void visit(DivPtr v) override;
+  void visit(ModPtr v) override;
+  void visit(RoundOffPtr v) override;
+  void visit(MaxPtr v) override;
+  void visit(MinPtr v) override;
+  void visit(AndPtr v) override;
+  void visit(OrPtr v) override;
+  void visit(XorPtr v) override;
+  void visit(LshiftPtr v) override;
+  void visit(RshiftPtr v) override;
+  void visit(CompareSelectPtr v) override;
+
+// NOLINTNEXTLINE
+#define IMM_VISIT(Type, Name)                    \
+  void visit(Name##ImmPtr v) override {          \
+    CACHE_GUARD();                               \
+    putHash(v, hash_combine(#Name, v->value())); \
+  }
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_VISIT);
+#undef IMM_VISIT
+
+  void visit(CastPtr v) override;
+  void visit(VarPtr v) override;
+  void visit(RampPtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(StorePtr v) override;
+  void visit(BlockPtr v) override;
+  void visit(ForPtr v) override;
+  void visit(BroadcastPtr v) override;
+  void visit(IfThenElsePtr v) override;
+  void visit(IntrinsicsPtr v) override;
+  void visit(AllocatePtr v) override;
+  void visit(FreePtr v) override;
+  void visit(CondPtr v) override;
+  void visit(TermPtr v) override;
+  void visit(PolynomialPtr v) override;
+  void visit(MaxTermPtr v) override;
+  void visit(MinTermPtr v) override;
+
+  template <typename... Types>
+  SimplifierHashType hash_combine(const Types&... args) {
+    SimplifierHashType seed;
+    _hash_combine(seed, args...);
+    return seed;
+  }
+
+ private:
+  SimplifierHashType hashOf(ExprPtr e) {
+    auto it = exprToHash_.find(e);
+    if (it != exprToHash_.end()) {
+      return it->second;
+    }
+
+    // As a failsafe fall back to IRPrinter.
+    std::stringstream ss;
+    IRPrinter printer(ss);
+    e->accept(&printer);
+    SimplifierHashType hash = SimplifierHashType(te_hash(ss.str()));
+    putHash(std::move(e), hash);
+
+    return hash;
+  }
+
+  SimplifierHashType hashOf(StmtPtr s) {
+    auto it = stmtToHash_.find(s);
+    if (it != stmtToHash_.end()) {
+      return it->second;
+    }
+
+    // As a failsafe fall back to IRPrinter.
+    std::stringstream ss;
+    IRPrinter printer(ss);
+    s->accept(&printer);
+    SimplifierHashType hash = SimplifierHashType(te_hash(ss.str()));
+    putHash(std::move(s), hash);
+
+    return hash;
+  }
+
+  // Hash funcs for various types, numbers are random.
+  template <typename T>
+  void _hash_combine(SimplifierHashType& seed, const T& val) {
+    seed._h ^= te_hash(val) + 0x1f752c19 + (seed._h << 7) + (seed._h >> 4);
+  }
+
+  void _hash_combine(SimplifierHashType& seed, const char* val) {
+    seed._h ^= te_hash(val) + 0x1f752c19 + (seed._h << 7) + (seed._h >> 4);
+  }
+
+  // at:::Half doesn't have a prime_number_hash, so cast to short.
+  void _hash_combine(SimplifierHashType& seed, const at::Half& val) {
+    seed._h ^=
+        te_hash((uint16_t)val) + 0x1f752c19 + (seed._h << 7) + (seed._h >> 4);
+  }
+
+  void _hash_combine(SimplifierHashType& seed, const Dtype& val) {
+    seed._h ^= te_hash(val.ToCppString()) + 0x1f752c19 + (seed._h << 7) +
+        (seed._h >> 4);
+  }
+
+  void _hash_combine(SimplifierHashType& seed, ExprPtr e) {
+    _hash_combine(seed, hash(std::move(e)));
+  }
+
+  template <typename T, typename... Types>
+  void _hash_combine(
+      SimplifierHashType& seed,
+      const T& val,
+      const Types&... args) {
+    _hash_combine(seed, val);
+    _hash_combine(seed, args...);
+  }
+
+  void putHash(ExprPtr e, SimplifierHashType h) {
+    auto res = exprToHash_.emplace(e, h);
+    if (res.second == false) {
+      // This is always a logic bug since we should check the cache first.
+      throw std::runtime_error("hash collision");
+    }
+  }
+  void putHash(StmtPtr s, SimplifierHashType h) {
+    auto res = stmtToHash_.emplace(s, h);
+    if (res.second == false) {
+      // This is always a logic bug since we should check the cache first.
+      throw std::runtime_error("hash collision");
+    }
+  }
+
+  std::unordered_map<ExprPtr, SimplifierHashType> exprToHash_;
+  std::unordered_map<StmtPtr, SimplifierHashType> stmtToHash_;
+  UniqueNameManager name_manager_;
+
+  size_t te_hash(SimplifierHashType val) {
+    return val._h;
+  }
+
+  size_t te_hash(int64_t val) {
+    // put the thing down.
+    size_t h = val ^ 0x647AA4D20C0B;
+    // bit flip it.
+    size_t h2 = ~h;
+    // and reverse byte order.
+    size_t h3 = 0;
+    for (unsigned int i = 0; i < 64; i += 8) {
+      h3 |= ((h2 >> i) & 0xFF) << (64 - i - 8);
+    }
+    return h3;
+  }
+
+  size_t te_hash(int32_t val) {
+    int64_t v2 = val;
+    return te_hash(v2);
+  }
+
+  size_t te_hash(uint32_t val) {
+    int64_t v2 = val;
+    return te_hash(v2);
+  }
+
+  size_t te_hash(uint64_t val) {
+    int64_t v2 = val;
+    return te_hash(v2);
+  }
+
+  size_t te_hash(int16_t val) {
+    int64_t v2 = val;
+    return te_hash(v2);
+  }
+
+  size_t te_hash(std::string val) {
+    size_t hash{0};
+    int64_t intval{0};
+    int64_t s = val.size() - 1;
+    while (s >= 0) {
+      for (unsigned int i = 0; i < 8; ++i) {
+        if (s < 0)
+          break;
+        // NOLINTNEXTLINE(bugprone-signed-char-misuse)
+        int64_t c = val.data()[s];
+        intval |= (c << (i * 8));
+
+        s--;
+      }
+      hash ^= te_hash(intval);
+      intval = 0;
+    }
+
+    return hash;
+  }
+
+  size_t te_hash(double d) {
+    // memcpy as type punning. Should be optimized out.
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    int64_t n;
+    std::memcpy(&n, &d, sizeof d);
+    return te_hash(n);
+  }
+
+  size_t te_hash(float d) {
+    // memcpy as type punning. Should be optimized out.
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    int32_t n;
+    std::memcpy(&n, &d, sizeof d);
+    return te_hash(n);
+  }
+
+  size_t te_hash(at::Half d) {
+    // memcpy as type punning. Should be optimized out.
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    int16_t n;
+    std::memcpy(&n, &d, sizeof d);
+    return te_hash(n);
+  }
+
+  size_t te_hash(at::BFloat16 d) {
+    // memcpy as type punning. Should be optimized out.
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    int16_t n;
+    std::memcpy(&n, &d, sizeof d);
+    return te_hash(n);
+  }
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/intrinsic_symbols.h

@@ -0,0 +1,22 @@
+#pragma once
+
+#ifdef TORCH_ENABLE_LLVM
+#include <c10/util/ArrayRef.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+struct SymbolAddress {
+  const char* symbol;
+  void* address;
+
+  SymbolAddress(const char* sym, void* addr) : symbol(sym), address(addr) {}
+};
+
+c10::ArrayRef<SymbolAddress> getIntrinsicSymbols();
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+#endif // TORCH_ENABLE_LLVM

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir.h

@@ -0,0 +1,934 @@
+#pragma once
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include <c10/util/string_utils.h>
+#include <torch/csrc/jit/tensorexpr/exceptions.h>
+#include <torch/csrc/jit/tensorexpr/expr.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+#include <torch/csrc/jit/tensorexpr/stmt.h>
+
+#include <ATen/core/ivalue.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+enum CompareSelectOperation {
+  kEQ = 0,
+  kGT,
+  kGE,
+  kLT,
+  kLE,
+  kNE,
+};
+
+enum CompareSelectBias {
+  kUnbiased,
+  kLikely,
+  kUnlikely,
+};
+
+inline int getPrecedence(IRNodeType ty) {
+  // Match C++ operator precedence rules, since some pretty-print expressions to
+  // C++. SEE: https://en.cppreference.com/w/cpp/language/operator_precedence
+  switch (ty) {
+    case kPrimitive:
+      return 0;
+    case kCast:
+    case kBitCast:
+      return 2;
+    case kAdd:
+    case kSub:
+      return 6;
+    case kMul:
+    case kDiv:
+    case kMod:
+      return 5;
+    case kMax:
+    case kMin:
+      return 99;
+    case kAnd:
+      return 11;
+    case kOr:
+      return 13;
+    case kLshift:
+    case kRshift:
+      return 7;
+    case kXor:
+      return 12;
+    case kCompareSelect:
+      return 16;
+    default:
+      return 99;
+  }
+}
+
+class TORCH_API Cast : public ExprNode<Cast> {
+ public:
+  ExprPtr src_value() const {
+    return src_value_;
+  }
+
+  void set_src_value(ExprPtr src_value) {
+    src_value_ = std::move(src_value);
+  }
+
+  static ExprHandle make(Dtype dtype, const ExprHandle& src_value) {
+    return ExprHandle(alloc<Cast>(dtype, src_value.node()));
+  }
+  Cast(Dtype dtype, ExprPtr src_value)
+      : ExprNodeBase(dtype, kCast), src_value_(std::move(src_value)) {}
+
+  bool isConstant() const override {
+    return src_value_->isConstant();
+  }
+
+ private:
+  ExprPtr src_value_;
+};
+
+template <typename T>
+ExprHandle cast(const ExprHandle& src_value) {
+  return Cast::make(Dtype(ToDtype<T>(), src_value.dtype().lanes()), src_value);
+}
+
+// This is a bitwise cast, akin to bitcast in LLVM
+class TORCH_API BitCast : public ExprNode<BitCast> {
+ public:
+  ExprPtr src_value() const {
+    return src_value_;
+  }
+
+  void set_src_value(ExprPtr src_value) {
+    src_value_ = std::move(src_value);
+  }
+
+  static ExprHandle make(Dtype dtype, const ExprHandle& src_value) {
+    return ExprHandle(alloc<BitCast>(dtype, src_value.node()));
+  }
+  BitCast(Dtype dtype, ExprPtr src_value)
+      : ExprNodeBase(dtype, kBitCast), src_value_(std::move(src_value)) {
+    TORCH_CHECK(src_value_->dtype().byte_size() == dtype.byte_size());
+  }
+
+  bool isConstant() const override {
+    return src_value_->isConstant();
+  }
+
+ private:
+  ExprPtr src_value_;
+};
+
+template <typename T>
+ExprHandle bitcast(const ExprHandle& src_value) {
+  return BitCast::make(
+      Dtype(ToDtype<T>(), src_value.dtype().lanes()), src_value);
+}
+
+// Represent the expression node for binary operators.
+// A CRTP pattern to share common code among the operators.
+template <typename Op>
+class BinaryOpNode : public ExprNode<Op> {
+ public:
+  ExprPtr lhs() const {
+    return this->lhs_;
+  }
+  ExprPtr rhs() const {
+    return this->rhs_;
+  }
+
+  void set_lhs(ExprPtr lhs) {
+    lhs_ = std::move(lhs);
+  }
+
+  void set_rhs(ExprPtr rhs) {
+    rhs_ = std::move(rhs);
+  }
+
+  static ExprHandle make(const ExprHandle& lhs, const ExprHandle& rhs) {
+    return ExprHandle(alloc<Op>(lhs.node(), rhs.node()));
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  BinaryOpNode(
+      ExprPtr lhs_v,
+      ExprPtr rhs_v,
+      IRNodeType expr_type,
+      ScalarType ret_type = ScalarType::Undefined)
+      : ExprNode<Op>(
+            // NOLINTNEXTLINE(clang-analyzer-core.CallAndMessage)
+            BinaryOpDtype(lhs_v->dtype(), rhs_v->dtype(), ret_type),
+            expr_type),
+        lhs_(CastIfNeeded(std::move(lhs_v), ExprNode<Op>::dtype())),
+        rhs_(CastIfNeeded(std::move(rhs_v), ExprNode<Op>::dtype())) {}
+
+ private:
+  static ExprPtr CastIfNeeded(ExprPtr expr, Dtype dst_dtype) {
+    if (expr->dtype() == dst_dtype) {
+      return expr;
+    }
+    return Cast::make(dst_dtype, ExprHandle(std::move(expr))).node();
+  }
+
+  ExprPtr lhs_;
+  ExprPtr rhs_;
+};
+
+namespace detail {
+template <typename T>
+void bin_op_deducer(BinaryOpNode<T>);
+bool bin_op_deducer(...);
+} // namespace detail
+
+class TORCH_API Add : public BinaryOpNode<Add> {
+ public:
+  Add(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kAdd) {}
+};
+
+class TORCH_API Sub : public BinaryOpNode<Sub> {
+ public:
+  Sub(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kSub) {}
+};
+
+class TORCH_API Mul : public BinaryOpNode<Mul> {
+ public:
+  Mul(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kMul) {}
+};
+
+class TORCH_API Div : public BinaryOpNode<Div> {
+ public:
+  Div(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kDiv) {}
+};
+
+class TORCH_API Mod : public BinaryOpNode<Mod> {
+ public:
+  Mod(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kMod) {}
+};
+
+template <typename Op>
+class BitwiseOpNode : public BinaryOpNode<Op> {
+ public:
+  BitwiseOpNode(ExprPtr lhs, ExprPtr rhs, IRNodeType type)
+      : BinaryOpNode<Op>(std::move(lhs), std::move(rhs), type) {}
+
+  static ExprHandle make(const ExprHandle& lhs, const ExprHandle& rhs) {
+    if (!lhs.dtype().is_integral()) {
+      throw unsupported_dtype();
+    }
+    if (lhs.dtype() != rhs.dtype()) {
+      throw malformed_input("lhs/rhs dtype mismatch");
+    }
+    return BinaryOpNode<Op>::make(lhs, rhs);
+  }
+};
+
+class TORCH_API And : public BitwiseOpNode<And> {
+ public:
+  And(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kAnd) {}
+};
+
+class TORCH_API Or : public BitwiseOpNode<Or> {
+ public:
+  Or(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kOr) {}
+};
+
+class TORCH_API Xor : public BitwiseOpNode<Xor> {
+ public:
+  Xor(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kXor) {}
+};
+
+class TORCH_API Lshift : public BitwiseOpNode<Lshift> {
+ public:
+  Lshift(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kLshift) {}
+};
+
+class TORCH_API Rshift : public BitwiseOpNode<Rshift> {
+ public:
+  Rshift(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kRshift) {}
+};
+
+// TODO: add TORCH_API
+// Currently adding it results in a compilation error on Windows
+class Max : public BinaryOpNode<Max> {
+ private:
+  bool propagate_nans_;
+
+ public:
+  Max(ExprPtr lhs, ExprPtr rhs, bool propagate_nans)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kMax),
+        propagate_nans_(propagate_nans) {}
+
+  bool propagate_nans() const {
+    return propagate_nans_;
+  }
+
+  static ExprHandle make(const ExprHandle& lhs, const ExprHandle& rhs) = delete;
+  static ExprHandle make(
+      const ExprHandle& lhs,
+      const ExprHandle& rhs,
+      bool propagate_nans) {
+    return ExprHandle(alloc<Max>(lhs.node(), rhs.node(), propagate_nans));
+  }
+};
+
+// TODO: add TORCH_API
+// Currently adding it results in a compilation error on Windows
+class Min : public BinaryOpNode<Min> {
+ private:
+  bool propagate_nans_;
+
+ public:
+  Min(ExprPtr lhs, ExprPtr rhs, bool propagate_nans)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kMin),
+        propagate_nans_(propagate_nans) {}
+
+  bool propagate_nans() const {
+    return propagate_nans_;
+  }
+
+  static ExprHandle make(const ExprHandle& lhs, const ExprHandle& rhs) = delete;
+  static ExprHandle make(
+      const ExprHandle& lhs,
+      const ExprHandle& rhs,
+      bool propagate_nans) {
+    return ExprHandle(alloc<Min>(lhs.node(), rhs.node(), propagate_nans));
+  }
+};
+
+// Encode typed immediate values e.g. IntImm, FloatImm.
+#define IMM_DECLARE(Type, Name)                               \
+  class TORCH_API Name##Imm : public ExprNode<Name##Imm> {    \
+   public:                                                    \
+    Name##Imm(Type value)                                     \
+        : ExprNodeBase(k##Name, kPrimitive), value_(value) {} \
+    bool isConstant() const override {                        \
+      return true;                                            \
+    }                                                         \
+    Type value() const {                                      \
+      return value_;                                          \
+    }                                                         \
+    static ExprHandle make(Type value) {                      \
+      return ExprHandle(alloc<Name##Imm>(value));             \
+    }                                                         \
+                                                              \
+   private:                                                   \
+    Type value_;                                              \
+  };
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_DECLARE);
+#undef IMM_DECLARE
+
+// Get immediate by ScalarType.
+template <typename T>
+ExprPtr getImmediateByType(ScalarType immType, T initialVal) {
+  switch (immType) {
+#define TYPE_CASE(Type, Name) \
+  case ScalarType::Name:      \
+    return alloc<Name##Imm>(Type(initialVal));
+    // NOLINTNEXTLINE(bugprone-branch-clone)
+    AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+    default:
+      throw unsupported_dtype();
+  }
+  return nullptr;
+}
+
+template <typename T>
+ExprPtr getImmediateByType(Dtype dtype, T initialVal) {
+  return getImmediateByType<T>(dtype.scalar_type(), initialVal);
+}
+
+template <typename T>
+ExprPtr immLike(const ExprPtr& e, T v) {
+  return getImmediateByType<T>(e->dtype(), v);
+}
+
+template <typename T>
+ExprPtr immLike(const ExprHandle& e, T v) {
+  return immLike(e.node(), v);
+}
+
+inline c10::optional<int64_t> intValue(const ExprPtr& e) {
+#define TYPE_CASE(Type, Name)      \
+  if (auto v = to<Name##Imm>(e)) { \
+    return v->value();             \
+  }
+  AT_FORALL_INT_TYPES(TYPE_CASE);
+#undef TYPE_CASE
+  return c10::nullopt;
+}
+
+inline c10::optional<int64_t> intValue(const ExprHandle& e) {
+  return intValue(e.node());
+}
+
+template <typename T>
+T immediateAs(const ExprPtr& e) {
+#define TYPE_CASE(Type, Name)                \
+  if (Name##ImmPtr imm = to<Name##Imm>(e)) { \
+    return imm->value();                     \
+  }
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+  throw unsupported_dtype();
+  return 0;
+}
+
+template <typename T>
+T immediateAs(const ExprHandle& e) {
+  return immediateAs<T>(e.node());
+}
+
+template <typename T>
+bool immediateEquals(const ExprPtr& e, T val) {
+#define TYPE_CASE(Type, Name)                \
+  if (Name##ImmPtr imm = to<Name##Imm>(e)) { \
+    return imm->value() == val;              \
+  }
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+  throw unsupported_dtype();
+  return false;
+}
+
+TORCH_API bool immediateIsNegative(const ExprPtr& e);
+
+TORCH_API bool immediateIsPositive(const ExprPtr& e);
+
+TORCH_API bool immediateIsZero(const ExprPtr& e);
+
+// Represents a ramp vector node:
+//     [base, base + 1 * stride, ... , base + (lanes - 1) * stride]
+class TORCH_API Ramp : public ExprNode<Ramp> {
+ public:
+  ExprPtr base() const {
+    return base_;
+  }
+  ExprPtr stride() const {
+    return stride_;
+  }
+
+  void set_base(ExprPtr base) {
+    base_ = std::move(base);
+  }
+
+  void set_stride(ExprPtr stride) {
+    stride_ = std::move(stride);
+  }
+
+  static ExprHandle make(
+      const ExprHandle& base,
+      const ExprHandle& stride,
+      int lanes) {
+    if (stride.dtype() != base.dtype()) {
+      throw malformed_input("Bad stride in Ramp");
+    }
+    return ExprHandle(alloc<Ramp>(base.node(), stride.node(), lanes));
+  }
+  int lanes() const {
+    return lanes_;
+  }
+
+  Ramp(ExprPtr base, ExprPtr stride, int lanes)
+      : ExprNodeBase(Dtype(base->dtype(), lanes)),
+        base_(std::move(base)),
+        stride_(std::move(stride)),
+        lanes_(lanes) {}
+
+ private:
+  ExprPtr base_;
+  ExprPtr stride_;
+  int lanes_;
+};
+
+class TORCH_API Load : public ExprNode<Load> {
+ public:
+  VarPtr base_handle() const {
+    return buf_->base_handle();
+  }
+  std::vector<ExprPtr> indices() const {
+    return indices_;
+  }
+  ExprPtr flat_index() const {
+    TORCH_CHECK(indices_.size() == 1, "Indices haven't been flattened.");
+    return indices_[0];
+  }
+  BufPtr buf() const {
+    return buf_;
+  }
+
+  void set_buf(BufPtr buf) {
+    buf_ = std::move(buf);
+  }
+
+  void set_indices(std::vector<ExprPtr> indices) {
+    indices_ = std::move(indices);
+  }
+
+  static ExprHandle make(
+      Dtype dtype,
+      const BufHandle& buf,
+      const std::vector<ExprHandle>& indices);
+  static ExprHandle make(
+      const BufHandle& buf,
+      const std::vector<ExprHandle>& indices);
+
+  Load(Dtype dtype, BufPtr base_handle, std::vector<ExprPtr> indices);
+  Load(BufPtr base_handle, const std::vector<ExprPtr>& indices);
+
+ private:
+  BufPtr buf_;
+  std::vector<ExprPtr> indices_;
+};
+
+class TORCH_API Broadcast : public ExprNode<Broadcast> {
+ public:
+  ExprPtr value() const {
+    return value_;
+  }
+
+  void set_value(ExprPtr value) {
+    value_ = std::move(value);
+  }
+
+  int lanes() const {
+    return lanes_;
+  }
+  static ExprHandle make(const ExprHandle& value, int lanes) {
+    return ExprHandle(alloc<Broadcast>(value.node(), lanes));
+  }
+  Broadcast(ExprPtr value, int lanes)
+      : ExprNodeBase(Dtype(value->dtype(), lanes)),
+        value_(std::move(value)),
+        lanes_(lanes) {}
+
+ private:
+  ExprPtr value_;
+  int lanes_;
+};
+
+class TORCH_API IfThenElse : public ExprNode<IfThenElse> {
+ public:
+  ExprPtr condition() const {
+    return condition_;
+  }
+
+  // Lazily evaluated only if condition is true
+  ExprPtr true_value() const {
+    return true_;
+  }
+
+  // Lazily evaluated only if condition is false
+  ExprPtr false_value() const {
+    return false_;
+  }
+
+  void set_condition(ExprPtr condition) {
+    condition_ = std::move(condition);
+  }
+
+  void set_true_value(ExprPtr true_value) {
+    true_ = std::move(true_value);
+  }
+
+  void set_false_value(ExprPtr false_value) {
+    false_ = std::move(false_value);
+  }
+
+  static ExprHandle make(
+      const ExprHandle& c,
+      const ExprHandle& t,
+      const ExprHandle& f) {
+    if (!c.dtype().is_integral()) {
+      throw unsupported_dtype();
+    }
+    if (c.dtype().lanes() != 1) {
+      throw unsupported_dtype();
+    }
+    if (t.dtype() != f.dtype()) {
+      throw malformed_input("Bad dtype in IfThenElse");
+    }
+    return ExprHandle(alloc<IfThenElse>(c.node(), t.node(), f.node()));
+  }
+
+  IfThenElse(ExprPtr c, ExprPtr t, ExprPtr f)
+      : ExprNodeBase(t->dtype()),
+        condition_(std::move(c)),
+        true_(std::move(t)),
+        false_(std::move(f)) {}
+
+ private:
+  ExprPtr condition_;
+  ExprPtr true_;
+  ExprPtr false_;
+};
+
+class TORCH_API CompareSelect : public ExprNode<CompareSelect> {
+ public:
+  CompareSelectOperation compare_select_op() const {
+    return compare_op_;
+  }
+  ExprPtr lhs() const {
+    return this->lhs_;
+  }
+  ExprPtr rhs() const {
+    return this->rhs_;
+  }
+  ExprPtr ret_val1() const {
+    return this->ret_val1_;
+  }
+  ExprPtr ret_val2() const {
+    return this->ret_val2_;
+  }
+
+  void set_lhs(ExprPtr lhs) {
+    lhs_ = std::move(lhs);
+  }
+
+  void set_rhs(ExprPtr rhs) {
+    rhs_ = std::move(rhs);
+  }
+
+  void set_ret_val1(ExprPtr ret_val1) {
+    ret_val1_ = std::move(ret_val1);
+  }
+
+  void set_ret_val2(ExprPtr ret_val2) {
+    ret_val2_ = std::move(ret_val2);
+  }
+
+  CompareSelectBias bias() const {
+    return bias_;
+  }
+
+  static ExprHandle make(
+      const ExprHandle& lhs,
+      const ExprHandle& rhs,
+      CompareSelectOperation cmp_op,
+      CompareSelectBias bias = kUnbiased) {
+    if (lhs.dtype() != rhs.dtype()) {
+      throw malformed_input("bad dtype in CompareSelect");
+    }
+    return ExprHandle(alloc<CompareSelect>(
+        lhs.node(),
+        rhs.node(),
+        IntImm::make(1).node(),
+        IntImm::make(0).node(),
+        cmp_op,
+        bias));
+  }
+
+  static ExprHandle make(
+      const ExprHandle& lhs,
+      const ExprHandle& rhs,
+      const ExprHandle& ret_val1,
+      const ExprHandle& ret_val2,
+      CompareSelectOperation cmp_op,
+      CompareSelectBias bias = kUnbiased) {
+    if (lhs.dtype() != rhs.dtype() || ret_val1.dtype() != ret_val2.dtype()) {
+      throw malformed_input("bad dtype in CompareSelect");
+    }
+    return ExprHandle(alloc<CompareSelect>(
+        lhs.node(),
+        rhs.node(),
+        ret_val1.node(),
+        ret_val2.node(),
+        cmp_op,
+        bias));
+  }
+
+  CompareSelect(
+      ExprPtr lhs,
+      ExprPtr rhs,
+      ExprPtr ret_val1,
+      ExprPtr ret_val2,
+      CompareSelectOperation cmp_op,
+      CompareSelectBias bias = kUnbiased)
+      : ExprNodeBase(ret_val1->dtype()),
+        lhs_(std::move(lhs)),
+        rhs_(std::move(rhs)),
+        ret_val1_(std::move(ret_val1)),
+        ret_val2_(std::move(ret_val2)),
+        compare_op_(cmp_op),
+        bias_(bias) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CompareSelect(
+      ExprPtr lhs,
+      ExprPtr rhs,
+      CompareSelectOperation cmp_op,
+      CompareSelectBias bias = kUnbiased)
+      : ExprNodeBase(kInt),
+        lhs_(std::move(lhs)),
+        rhs_(std::move(rhs)),
+        ret_val1_(alloc<IntImm>(1)),
+        ret_val2_(alloc<IntImm>(0)),
+        compare_op_(cmp_op),
+        bias_(bias) {}
+
+ private:
+  ExprPtr lhs_;
+  ExprPtr rhs_;
+  ExprPtr ret_val1_;
+  ExprPtr ret_val2_;
+  CompareSelectOperation compare_op_;
+  CompareSelectBias bias_;
+};
+
+enum IntrinsicsOp {
+  kSin,
+  kCos,
+  kTan,
+  kAsin,
+  kAcos,
+  kAtan,
+  kAtan2,
+  kSinh,
+  kCosh,
+  kTanh,
+  kSigmoid,
+  kExp,
+  kExpm1,
+  kAbs,
+  kLog,
+  kLog2,
+  kLog10,
+  kLog1p,
+  kErf,
+  kErfc,
+  kSqrt,
+  kRsqrt,
+  kPow,
+  kCeil,
+  kFloor,
+  kRound,
+  kTrunc,
+  kFmod,
+  kRemainder,
+  kLgamma,
+  kFrac,
+  kIsNan,
+  kRand, // We need more discussions on this. Should we consider stateful?
+  kMaxIntrinsicsOp,
+};
+
+class TORCH_API Intrinsics : public ExprNode<Intrinsics> {
+ public:
+  static ExprHandle make(IntrinsicsOp op_type, const ExprHandle& v1) {
+    return ExprHandle(alloc<Intrinsics>(op_type, v1.node()));
+  }
+
+  static ExprHandle make(
+      IntrinsicsOp op_type,
+      const ExprHandle& v1,
+      const ExprHandle& v2) {
+    return ExprHandle(alloc<Intrinsics>(op_type, v1.node(), v2.node()));
+  }
+
+  static ExprHandle make(
+      IntrinsicsOp op_type,
+      const std::vector<ExprHandle>& params) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<ExprPtr> params_nodes(params.size());
+    for (size_t i = 0; i < params.size(); i++) {
+      params_nodes[i] = params[i].node();
+    }
+    return ExprHandle(alloc<Intrinsics>(op_type, params_nodes));
+  }
+
+  static ExprHandle make(IntrinsicsOp op_type, Dtype dtype) {
+    return ExprHandle(alloc<Intrinsics>(op_type, dtype));
+  }
+
+  IntrinsicsOp op_type() const {
+    return op_type_;
+  }
+
+  std::string func_name() const {
+    switch (op_type()) {
+      case kSin:
+        return "sin";
+      case kCos:
+        return "cos";
+      case kTan:
+        return "tan";
+      case kAsin:
+        return "asin";
+      case kAcos:
+        return "acos";
+      case kAtan:
+        return "atan";
+      case kAtan2:
+        return "atan2";
+      case kSinh:
+        return "sinh";
+      case kCosh:
+        return "cosh";
+      case kTanh:
+        return "tanh";
+      case kSigmoid:
+        return "sigmoid";
+      case kExp:
+        return "exp";
+      case kAbs:
+        return "abs";
+      case kLog:
+        return "log";
+      case kLog2:
+        return "log2";
+      case kLog10:
+        return "log10";
+      case kLog1p:
+        return "log1p";
+      case kErf:
+        return "erf";
+      case kSqrt:
+        return "sqrt";
+      case kRsqrt:
+        return "rsqrt";
+      case kPow:
+        return "pow";
+      case kCeil:
+        return "ceil";
+      case kFloor:
+        return "floor";
+      case kRound:
+        return "round";
+      case kTrunc:
+        return "trunc";
+      case kRand:
+        return "rand";
+      case kFmod:
+        return "fmod";
+      case kRemainder:
+        return "remainder";
+      case kLgamma:
+        return "lgamma";
+      case kExpm1:
+        return "expm1";
+      case kErfc:
+        return "erfc";
+      case kFrac:
+        return "frac";
+      case kIsNan:
+        return "isnan";
+      default:
+        throw std::runtime_error(
+            "invalid op_type: " + c10::to_string(op_type()));
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(IntrinsicsOp op_type, Dtype dtype)
+      : ExprNodeBase(IntrinsicsDtype(op_type, dtype)),
+        params_({}),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != 0) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(IntrinsicsOp op_type, ExprPtr v1)
+      : ExprNodeBase(IntrinsicsDtype(op_type, v1->dtype())),
+        params_({std::move(v1)}),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != 1) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(IntrinsicsOp op_type, ExprPtr v1, ExprPtr v2)
+      : ExprNodeBase(IntrinsicsDtype(op_type, v1->dtype(), v2->dtype())),
+        params_({std::move(v1), std::move(v2)}),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != 2) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(IntrinsicsOp op_type, const std::vector<ExprPtr>& params)
+      : ExprNodeBase(IntrinsicsDtype(op_type, params)),
+        params_(params),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != nparams()) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(
+      IntrinsicsOp op_type,
+      Dtype dtype,
+      const std::vector<ExprPtr>& params)
+      : ExprNodeBase(IntrinsicsDtype(op_type, dtype)),
+        params_(params),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != nparams()) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  bool isPure() const {
+    return op_type_ != kRand;
+  }
+
+  int nparams() const {
+    return params_.size();
+  }
+
+  ExprPtr param(int index) const {
+    return params_[index];
+  }
+  const std::vector<ExprPtr>& params() const {
+    return params_;
+  }
+
+  void set_params(std::vector<ExprPtr> params) {
+    params_ = std::move(params);
+  }
+
+  static int OpArgCount(IntrinsicsOp op_type);
+
+ private:
+  static Dtype IntrinsicsDtype(IntrinsicsOp op_type, Dtype dt1);
+  static Dtype IntrinsicsDtype(IntrinsicsOp op_type, Dtype dt1, Dtype dt2);
+  static Dtype IntrinsicsDtype(
+      IntrinsicsOp op_type,
+      const std::vector<ExprPtr>& params);
+
+  std::vector<ExprPtr> params_;
+  IntrinsicsOp op_type_;
+};
+
+TORCH_API std::vector<ExprPtr> ExprHandleVectorToExprVector(
+    const std::vector<ExprHandle>&);
+TORCH_API std::vector<ExprHandle> ExprVectorToExprHandleVector(
+    const std::vector<ExprPtr>&);
+TORCH_API std::vector<VarPtr> VarHandleVectorToVarVector(
+    const std::vector<VarHandle>&);
+TORCH_API std::vector<VarHandle> VarVectorToVarHandleVector(
+    const std::vector<VarPtr>&);
+TORCH_API ExprPtr flatten_index(
+    const std::vector<ExprPtr>& dims,
+    const std::vector<ExprPtr>& indices,
+    const std::vector<ExprPtr>& strides);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir_cloner.h

@@ -0,0 +1,65 @@
+#pragma once
+#include <c10/core/ScalarType.h>
+#include <torch/csrc/Export.h>
+#include <vector>
+
+#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class TORCH_API IRCloner : public IRMutator {
+ public:
+  ~IRCloner() override = default;
+  ExprPtr mutate(AddPtr v) override;
+  ExprPtr mutate(SubPtr v) override;
+  ExprPtr mutate(MulPtr v) override;
+  ExprPtr mutate(DivPtr v) override;
+  ExprPtr mutate(ModPtr v) override;
+  ExprPtr mutate(MaxPtr v) override;
+  ExprPtr mutate(MinPtr v) override;
+  ExprPtr mutate(AndPtr v) override;
+  ExprPtr mutate(OrPtr v) override;
+  ExprPtr mutate(XorPtr v) override;
+  ExprPtr mutate(LshiftPtr v) override;
+  ExprPtr mutate(RshiftPtr v) override;
+  ExprPtr mutate(CompareSelectPtr v) override;
+#define IMM_MUTATE_DECLARE(Type, Name) ExprPtr mutate(Name##ImmPtr v) override;
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_MUTATE_DECLARE);
+#undef IMM_MUTATE_DECLARE
+  ExprPtr mutate(CastPtr v) override;
+  ExprPtr mutate(BitCastPtr v) override;
+  ExprPtr mutate(VarPtr v) override;
+  ExprPtr mutate(BufPtr v) override;
+  ExprPtr mutate(RampPtr v) override;
+  ExprPtr mutate(LoadPtr v) override;
+  ExprPtr mutate(BroadcastPtr v) override;
+  ExprPtr mutate(IfThenElsePtr v) override;
+  ExprPtr mutate(IntrinsicsPtr v) override;
+
+  ExprPtr mutate(TermPtr v) override;
+  ExprPtr mutate(PolynomialPtr v) override;
+  ExprPtr mutate(RoundOffPtr v) override;
+  ExprPtr mutate(MaxTermPtr v) override;
+  ExprPtr mutate(MinTermPtr v) override;
+
+  ExprPtr mutate(ReduceOpPtr v) override;
+
+  StmtPtr mutate(ForPtr v) override;
+  StmtPtr mutate(BlockPtr v) override;
+  StmtPtr mutate(StorePtr v) override;
+  StmtPtr mutate(AtomicAddPtr v) override;
+  StmtPtr mutate(SyncThreadsPtr v) override;
+  StmtPtr mutate(ExternalCallPtr v) override;
+  StmtPtr mutate(ExternalCallWithAllocPtr v) override;
+
+  StmtPtr mutate(AllocatePtr v) override;
+  StmtPtr mutate(FreePtr v) override;
+  StmtPtr mutate(LetPtr v) override;
+  StmtPtr mutate(CondPtr v) override;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir_mutator.h

@@ -0,0 +1,66 @@
+#pragma once
+#include <c10/core/ScalarType.h>
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+#include <vector>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class TORCH_API IRMutator {
+ public:
+  virtual ~IRMutator() = default;
+  virtual ExprPtr mutate(AddPtr v);
+  virtual ExprPtr mutate(SubPtr v);
+  virtual ExprPtr mutate(MulPtr v);
+  virtual ExprPtr mutate(DivPtr v);
+  virtual ExprPtr mutate(ModPtr v);
+  virtual ExprPtr mutate(MaxPtr v);
+  virtual ExprPtr mutate(MinPtr v);
+  virtual ExprPtr mutate(AndPtr v);
+  virtual ExprPtr mutate(OrPtr v);
+  virtual ExprPtr mutate(XorPtr v);
+  virtual ExprPtr mutate(LshiftPtr v);
+  virtual ExprPtr mutate(RshiftPtr v);
+  virtual ExprPtr mutate(CompareSelectPtr v);
+#define IMM_MUTATE_DECLARE(Type, Name) virtual ExprPtr mutate(Name##ImmPtr v);
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_MUTATE_DECLARE);
+#undef IMM_MUTATE_DECLARE
+  virtual ExprPtr mutate(CastPtr v);
+  virtual ExprPtr mutate(BitCastPtr v);
+  virtual ExprPtr mutate(VarPtr v);
+  virtual ExprPtr mutate(BufPtr v);
+  virtual ExprPtr mutate(RampPtr v);
+  virtual ExprPtr mutate(LoadPtr v);
+  virtual ExprPtr mutate(BroadcastPtr v);
+  virtual ExprPtr mutate(IfThenElsePtr v);
+  virtual ExprPtr mutate(IntrinsicsPtr v);
+
+  virtual ExprPtr mutate(TermPtr v);
+  virtual ExprPtr mutate(PolynomialPtr v);
+  virtual ExprPtr mutate(RoundOffPtr v);
+  virtual ExprPtr mutate(MaxTermPtr v);
+  virtual ExprPtr mutate(MinTermPtr v);
+
+  virtual ExprPtr mutate(ReduceOpPtr v);
+
+  virtual StmtPtr mutate(ForPtr v);
+  virtual StmtPtr mutate(BlockPtr v);
+  virtual StmtPtr mutate(StorePtr v);
+  virtual StmtPtr mutate(AtomicAddPtr v);
+  virtual StmtPtr mutate(SyncThreadsPtr v);
+  virtual StmtPtr mutate(ExternalCallPtr v);
+  virtual StmtPtr mutate(ExternalCallWithAllocPtr v);
+
+  virtual StmtPtr mutate(AllocatePtr v);
+  virtual StmtPtr mutate(FreePtr v);
+  virtual StmtPtr mutate(FreeExtPtr v);
+  virtual StmtPtr mutate(PlacementAllocatePtr v);
+  virtual StmtPtr mutate(LetPtr v);
+  virtual StmtPtr mutate(CondPtr v);
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir_simplifier.h

@@ -0,0 +1,554 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/bounds_overlap.h>
+#include <torch/csrc/jit/tensorexpr/eval.h>
+#include <torch/csrc/jit/tensorexpr/hash_provider.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/types.h>
+
+#include <utility>
+
+/* IR Simplification
+ *
+ * Simplifies expressions in two stages:
+ *  1. Recursively traverse the map combining similar operations into Terms
+ * (interacted via Multiplication) and Polynomials (interacted via Addition). We
+ * reorder the components of each Term or Polynomial into a consistent order to
+ * allow combination or cancelling of like terms.
+ *  2. Once the format of the tree is minimal, expand each Term into a sequence
+ * of Muls, and each Polynomial into a sequence of Ads.
+ */
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// A bunch of helpers for determine the Dtype of the output of a multi argument
+// Term or Polynomial.
+template <class ExprType>
+Dtype promoteTypesVec(ExprPtr s, std::vector<ExprType>& v) {
+  Dtype t = s->dtype();
+  bool first = true;
+
+  for (const auto& e : v) {
+    if (first) {
+      t = Dtype(t.scalar_type(), e->dtype().lanes());
+      first = false;
+    }
+    t = promoteTypes(t, e->dtype());
+  }
+  return t;
+}
+
+template <class ExprType>
+Dtype promoteTypesVec(std::vector<ExprType>& v) {
+  if (v.empty()) {
+    throw malformed_input("empty list of types");
+  }
+
+  Dtype t = v[0]->dtype();
+  for (const auto& e : v) {
+    t = promoteTypes(t, e->dtype());
+  }
+  return t;
+}
+
+template <class ExprType>
+Dtype promoteTypesMap(
+    ExprPtr s,
+    std::unordered_map<SimplifierHashType, ExprType>& m) {
+  Dtype t = s->dtype();
+  bool first = true;
+  for (auto& e : m) {
+    if (first) {
+      t = Dtype(t.scalar_type(), e.second->dtype().lanes());
+      first = false;
+    }
+    t = promoteTypes(t, e.second->dtype());
+  }
+  return t;
+}
+
+template <class ExprType>
+Dtype promoteTypesVar(ExprType e) {
+  return e->dtype();
+}
+
+template <class ExprType, class... Args>
+Dtype promoteTypesVar(ExprType e, Args... es) {
+  Dtype lhs = e->dtype();
+  Dtype rhs = promoteTypesVar(es...);
+  if (e->isConstant()) {
+    lhs = Dtype(lhs.scalar_type(), rhs.lanes());
+  }
+
+  return promoteTypes(lhs, rhs);
+}
+
+// Uses the evaluator to fold an Expression with constant terms.
+// E.g. evaluateOp(Add(3, 4)) => 7.
+// Expr v must not have any unbound Vars.
+inline ExprPtr evaluateOp(ExprPtr v) {
+  ExprHandle handle(v);
+  ExprEval<SimpleIREvaluator> eval(handle);
+
+  switch (v->dtype().scalar_type()) {
+#define TYPE_CASE(Type, Name)                                 \
+  case ScalarType::Name: {                                    \
+    Type val = eval.value<Type>();                            \
+    return getImmediateByType(v->dtype().scalar_type(), val); \
+  }
+    AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+    default:
+      LOG(FATAL) << "Unsupported datatype: " << v->dtype();
+      return nullptr;
+  }
+  return nullptr;
+}
+
+// A Term represents a grouping of Exprs through multiplication.
+// E.g. product(scalar, *variables).
+class Term : public ExprNode<Term> {
+ public:
+  template <class... Args>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Term(HashProvider& hasher, ExprPtr s, Args... ts)
+      : ExprNodeBase(promoteTypesVar(s, ts...)), scalar_(s), hasher_(hasher) {
+    CHECK(s->isConstant());
+    addComponent(ts...);
+    sort();
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Term(HashProvider& hasher, ExprPtr s, std::vector<ExprPtr> v)
+      : ExprNodeBase(promoteTypesVec(s, v)),
+        variables_(std::move(v)),
+        scalar_(s),
+        hasher_(hasher) {
+    sort();
+  }
+
+  // Convenience constructor from a map of hash -> var, used when merging Terms.
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Term(
+      HashProvider& hasher,
+      ExprPtr s,
+      std::unordered_map<SimplifierHashType, ExprPtr> varmap)
+      : ExprNodeBase(promoteTypesMap(s, varmap)), scalar_(s), hasher_(hasher) {
+    for (auto& p : varmap) {
+      addComponent(p.second);
+    }
+    sort();
+  }
+
+  ExprPtr scalar() const {
+    return scalar_;
+  }
+  const std::vector<ExprPtr>& variables() const {
+    return variables_;
+  }
+  HashProvider& hasher() const {
+    return hasher_;
+  }
+
+  // Produce a hash of just the variable components of this term, to determine
+  // if it can be combined with another term.
+  SimplifierHashType hashVars() const;
+
+ private:
+  std::vector<ExprPtr> variables_;
+  ExprPtr scalar_;
+  HashProvider& hasher_;
+
+  void addComponent() {}
+  void addComponent(ExprPtr e) {
+    variables_.push_back(std::move(e));
+  }
+  template <class... Es>
+  void addComponent(ExprPtr e, Es&&... es) {
+    addComponent(std::move(e));
+    addComponent(std::forward<Es>(es)...);
+  }
+
+  // Sort by hash to normalize order of components.
+  void sort();
+};
+
+// Polynomial represents a grouping of Exprs by addition.
+// E.g. sum(*variables, scalar).
+// This would better be called Expression, but, naming conflict...
+class Polynomial : public ExprNode<Polynomial> {
+ public:
+  template <class... Args>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Polynomial(HashProvider& hasher, ExprPtr s, Args... ts)
+      : ExprNodeBase(promoteTypesVar(s, ts...)), scalar_(s), hasher_(hasher) {
+    CHECK(s->isConstant());
+    addTerm(ts...);
+    sort();
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Polynomial(HashProvider& hasher, ExprPtr s, std::vector<TermPtr> v)
+      : ExprNodeBase(promoteTypesVec(s, v)),
+        variables_(std::move(v)),
+        scalar_(s),
+        hasher_(hasher) {
+    sort();
+  }
+
+  // Helper constructor for list of terms with no scalar component.
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Polynomial(HashProvider& hasher, std::vector<TermPtr> terms)
+      : ExprNodeBase(promoteTypesVec(terms)),
+        variables_(std::move(terms)),
+        scalar_(getImmediateByType(dtype(), 0)),
+        hasher_(hasher) {
+    sort();
+  }
+
+  // Convenience constructor for map of hash -> var, used when merging
+  // Polynomials.
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Polynomial(
+      HashProvider& hasher,
+      ExprPtr s,
+      std::unordered_map<SimplifierHashType, TermPtr> varmap)
+      : ExprNodeBase(promoteTypesMap(s, varmap)), scalar_(s), hasher_(hasher) {
+    for (auto& p : varmap) {
+      addTerm(p.second);
+    }
+    sort();
+  }
+
+  ExprPtr scalar() const {
+    return scalar_;
+  }
+  const std::vector<TermPtr>& variables() const {
+    return variables_;
+  }
+  HashProvider& hasher() const {
+    return hasher_;
+  }
+
+  SimplifierHashType hashVars() const;
+
+ private:
+  std::vector<TermPtr> variables_;
+  ExprPtr scalar_;
+  HashProvider& hasher_;
+
+  void addTerm(TermPtr t) {
+    variables_.push_back(std::move(t));
+  }
+  template <class... Ts>
+  void addTerm(TermPtr t, Ts&&... ts) {
+    addTerm(std::move(t));
+    addTerm(std::forward<Ts>(ts)...);
+  }
+
+  // Sort by hash to normalize order of terms.
+  void sort();
+};
+
+class RoundOff : public BinaryOpNode<RoundOff> {
+ public:
+  RoundOff(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(lhs, rhs, IRNodeType::kOther) {}
+};
+
+class MaxTerm : public ExprNode<MaxTerm> {
+ public:
+  template <class... Args>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  MaxTerm(HashProvider& hasher, ExprPtr s, bool p, Args... ts)
+      : ExprNodeBase(s ? promoteTypesVar(s, ts...) : promoteTypesVar(ts...)),
+        scalar_(s),
+        hasher_(hasher),
+        propagate_nans_(p) {
+    addComponent(ts...);
+    uniquefy();
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  MaxTerm(HashProvider& hasher, ExprPtr s, bool p, std::vector<ExprPtr> v)
+      : ExprNodeBase(s ? promoteTypesVec(s, v) : promoteTypesVec(v)),
+        variables_(std::move(v)),
+        scalar_(s),
+        hasher_(hasher),
+        propagate_nans_(p) {
+    uniquefy();
+  }
+
+  bool propagate_nans() const {
+    return propagate_nans_;
+  }
+
+  ExprPtr scalar() const {
+    return scalar_;
+  }
+  const std::vector<ExprPtr>& variables() const {
+    return variables_;
+  }
+  HashProvider& hasher() const {
+    return hasher_;
+  }
+
+ private:
+  std::vector<ExprPtr> variables_;
+  ExprPtr scalar_;
+  HashProvider& hasher_;
+  bool propagate_nans_;
+
+  void addComponent() {}
+  void addComponent(ExprPtr e) {
+    variables_.push_back(std::move(e));
+  }
+  template <class... Es>
+  void addComponent(ExprPtr e, Es&&... es) {
+    addComponent(std::move(e));
+    addComponent(std::forward<Es>(es)...);
+  }
+
+  // Uniquefy the terms using their hash.
+  void uniquefy();
+};
+
+class MinTerm : public ExprNode<MinTerm> {
+ public:
+  template <class... Args>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  MinTerm(HashProvider& hasher, ExprPtr s, bool p, Args... ts)
+      : ExprNodeBase(s ? promoteTypesVar(s, ts...) : promoteTypesVar(ts...)),
+        scalar_(s),
+        hasher_(hasher),
+        propagate_nans_(p) {
+    addComponent(ts...);
+    uniquefy();
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  MinTerm(HashProvider& hasher, ExprPtr s, bool p, std::vector<ExprPtr> v)
+      : ExprNodeBase(s ? promoteTypesVec(s, v) : promoteTypesVec(v)),
+        variables_(std::move(v)),
+        scalar_(s),
+        hasher_(hasher),
+        propagate_nans_(p) {
+    uniquefy();
+  }
+
+  bool propagate_nans() const {
+    return propagate_nans_;
+  }
+
+  ExprPtr scalar() const {
+    return scalar_;
+  }
+  const std::vector<ExprPtr>& variables() const {
+    return variables_;
+  }
+  HashProvider& hasher() const {
+    return hasher_;
+  }
+
+ private:
+  std::vector<ExprPtr> variables_;
+  ExprPtr scalar_;
+  HashProvider& hasher_;
+  bool propagate_nans_;
+
+  void addComponent() {}
+  void addComponent(ExprPtr e) {
+    variables_.push_back(std::move(e));
+  }
+  template <class... Es>
+  void addComponent(ExprPtr e, Es&&... es) {
+    addComponent(std::move(e));
+    addComponent(std::forward<Es>(es)...);
+  }
+
+  // Uniquefy the terms using their hash.
+  void uniquefy();
+};
+
+// Context-sensitive IR simplification
+using VarBoundInfo = std::unordered_map<VarPtr, analysis::Bound>;
+
+class TORCH_API SimplifierUnderContext : public IRMutator {
+ public:
+  ~SimplifierUnderContext() override = default;
+  // Add boundary info for index variables in for-loops
+  StmtPtr mutate(ForPtr v) override;
+
+  ExprPtr mutate(DivPtr v) override;
+  ExprPtr mutate(ModPtr v) override;
+  ExprPtr mutate(CompareSelectPtr v) override;
+  ExprPtr mutate(IfThenElsePtr v) override;
+
+ protected:
+  bool getLoopBoundInfo(const ExprPtr& expr, analysis::Bound* loop_bound_info);
+
+ protected:
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  HashProvider hasher_;
+  VarBoundInfo var_bound_info_;
+};
+
+// Stmt simplification should occur in both modes.
+class TORCH_API PolynomialBase : public IRMutator {
+ public:
+  ~PolynomialBase() override = default;
+
+  StmtPtr mutate(BlockPtr v) override;
+
+  StmtPtr mutate(CondPtr v) override;
+
+  StmtPtr mutate(ForPtr v) override;
+
+  // Trivially factorize terms by GCD of scalar components.
+  TermPtr factorizePolynomial(PolynomialPtr poly);
+
+  HashProvider& hasher() {
+    return hasher_;
+  }
+
+ protected:
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  HashProvider hasher_;
+};
+
+// Simplify the IR by combining arithmetic expressions over common terms.
+class TORCH_API PolynomialTransformer : public PolynomialBase {
+ public:
+  using PolynomialBase::mutate;
+  // Inserts term into the provided map, in the case of a hash collision
+  // combines the term with the existing and updates the map.
+  void addOrUpdateTerm(
+      std::unordered_map<SimplifierHashType, TermPtr>& varmap,
+      TermPtr term);
+
+  // Add Polynomial expressions, combining Terms representing the same
+  // variables.
+  ExprPtr addPolynomials(PolynomialPtr lhs, PolynomialPtr rhs);
+
+  // Insert a new Term into the provided polynomial. If the new term has
+  // common variables to an existing term it is combined.
+  ExprPtr insertTerm(PolynomialPtr poly, TermPtr term);
+
+  // Merge and simplify addition.
+  ExprPtr mutate(AddPtr v) override;
+
+  // Subtract one term from another, cancelling if necessary.
+  ExprPtr subTerms(TermPtr lhs, TermPtr rhs, bool negated);
+
+  // Subtract the RHS Polynomial from the LHS Polynomial, cancelling out where
+  // possible.
+  ExprPtr subPolynomials(PolynomialPtr lhs, PolynomialPtr rhs);
+
+  // Merge and simplify subtraction.
+  ExprPtr mutate(SubPtr v) override;
+
+  // Multiply two terms together, usually creating a new term with the variable
+  // lists concatenated.
+  TermPtr mulTerms(TermPtr lhs, TermPtr rhs);
+
+  // Multiply a Polynomial by a Term.
+  ExprPtr polyByTerm(PolynomialPtr poly, TermPtr term);
+
+  // Match a rounding pattern and create a RoundOff if found.
+  ExprPtr isRoundOff(ExprPtr lhs, ExprPtr rhs);
+
+  // Inserts a new component into a term, simplifying if possible.
+  ExprPtr insertIntoTerm(TermPtr term, ExprPtr expr);
+
+  // Merge and simplify multiplication.
+  ExprPtr mutate(MulPtr v) override;
+
+  ExprPtr mutate(DivPtr v) override;
+
+  ExprPtr mutate(ModPtr v) override;
+
+  ExprPtr mutate(AndPtr v) override;
+
+  ExprPtr mutate(XorPtr v) override;
+
+  ExprPtr mutate(LshiftPtr v) override;
+
+  ExprPtr mutate(RshiftPtr v) override;
+
+  ExprPtr mutate(MaxPtr v) override;
+
+  ExprPtr mutate(MinPtr v) override;
+
+  ExprPtr mutate(CompareSelectPtr v) override;
+
+  ExprPtr mutate(IntrinsicsPtr v) override;
+
+  ExprPtr mutate(CastPtr v) override;
+
+  ExprPtr mutate(IfThenElsePtr v) override;
+
+  static ExprPtr simplify(ExprPtr e);
+  static ExprHandle simplify(const ExprHandle& e);
+  static StmtPtr simplify(StmtPtr e);
+};
+
+// Expands Terms and Polynomial expressions into primitive operations.
+// Does some simple factorization and reordering.
+class TORCH_API TermExpander : public PolynomialBase {
+  PolynomialTransformer* simplifier_;
+  std::set<VarPtr> eliminated_allocations_;
+
+ public:
+  using PolynomialBase::mutate;
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  TermExpander(PolynomialTransformer* simplifier) : simplifier_(simplifier) {}
+  bool check_safe() {
+    return eliminated_allocations_.empty();
+  }
+
+  // Expand Terms out to a series of Muls.
+  ExprPtr mutate(TermPtr v) override;
+
+  // Expand Polynomials out to a series of Adds.
+  ExprPtr mutate(PolynomialPtr v) override;
+
+  // Expand MaxTerms to a series of Max ops.
+  ExprPtr mutate(MaxTermPtr v) override;
+
+  // Expand MinTerms to a series of Min ops.
+  ExprPtr mutate(MinTermPtr v) override;
+
+  // Expand RoundOff to it's component: Mul(Div(lhs, rhs), rhs).
+  ExprPtr mutate(RoundOffPtr v) override;
+
+  // Eliminate zero length allocations.
+  StmtPtr mutate(AllocatePtr v) override;
+  StmtPtr mutate(FreePtr v) override;
+
+  // Override to enable condition fusing.
+  BlockPtr fuseConditions(BlockPtr v);
+  StmtPtr fuseSyncThreads(BlockPtr block);
+  StmtPtr mutate(BlockPtr v) override;
+};
+
+class TORCH_API IRSimplifier {
+ public:
+  static StmtPtr simplify(StmtPtr s);
+  static ExprPtr simplify(ExprPtr e);
+  static ExprHandle simplify(const ExprHandle& e) {
+    return ExprHandle(simplify(e.node()));
+  }
+};
+
+// Flattens the buf and performs the simplifier on the flattened dims.
+ExprPtr buf_flat_size(BufPtr v);
+// Returns true if expressions A and B can be simplified to an equal expression.
+TORCH_API bool exprEquals(ExprPtr A, ExprPtr B);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir_verifier.h

@@ -0,0 +1,58 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class Expr;
+class ExprHandle;
+class Mod;
+class And;
+class Or;
+class Xor;
+class Lshift;
+class Rshift;
+class CompareSelect;
+class Ramp;
+class Load;
+class IfThenElse;
+class Intrinsics;
+
+class Stmt;
+class ExternalCall;
+class Store;
+class For;
+class Block;
+
+class TORCH_API IRVerifier : public IRVisitor {
+ public:
+  IRVerifier() = default;
+
+  void visit(ModPtr v) override;
+  void visit(AndPtr v) override;
+  void visit(OrPtr v) override;
+  void visit(XorPtr v) override;
+  void visit(LshiftPtr v) override;
+  void visit(RshiftPtr v) override;
+  void visit(CompareSelectPtr v) override;
+  void visit(RampPtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(IfThenElsePtr v) override;
+  void visit(IntrinsicsPtr v) override;
+
+  void visit(ExternalCallPtr v) override;
+  void visit(StorePtr v) override;
+  void visit(ForPtr v) override;
+  void visit(BlockPtr v) override;
+};
+
+TORCH_API void verify(StmtPtr);
+TORCH_API void verify(ExprPtr);
+TORCH_API void verify(ExprHandle);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir_visitor.h

@@ -0,0 +1,64 @@
+#pragma once
+#include <c10/core/ScalarType.h>
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class TORCH_API IRVisitor {
+ public:
+  virtual ~IRVisitor() = default;
+  virtual void visit(AddPtr v);
+  virtual void visit(SubPtr v);
+  virtual void visit(MulPtr v);
+  virtual void visit(DivPtr v);
+  virtual void visit(ModPtr v);
+  virtual void visit(MaxPtr v);
+  virtual void visit(MinPtr v);
+  virtual void visit(AndPtr v);
+  virtual void visit(OrPtr v);
+  virtual void visit(XorPtr v);
+  virtual void visit(LshiftPtr v);
+  virtual void visit(RshiftPtr v);
+  virtual void visit(CompareSelectPtr v);
+
+#define IMM_PRINT_VISIT(Type, Name) virtual void visit(Name##ImmPtr v);
+
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_PRINT_VISIT)
+#undef IMM_PRINT_VISIT
+
+  virtual void visit(CastPtr v);
+  virtual void visit(BitCastPtr v);
+  virtual void visit(VarPtr v);
+  virtual void visit(BufPtr v);
+  virtual void visit(RampPtr v);
+  virtual void visit(LoadPtr v);
+  virtual void visit(ForPtr v);
+  virtual void visit(BlockPtr v);
+  virtual void visit(StorePtr v);
+  virtual void visit(BroadcastPtr v);
+  virtual void visit(IfThenElsePtr v);
+  virtual void visit(IntrinsicsPtr v);
+  virtual void visit(AllocatePtr v);
+  virtual void visit(FreePtr v);
+  virtual void visit(FreeExtPtr v);
+  virtual void visit(PlacementAllocatePtr v);
+  virtual void visit(LetPtr v);
+  virtual void visit(CondPtr v);
+  virtual void visit(TermPtr v);
+  virtual void visit(PolynomialPtr v);
+  virtual void visit(RoundOffPtr v);
+  virtual void visit(MaxTermPtr v);
+  virtual void visit(MinTermPtr v);
+  virtual void visit(ReduceOpPtr v);
+  virtual void visit(AtomicAddPtr v);
+  virtual void visit(SyncThreadsPtr v);
+  virtual void visit(ExternalCallPtr v);
+  virtual void visit(ExternalCallWithAllocPtr v);
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/kernel.h

@@ -0,0 +1,382 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+#include <torch/csrc/jit/passes/symbolic_shape_runtime_fusion.h>
+#include <torch/csrc/jit/passes/utils/subgraph_utils.h>
+#include <torch/csrc/jit/runtime/interpreter.h>
+#include <torch/csrc/jit/tensorexpr/analysis.h>
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/lowerings.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+struct SmallSizeTPairHash {
+ public:
+  std::size_t operator()(const std::pair<size_t, size_t>& x) const {
+    // hashing input index and then dim index
+    return x.first * 128 + x.second;
+  }
+};
+
+// Returns true if the TE fuser supports this conv2d.
+bool conv2dIsSupportedJit(const Node* node);
+// Returns true if the TE fuser supports this conv2d with mkldnn prepacked conv.
+bool mkldnnPrepackedConvIsSupportedJit(const Node* node);
+// Returns true if the TE _convolution node is Conv2d.
+bool isConv2d(const Node* node);
+// Returns true if the TE fuser supports this matmul.
+bool matmulIsSupported(const Node* node);
+template <typename T>
+inline std::vector<int64_t> bufferSizes(const T& t) {
+  std::vector<int64_t> sizes;
+  for (size_t i = 0; i < t->ndim(); i++) {
+    sizes.push_back(*intValue(t->dim(i)));
+  }
+  return sizes;
+}
+
+// Get the dimensions of a value.
+std::vector<ExprHandle> valueShape(const ArgValue& v);
+
+// If v is a tensor, broadcast it to match the shape of axes, or return
+// directly if v is a constant.
+ExprHandle tensorOrConstant(
+    const ArgValue& v,
+    const std::vector<ExprHandle>& axes);
+
+int64_t normalizeAndCheckIndex(int64_t idx, int64_t list_size);
+
+ExprHandle broadcast(BufHandle b, const std::vector<ExprHandle>& axes);
+
+ExprHandle constant(const ArgValue& v);
+
+std::vector<ExprHandle> computeIndicesToBroadcast(
+    const std::vector<ExprHandle>& outputAxes,
+    const std::vector<ExprHandle>& inputSizes);
+
+inline std::string getArgValueName(const ArgValue& a) {
+  if (std::holds_alternative<tensorexpr::BufHandle>(a)) {
+    return "BufHandle";
+  } else if (std::holds_alternative<tensorexpr::VarHandle>(a)) {
+    return "VarHandle";
+  } else if (std::holds_alternative<double>(a)) {
+    return "double";
+  } else if (std::holds_alternative<int64_t>(a)) {
+    return "int64_t";
+  } else if (std::holds_alternative<bool>(a)) {
+    return "bool";
+  } else if (std::holds_alternative<BufList>(a)) {
+    return "BufList";
+  } else if (std::holds_alternative<DoubleList>(a)) {
+    return "DoubleList";
+  } else if (std::holds_alternative<IntList>(a)) {
+    return "IntList";
+  } else if (std::holds_alternative<ArgNone>(a)) {
+    return "None";
+  } else {
+    throw std::runtime_error("ArgValue type not handled in string conversion");
+  }
+}
+
+template <class T>
+std::vector<T> convertVecArgValue(const std::vector<ArgValue>& v) {
+  std::vector<T> res;
+  for (auto& x : v) {
+    auto val = std::get_if<T>(&x);
+    if (val) {
+      res.push_back(*val);
+    } else {
+      throw std::runtime_error(
+          "vector type not homogeneous - found " + getArgValueName(x) +
+          ", expected " + getArgValueName(v[0]));
+    }
+  }
+  return res;
+}
+
+class TORCH_API TensorExprKernel {
+  struct ConstantDescr {
+    BufPtr buf;
+    // Only one of ptr and node is used at a time
+    // 1) ptr for the constant tensors
+    // 2) node for the constant custom class objects
+    void* ptr = nullptr;
+    Node* node = nullptr;
+  };
+
+ public:
+  // Constructor Params:
+  //  * subgraph
+  //      - the graph that needs to be compiled.
+  //  * kernel_func_name
+  //      - the name that should be used for the generated kernel.
+  //  * custom_lowerings
+  //      - map that represents custom lowering definitions for a set of ops.
+  //  * symbolic_shape_inputs
+  //      - a list of symbolic graph inputs that represent the symbolic dims of
+  //        the input tensors.
+  //  * pre_alloc
+  //      - a flag to control pre-allocation of buffers.
+  explicit TensorExprKernel(
+      const std::shared_ptr<Graph>& subgraph,
+      const std::string& kernel_func_name,
+      std::unordered_map<c10::Symbol, NNCLoweringFunction> custom_lowerings =
+          {},
+      std::vector<int64_t> symbolic_shape_inputs = {},
+      bool pre_alloc = false,
+      std::unordered_map<
+          const torch::jit::Value*,
+          std::vector<torch::jit::StrideInput>> symbolic_strides = {});
+
+  explicit TensorExprKernel(
+      const std::shared_ptr<Graph>& subgraph,
+      std::unordered_map<c10::Symbol, NNCLoweringFunction> custom_lowerings =
+          {},
+      std::vector<int64_t> symbolic_shape_inputs = {},
+      bool pre_alloc = false,
+      std::unordered_map<
+          const torch::jit::Value*,
+          std::vector<torch::jit::StrideInput>> symbolic_strides = {})
+      : TensorExprKernel(
+            subgraph,
+            SubgraphUtils::generateNameForGraph(subgraph),
+            custom_lowerings,
+            symbolic_shape_inputs,
+            pre_alloc,
+            symbolic_strides) {}
+
+  void run(Stack& stack) const;
+  void runFast(
+      const std::vector<void*>& inputs,
+      const std::vector<void*>& outputs) const;
+  // Expected format of stack:
+  //  ... <outputs> <inputs>
+  // i.e., output IValues must be below the input IValues in the stack.
+  void runWithAllocatedOutputs(Stack& stack) const;
+
+  void fallback(Stack& stack) const {
+    InterpreterState(code_).run(stack);
+  }
+  void recompile();
+
+  StmtPtr getCodeGenStmt();
+
+  std::string getCodeText(const std::string& attr = "") {
+    return codegen_->getCodeText(attr);
+  }
+
+  const std::shared_ptr<Graph> graph() {
+    return graph_;
+  }
+
+  const std::vector<ConstantDescr>& getConstantDescriptors() const {
+    return constants_;
+  }
+
+  const std::vector<CodeGen::BufferArg>& getBufferArgs() const {
+    return bufferArgs_;
+  }
+
+  const std::string& getKernelName() const {
+    return codegen_->kernel_func_name();
+  }
+
+  const std::vector<int64_t>& getSymbolicShapeInputs() const {
+    return symbolic_shape_inputs_;
+  }
+
+ private:
+  enum BackendType {
+    kUninitialized,
+    kSimpleIREval,
+    kLLVMCodeGen,
+    kCudaCodeGen,
+    kBlockCodeGen,
+  };
+
+  enum MemoryLayoutPolicy {
+    kContiguous,
+    kChannelsLastNdContiguous,
+  };
+
+  void compile();
+  void genInputDebugNames();
+  void runKernel(Stack& stack) const;
+
+  std::vector<ExprHandle> sizesForValue(const torch::jit::Value* v);
+
+  // These functions broadcast shape and also store a `hasBroadcast_` variable.
+  std::vector<ExprHandle> broadcastShapesMut(
+      const std::vector<ExprHandle>& a,
+      const std::vector<ExprHandle>& b);
+  std::vector<ExprHandle> broadcastShapesMut(
+      std::vector<std::vector<ExprHandle>> shapes);
+
+  ArgValue toArg(const torch::jit::Value* v) const;
+  ExprHandle constant(const torch::jit::Value* v);
+
+  Tensor computeValue(const torch::jit::Value* v);
+
+  void bindConstant(const torch::jit::Value* v);
+
+  StmtPtr transformLoops(BackendType backendType, StmtPtr st);
+
+  std::string getCodeGenName(BackendType backendType);
+
+  void getStaticOutputSizesAndStrides(
+      const at::ArrayRef<IValue>& inputs,
+      std::vector<std::vector<int64_t>>* static_sizes,
+      std::vector<std::vector<int64_t>>* static_strides) const;
+
+  std::vector<CodeGen::CallArg> prepareRunArgs(
+      const at::ArrayRef<IValue>& inputs,
+      std::vector<at::Tensor>& outputs) const;
+  BackendType inferBackendTypeFromDevice(at::Device device);
+
+  Tensor bindInput(const torch::jit::Value* input);
+  BlockPtr bindAllInputs();
+
+  // Deduce the memory layout policy to be propagated within
+  // NNC fusion group. The memory layout policy could be `kContiguous`
+  // or `kChannelsLastNdContiguous`.
+  //    `kContiguous`: Always convert the non-contiguous input tensors and
+  //        internal buffers to contiguous.
+  //    `kChannelsLastNdContiguous`: Always convert the input tensors and
+  //        internal buffers to channels-last contiguous.
+  // Currently, the rule is simple.
+  //    If all the input and out tensors of NNC fusion group are channels-last
+  //    contiguous, the policy is `kChannelsLastNdContiguous`. Otherwise, it
+  //    is always `kContiguous`.
+  void deduceMemoryLayoutPolicy();
+
+  Tensor convertSymbolicOutputToCorrectStrides(torch::jit::Value* v);
+  Tensor convertStaticShapeOutputToCorrectStrides(torch::jit::Value* v);
+  Tensor convertSymbolicOutputToCorrectStrides(
+      const std::vector<ExprHandle>& sizes,
+      const std::vector<size_t>& sorted_stride_indices_descending,
+      const std::vector<ExprPtr>& strides,
+      BufPtr& buf);
+
+  NNCLoweringFunction getCustomLoweringFor(c10::Symbol op) const;
+  std::unordered_map<c10::Symbol, NNCLoweringFunction> getCustomLowerings()
+      const {
+    return custom_lowerings_;
+  }
+
+  // Allocate memory for intermediate buffers at compile time.
+  // Specifically, we pre-allocate memory for intermediate buffers with static
+  // size and manage these buffers in the way we manage JIT constant tensors:
+  // push the buf args into the stack so NNC IR can access them at runtime.
+  std::vector<BufPtr> preAllocIntermediateBufs(
+      const std::vector<BufPtr>& interm_bufs);
+
+  struct UnpackedTensorOptions {
+    c10::optional<c10::ScalarType> dtype;
+    c10::optional<c10::Layout> layout;
+    c10::optional<c10::Device> device;
+    c10::optional<bool> pinned_memory;
+
+    UnpackedTensorOptions(const c10::TensorOptions& opts)
+        : dtype(c10::optTypeMetaToScalarType(opts.dtype_opt())),
+          layout(opts.layout_opt()),
+          device(opts.device_opt()),
+          pinned_memory(opts.pinned_memory_opt()) {}
+  };
+
+  ExprHandle getVarForShape(const c10::ShapeSymbol& ss);
+  std::vector<ExprHandle> computeInputTensorDims(
+      const torch::jit::Value* input);
+  ExprHandle getStrideArg(size_t tensor_input, size_t stride_index);
+  std::vector<ExprHandle> sizesFromSymbolicShape(
+      const c10::SymbolicShape& shape);
+  std::vector<ExprHandle> getInputStrides(
+      const torch::jit::Value* input,
+      const std::vector<ExprHandle>& inputTensorDims);
+  std::vector<torch::jit::StrideInput>& getSymbolicStrideDesc(
+      const torch::jit::Value* value);
+
+  // Apply the optimizations to the graph owned by the current fusion group,
+  // like concatenation optimization, post-op fusion, and some other graph-level
+  // optimizations.
+  void optimizeOwningGraph();
+
+  int64_t nInputs_ = 0;
+  int64_t nOutputs_ = 0;
+  std::vector<CodeGen::BufferArg> bufferArgs_;
+  std::vector<std::vector<int64_t>> tensorOutputSizes_;
+  std::vector<std::vector<int64_t>> tensorOutputStrides_;
+  std::vector<torch::jit::StrideInput> tensorOutputStrideDesc_;
+  std::vector<bool> isOutputScalar_;
+  std::vector<UnpackedTensorOptions> tensorOutputTensorOptions_;
+  std::unordered_set<BufPtr> bufOutputs_;
+  std::unordered_set<BufPtr> bufsToBeParallelized_;
+  std::unordered_map<const torch::jit::Value*, BufPtr> bufs_;
+  std::unordered_map<const torch::jit::Value*, VarHandle> scalars_;
+  std::unordered_map<const torch::jit::Value*, std::string> input_name_map_;
+  std::unique_ptr<CodeGen> codegen_;
+  at::Device device_ = at::kCPU;
+  std::shared_ptr<Graph> graph_;
+  Code code_;
+  bool allow_fallback_{false};
+  bool use_fallback_{false};
+  bool hasRandom_{false};
+  bool hasBroadcast_{false};
+  std::unordered_map<const torch::jit::Value*, std::vector<ExprHandle>>
+      known_sizes_;
+
+  std::vector<std::vector<ExprHandle>> tensorOutputSymbolicSizes_;
+  // A map from ShapeSymbol.value() to the corresponding Var.
+  std::unordered_map<int64_t, VarHandle> shapeSymbolToVar_;
+  std::unordered_map<ExprPtr, size_t> shapeSymbolInputPos_;
+  // List of values corresponding to the ShapeSymbols that are inputs to
+  // kernel being compiled. The order of these values correspond to the order
+  // of the symbolic inputs at the end of the list of inputs to the kernel.
+  std::vector<int64_t> symbolic_shape_inputs_;
+  bool has_symbolic_shapes_{false};
+
+  std::vector<at::Tensor> unpacked_constant_tensors_;
+  std::vector<ConstantDescr> constants_;
+
+  std::unordered_map<c10::Symbol, NNCLoweringFunction> custom_lowerings_;
+  StmtPtr stmt_ = nullptr;
+  bool pre_alloc_{false};
+  std::string kernel_func_name_;
+
+  // index of stack, stride index of tensor that will be appended as a codegen
+  // arg
+  std::vector<std::pair<size_t, size_t>> input_stride_args_;
+  // map from <input index, tensor dimension> to stride as arg VarHandle
+  std::unordered_map<std::pair<size_t, size_t>, VarHandle, SmallSizeTPairHash>
+      strideArgToVar_;
+  std::unordered_map<
+      const torch::jit::Value*,
+      std::vector<torch::jit::StrideInput>>
+      symbolic_strides_;
+
+  // Memory layout to be propagated with fusion group
+  MemoryLayoutPolicy memory_layout_policy_ = MemoryLayoutPolicy::kContiguous;
+};
+
+TORCH_API int& getTECudaPointwiseLoopLevels();
+TORCH_API int& getTECudaPointwiseBlockCount();
+TORCH_API int& getTECudaPointwiseBlockSize();
+TORCH_API bool& getTEGenerateBlockCode();
+TORCH_API bool& getTEMustUseLLVMOnCPU();
+TORCH_API bool fallbackAllowed();
+TORCH_API bool setFallbackAllowed(bool value);
+TORCH_API bool& getCatWoConditionals();
+TORCH_API bool& getOptConditionals();
+
+TORCH_API c10::optional<at::Device> pickDeviceType(
+    const at::ArrayRef<torch::jit::Value*>& inputs);
+
+bool isContiguous(
+    const torch::jit::Value* v,
+    at::MemoryFormat memory_format = at::MemoryFormat::Contiguous);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/llvm_codegen.h

@@ -0,0 +1,143 @@
+#pragma once
+
+#ifdef TORCH_ENABLE_LLVM
+#include <torch/csrc/Export.h>
+
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+
+#include <c10/util/Optional.h>
+
+#include <unordered_map>
+#include <vector>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class LLVMCodeGenImpl;
+class LLVMCodeGenCallee;
+
+class TORCH_API LLVMCodeGen : public CodeGen {
+ public:
+  explicit LLVMCodeGen(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& args,
+      at::Device device = at::kCPU,
+      const std::string& kernel_func_name = "func",
+      Dtype dtype = kInt,
+      c10::optional<std::string> triple = c10::nullopt,
+      c10::optional<std::string> cpu = c10::nullopt,
+      c10::optional<std::string> attrs = c10::nullopt);
+  explicit LLVMCodeGen(StmtPtr stmt);
+
+  LLVMCodeGen() = delete;
+  ~LLVMCodeGen() override;
+
+  // Cleans up all the memory used during LLVM code generation pass except
+  // the generated kernel. After calling this method, users should not call
+  // methods like `getCodeText` that require the LLVMCodeGenImpl data. However,
+  // users can continue to call this kernel using `call` and `call_raw`.
+  void cleanup_memory();
+
+  TORCH_API void call(const std::vector<CallArg>& args) override;
+  TORCH_API void call_raw(const std::vector<void*>& args) override;
+  TORCH_API void call_with_numel(void** args, int64_t numel) override;
+
+  at::Tensor empty_strided(
+      c10::IntArrayRef size,
+      c10::IntArrayRef stride,
+      c10::optional<c10::ScalarType> dtype_opt,
+      c10::optional<c10::Layout> layout_opt,
+      c10::optional<c10::Device> device_opt,
+      c10::optional<bool> pin_memory_opt) override;
+
+  template <typename T>
+  T value() {
+    return value<T>(nullptr);
+  }
+
+  template <typename T>
+  T value(std::vector<void*>& args) {
+    return value<T>(args.data());
+  }
+
+  template <typename T>
+  T value(void** args) {
+    T (*fp)(void**) = (T(*)(void**))getKernelAddress(callee_.get());
+    T rv = fp(args);
+    return rv;
+  }
+
+  std::string getCodeText(const std::string& attr = "") override;
+
+ private:
+  void* getKernelAddress(LLVMCodeGenCallee* callee);
+
+  std::unique_ptr<LLVMCodeGenCallee> callee_;
+  std::unique_ptr<LLVMCodeGenImpl> impl_;
+};
+
+struct TORCH_API LLVMCodeGenBuilder {
+  using BufferArg = CodeGen::BufferArg;
+
+  LLVMCodeGenBuilder(StmtPtr stmt, std::vector<BufferArg> args)
+      : stmt_(stmt), args_(std::move(args)) {}
+
+  LLVMCodeGenBuilder& device(at::Device device) {
+    device_ = device;
+    return *this;
+  }
+
+  LLVMCodeGenBuilder& kernelFuncName(std::string name) {
+    kernelFuncName_ = std::move(name);
+    return *this;
+  }
+
+  LLVMCodeGenBuilder& dtype(Dtype d) {
+    dtype_ = d;
+    return *this;
+  }
+
+  LLVMCodeGenBuilder& triple(std::string triple) {
+    triple_ = std::move(triple);
+    return *this;
+  }
+
+  LLVMCodeGenBuilder& cpu(std::string cpu) {
+    cpu_ = std::move(cpu);
+    return *this;
+  }
+
+  LLVMCodeGenBuilder& attrs(std::string attrs) {
+    attrs_ = std::move(attrs);
+    return *this;
+  }
+
+  std::unique_ptr<LLVMCodeGen> build() {
+    return std::make_unique<LLVMCodeGen>(
+        stmt_, args_, device_, kernelFuncName_, dtype_, triple_, cpu_, attrs_);
+  }
+
+ private:
+  StmtPtr stmt_;
+  std::vector<BufferArg> args_;
+  at::Device device_ = at::kCPU;
+  std::string kernelFuncName_ = "func";
+  Dtype dtype_ = kInt;
+  c10::optional<std::string> triple_ = c10::nullopt;
+  c10::optional<std::string> cpu_ = c10::nullopt;
+  c10::optional<std::string> attrs_ = c10::nullopt;
+};
+
+TORCH_API c10::optional<std::string>& LLVMTargetTriple();
+TORCH_API c10::optional<std::string>& LLVMTargetCPU();
+TORCH_API c10::optional<std::string>& LLVMTargetAttrs();
+TORCH_API bool& LLVMAOTWorkflow();
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+
+#endif // TORCH_ENABLE_LLVM

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/llvm_jit.h

@@ -0,0 +1,77 @@
+#pragma once
+
+#ifdef TORCH_ENABLE_LLVM
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Optional.h>
+#include <torch/csrc/Export.h>
+
+C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wsuggest-override")
+#include <llvm/ExecutionEngine/JITSymbol.h>
+C10_DIAGNOSTIC_POP()
+#include <llvm/ExecutionEngine/Orc/Core.h>
+#include <llvm/ExecutionEngine/Orc/ThreadSafeModule.h>
+#include <llvm/Target/TargetMachine.h>
+
+#include <memory>
+#include <string>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+inline std::string formatError(llvm::Error&& err, const char* msg) {
+  static constexpr const char* defaultErrorMsg =
+      "Unexpected failure in LLVM JIT";
+  std::string errorMsg(msg ? msg : defaultErrorMsg);
+  llvm::raw_string_ostream ss(errorMsg);
+  ss << ": " << err;
+  return ss.str();
+}
+
+template <typename T>
+T assertSuccess(llvm::Expected<T> valOrErr, const char* msg = nullptr) {
+  TORCH_INTERNAL_ASSERT(valOrErr, formatError(valOrErr.takeError(), msg));
+  return std::move(*valOrErr);
+}
+
+inline void assertSuccess(llvm::Error err, const char* msg = nullptr) {
+  TORCH_INTERNAL_ASSERT(!err, formatError(std::move(err), msg));
+}
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+
+namespace llvm {
+namespace orc {
+
+class PytorchLLVMJITImpl;
+
+class TORCH_API PytorchLLVMJIT {
+ public:
+  PytorchLLVMJIT(
+      c10::optional<std::string> triple,
+      c10::optional<std::string> cpu,
+      c10::optional<std::string> attrs);
+  ~PytorchLLVMJIT();
+
+  void addModule(std::unique_ptr<Module> M, std::unique_ptr<LLVMContext> C);
+
+  JITSymbol findSymbol(const std::string Name);
+
+  bool hasSymbol(const std::string& Name);
+
+  TargetMachine& getTargetMachine();
+
+  const DataLayout& getDataLayout();
+
+ private:
+  // Use the PImpl idiom here to hide the no-rtti parts of the JIT structure.
+  std::unique_ptr<PytorchLLVMJITImpl> impl_;
+};
+
+} // end namespace orc
+} // end namespace llvm
+
+#endif // ENABLE LLVM

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/loopnest.h

@@ -0,0 +1,606 @@
+#pragma once
+
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class Expr;
+class Var;
+class Buf;
+class Tensor;
+class Function;
+class Stmt;
+class For;
+class Block;
+class Store;
+class Dtype;
+
+class TORCH_API LoopNest {
+ public:
+  // A constructor for building a LoopNest from a list of Tensors
+  LoopNest(
+      const std::vector<Tensor>& output_tensors,
+      const std::vector<Tensor>& tensors_to_compute);
+
+  // A convenience constructor for the case when all tensors are output tensors
+  LoopNest(const std::vector<Tensor>& output_tensors);
+
+  // A constructor for building a LoopNest from an Stmt and a list of output
+  // buffers.
+  LoopNest(StmtPtr stmt, std::unordered_set<BufPtr> output_bufs);
+
+  // A constructor for building a LoopNest from another loopnest. It clones the
+  // other loopnest's stmt.
+  LoopNest(const LoopNest& other);
+
+  StmtPtr root_stmt() const {
+    return root_stmt_;
+  }
+
+  std::vector<ForPtr> getLoopStmtsFor(Tensor) const;
+  std::vector<ForPtr> getLoopStmtsFor(BufPtr) const;
+  std::vector<ForPtr> getLoopStmtsFor(StmtPtr) const;
+  StmtPtr getLoopBodyFor(Tensor) const;
+  StmtPtr getLoopBodyFor(BufPtr) const;
+
+  // Returns the For stmt indexed by 'indices' in the 'root' For stmt.
+  //'indices' indicates the path to the returned loop from 'root' in AST, e.g.,
+  //
+  // root: for(int i...){
+  // j_loop: for (int j...){
+  // k1_loop:  for (int k1...){
+  //            A[i, j, k1] = ....
+  //          }
+  //          B[i, j] = ...
+  // k2_loop:  for (int k2...){
+  //            A[i, j, k2] = ...
+  //          }
+  //        }
+  //      }
+  //
+  // the path from 'root' to 'j_loop' is [0]
+  // the path from 'root' to 'k1_loop' is [0, 0]
+  // the path from 'root' to 'k2_loop' is [0, 2]
+  ForPtr getLoopAt(ForPtr root, const std::vector<int>& indices) const;
+
+  // Returns the For stmt that is immediately enclosing the given stmt.
+  static ForPtr getParentLoop(StmtPtr st);
+
+  // Returns the list of For stmts corresponding to the loopnest that is
+  // enclosing the given stmt.
+  static std::vector<ForPtr> getEnclosingLoopNest(StmtPtr st);
+
+  // Returns a list of all Stmts that write to the given buf.
+  std::vector<StmtPtr> getAllWritesToBuf(BufPtr) const;
+
+  // The following methods return the For loops that contain writes to
+  // the given buf.
+  //
+  // For example, consider the following code:
+  //   for i1
+  //     for j1
+  //       a[i1,j1] =
+  //   for i2
+  //     for j2
+  //       for k2
+  //         a[i2,j2] =
+  //     for j3
+  //       a[i2,j3] =
+
+  // Returns a list of For loops which directly contain a Stmt that writes
+  // to buf.
+  // For the above example:
+  //   getAllInnermostLoopsWritingToBuf(a) => {j1, k2, j3}
+  std::vector<ForPtr> getAllInnermostLoopsWritingToBuf(BufPtr) const;
+
+  // Returns a list of For loopnests which contain a Stmt that writes to
+  // the given buf. Each loopnest here is a vector For loops.
+  // For the above example:
+  //   getAllLoopNestsWritingToBuf(a) => {{i1,j1}, {i2,j2,k2}, {i2,j3}}
+  std::vector<std::vector<ForPtr>> getAllLoopNestsWritingToBuf(BufPtr) const;
+
+  StmtPtr simplify();
+
+  // Sanitize variables and buffer names.
+  // The pass assigns predefined names for loop index variables
+  // (i,j,k,l,m,n,o,p,i1,j1,k1,...) and ensures these names are not conflicting
+  // anywhere. It also removes duplicates from other Buf nad Var names as well
+  // as replaces illegal characters in them with underscores.
+  //
+  // Note: since it's currently technically possible to use the same variable
+  // as index in two different loops, this transformation finds such cases and
+  // introduces new variables to avoid duplication.
+  static StmtPtr sanitizeNames(StmtPtr s);
+
+  bool computeInline(StmtPtr s);
+  bool computeInline(BufPtr b);
+  void inlineIntermediateBufs(bool allow_duplicated_work);
+
+  // Optimizes conditionals.
+  //
+  // Currently, only the following pattern of conditionals is optimized.
+  // This corresponds to the conditional format that is generated to handle
+  // `aten::cat` op.
+  //
+  //   for (int i = 0; i < 20; i++) {
+  //     A[i] = IfThenElse(i<5 ? 1 : 0, B[i], C[i-5])
+  //   }
+  //
+  // Constraints that must be satisfied for this optimization:
+  //   * All conditions should be of the form "var < expr".
+  //   * All conditions should have the same variable, say v.
+  //   * The condition variable found should be the same as the inner-most
+  //     loop variable. TODO: Remove this constraint.
+  //   * If there are multiple stores that contain conditionals using the same
+  //     loop variable, only the first conditional will be optimized.
+  //     TODO: Remove this constraint.
+  bool optimizeConditionals();
+
+  // Splits the given loop into 2 nested loops with the given factor as the
+  // inner loop bound. If the factor does not evenly divide the loop bound,
+  // then the remaining iterations are extracted into a tail loop that is
+  // added after the given loop.
+  //
+  // For example, consider the following code:
+  //   for (int i = 0; i < 100; ++i) {
+  //     A[i] =
+  //   }
+  //
+  // splitWithTail(i, 8, ...) will result in:
+  //   for (int i_outer = 0; i_outer < 12; ++i_outer) {
+  //     for (int i_inner = 0; i_inner < 8; ++i_inner) {
+  //       A[i_outer * 8 + i_inner] =
+  //     }
+  //   }
+  //   for (int i_tail = 0; i_tail < 4; ++i_tail) {
+  //     A[i_tail + 96] =
+  //   }
+  //
+  // The given loop will be transformed to the outer loop after splitting.
+  // So, the pointer to the input loop should be valid after splitting and
+  // will point to the outer loop. The `inner` and `tail` parameters will be
+  // set to point to the inner and tail loops that are generated.
+  static void splitWithTail(ForPtr f, int factor, ForPtr* inner, ForPtr* tail);
+  // A convenience wrapper when the caller does not need to access the
+  // split loops.
+  static void splitWithTail(ForPtr f, int factor);
+
+  // Splits the given loop into 2 nested loops with the given factor as the
+  // inner loop bound. If the factor does not evenly divide the loop bound,
+  // then a conditional is inserted into the body to handle the remaining
+  // iterations appropriately.
+  //
+  // For example, consider the following code:
+  //   for (int i = 0; i < 100; ++i) {
+  //     A[i] =
+  //   }
+  //
+  // splitWithMask(i, 8, ...) will result in:
+  //   for (int i_outer = 0; i_outer < 13; ++i_outer) {
+  //     for (int i_inner = 0; i_inner < 8; ++i_inner) {
+  //       if (i_outer * 8 + i_inner < 100) {
+  //         A[i_outer * 8 + i_inner] =
+  //       }
+  //     }
+  //   }
+  //
+  // The given loop will be transformed to the outer loop after splitting.
+  // So, the pointer to the input loop should be valid after splitting and
+  // will point to the outer loop. The `inner` parameter will be set to point
+  // to the inner loop that is generated.
+  static void splitWithMask(ForPtr f, int factor, ForPtr* inner);
+  // A convenience wrapper when the caller does not need to access the
+  // split loops.
+  static void splitWithMask(ForPtr f, int factor);
+
+  // The following methods support loop distribution.
+  // For example, consider the following code. This will be used to
+  // demonstrate the methods below.
+  //
+  // S0:  for m
+  // S1:    for i
+  // S2:      A[i] = 0
+  // S3:      for j
+  // S4:        A[i] = A[i] +
+  // S5:      B[i] = A[i]
+  // S6:      for k
+  // S7:        B[i] = B[i] +
+
+  // This method distributes the given loop over its body by splitting
+  // after every given pivot stmt.
+  //
+  // NOTE: Pivot stmts that are not in the given loop's body will be ignored.
+  //
+  // For the above example:
+  //   distributeLoop(S1, {S3, S5})
+  // will result in:
+  // S0:  for m
+  // S1:    for i
+  // S2:      A[i] = 0
+  // S3:      for j
+  // S4:        A[i] = A[i] +
+  //   :    for i
+  // S5:      B[i] = A[i]
+  //   :    for i
+  // S6:      for k
+  // S7:        B[i] = B[i] +
+  static std::vector<ForPtr> distributeLoop(
+      ForPtr loop,
+      const std::unordered_set<StmtPtr>& pivots);
+
+  // This method distributes the given loop over every stmt in its body.
+  //
+  // For the above example:
+  //   distributeLoop(S1)
+  // will result in:
+  // S0:  for m
+  // S1:    for i
+  // S2:      A[i] = 0
+  //   :    for i
+  // S3:      for j
+  // S4:        A[i] = A[i] +
+  //   :    for i
+  // S5:      B[i] = A[i]
+  //   :    for i
+  // S6:      for k
+  // S7:        B[i] = B[i] +
+  static std::vector<ForPtr> distributeLoop(ForPtr loop);
+  // Same as above, but also distribute parent loops.
+  // Returns the result of distributing the outermost loop.
+  //
+  // For the above example:
+  //   distributeLoopAndParents(S1) will result in:
+  // S0:  for m
+  // S1:    for i
+  // S2:      A[i] = 0
+  //   :  for m
+  //   :    for i
+  // S3:      for j
+  // S4:        A[i] = A[i] +
+  //   :  for m
+  //   :    for i
+  // S5:      B[i] = A[i]
+  //   :  for m
+  //   :    for i
+  // S6:      for k
+  // S7:        B[i] = B[i] +
+  static std::vector<ForPtr> distributeLoopAndParents(ForPtr loop);
+
+  // This method distributes the given loop over its body by splitting
+  // after every For stmt in its body.
+  //
+  // For the above example:
+  //   distributeLoopOverInnerLoops(S1)
+  // will result in:
+  // S0:  for m
+  // S1:    for i
+  // S2:      A[i] = 0
+  // S3:      for j
+  // S4:        A[i] = A[i] +
+  //   :    for i
+  // S5:      B[i] = A[i]
+  // S6:      for k
+  // S7:        B[i] = B[i] +
+  static std::vector<ForPtr> distributeLoopOverInnerLoops(ForPtr loop);
+  // Same as above, but also distribute parent loops.
+  // Returns the result of distributing the outermost loop.
+  //
+  // For the above example:
+  //   distributeLoopAndParentsOverInnerLoops(S1)
+  // will result in:
+  // S0:  for m
+  // S1:    for i
+  // S2:      A[i] = 0
+  // S3:      for j
+  // S4:        A[i] = A[i] +
+  //   :  for m
+  //   :    for i
+  // S5:      B[i] = A[i]
+  // S6:      for k
+  // S7:        B[i] = B[i] +
+  static std::vector<ForPtr> distributeLoopAndParentsOverInnerLoops(
+      ForPtr loop);
+
+  // This method performs loop fusion.
+  // For example, consider the following code.
+  //
+  // S1:  for m
+  // S2:    A[m] = 0
+  // S3:    for j
+  // S4:      A[m] = A[m] +
+  // S5:  for n
+  // S5:    B[n] = A[n]
+  // S6:    for k
+  // S7:      B[n] = B[n] +
+  //
+  // fuseLoops({S1, S5}), will return the following loop:
+  // S1:  for m
+  // S2:    A[m] = 0
+  // S3:    for j
+  // S4:      A[m] = A[m] +
+  // S5:    B[m] = A[m]
+  // S6:    for k
+  // S7:      B[m] = B[m] +
+  //
+  // This transformation is unsafe as it simply add all loops into the body of
+  // the first loop for fusion without correctness checks.
+  //
+  // Below are the two requirements to apply unsafeFuseLoops:
+  //  * All the loops have the same parent.
+  //  * There are no statements between these loops in their parent body.
+  static bool unsafeFuseLoops(const std::vector<ForPtr>& loops, ForPtr* fused);
+
+  // Loop fusion is done only when all the conditions below are satisfied.
+  //  * All the loops have the same parent.
+  //  * There are no statements between these loops in their parent body.
+  //  * The start bounds are the same for all loops.
+  //  * The stop bounds are the same for all loops.
+  //  * Fusing the loops does not violate or add any dependencies.
+  static bool fuseLoops(const std::vector<ForPtr>& loops, ForPtr* fused);
+
+  static void reorderAxis(ForPtr a, ForPtr b);
+
+  // Reorder the given list of loops according to the permutation specified.
+  // Here `permutation[i]` represents the position of the loop in the input
+  // which will end up at position `i` after the reorder.
+  //
+  // For example, consider the following code:
+  //   for p
+  //     for q
+  //       for r
+  //         for s
+  //           A[p,q,r,s] =
+  //
+  // reorder({p, q, r, s}, {2, 3, 0, 1}) will return the list of loops in the
+  // following form:
+  //    for r
+  //      for s
+  //        for p
+  //          for q
+  //            A[p,q,r,s] =
+  static std::vector<ForPtr> reorder(
+      const std::vector<ForPtr>& loops,
+      const std::vector<size_t>& permutation);
+
+  // Tile takes a 2d domain (x, y) and splits it into small rectangular blocks
+  // each with shape (x_factor, y_factor). The traversal over the domain turns
+  // into an outer iteration over the blocks and an inner traversal over all
+  // points in the block.
+  // Note that if x dim % x_factor or y dim % y_factor does not equal to 0, the
+  // loop body will generate corresponding tailing loops.
+  // The transformation is in-place and returns 'xtail'.
+  //
+  // For example, consider the following code:
+  //   for i: [0, 64)
+  //     for j: [0, 64)
+  //       for k: [0, 32)
+  //         A[i, j] = B[i, k] + C[j, k]
+  //
+  // tile(i, j, 4, 8) will transform "i" for-stmt into the following nested
+  // loop:
+  //   for i_outer: [0, 16)
+  //     for j_outer: [0, 8)
+  //       for i_inner: [0, 4)
+  //         for j_inner: [0, 8)
+  //           for k: [0, 32)
+  //             A[i_outer * 4 + i_inner, j_outer * 8 + j_inner] =
+  //             B[i_outer * 4 + i_inner, k] + C[j_outer * 8 + j_inner, k]
+  //
+  // tile(i, j, 4, 9) will transform "i" for-stmt into the following nested
+  // loop:
+  //   for i_outer: [0, 16)
+  //     for j_outer: [0, 7)
+  //       for i_inner: [0, 4)
+  //         for j_inner: [0, 9)
+  //           for k: (0, 32)
+  //             A[i_outer * 4 + i_inner, j_outer * 9 + j_inner] =
+  //             B[i_outer * 4 + i_inner, k] + C[j_outer * 9 + j_inner, k]
+  //     for j_tail: [0, 1)
+  //       for i_inner: [0, 4)
+  //         for k: (0, 32)
+  //           A[i_outer * 4 + i_inner, 7 * 9 + j_tail] =
+  //           B[i_outer * 4 + i_inner, k] + C[7 * 9 + j_tail, k]
+  ForPtr tile(ForPtr x, ForPtr y, int x_factor, int y_factor);
+
+  // Returns true if the given loops are perfectly nested, i.e., every loop
+  // (except the innermost) should have exactly one statement in its body
+  // and that statement must be the next inner loop.
+  static bool areLoopsPerfectlyNested(const std::vector<ForPtr>& loops);
+
+  // Returns true if the given loop has a loop-carried dependence.
+  static bool hasLoopCarriedDependence(ForPtr loop);
+
+  // Unrolls all the iterations of the given loop.
+  // Requires that the loop bounds are constant.
+  static void fullUnroll(ForPtr f, StmtPtr* unrolled);
+  static void fullUnroll(ForPtr f);
+
+  // Unrolls the given loop for the specified factor.
+  // This does not require constant bounds for the loop being unrolled.
+  static void unroll(ForPtr f, int factor, ForPtr* tail);
+  static void unroll(ForPtr f, int factor);
+
+  static bool normalize(ForPtr f);
+  static bool isNormalized(ForPtr f);
+
+  static bool flatten(const std::vector<ForPtr>& f, ForPtr* flattened);
+  static bool flatten(const std::vector<ForPtr>& f);
+
+  // Compresses the given buffer based on its use in the given Stmts.
+  //
+  // NOTE: This API assumes that there are no accesses to the given buffer
+  // outside the given statement. So, this should be called with the entire
+  // kernel statement to avoid incorrect buffer compressions.
+  //
+  // For example, given the input:
+  //
+  // for (int i = 0; i < 100; ++i) {
+  //   for (int j = 0; j < 200; ++j) {
+  //     A[i,j] = sin(i*j)
+  //   }
+  //   for (int j = 0; j < 199; ++j) {
+  //     B[i,j] = A[i,j] + A[i, j+1]
+  //   }
+  // }
+  //
+  // compressBuffer(A, ...) will compress buffer A from
+  // [100, 200] to [1, 200] and modify the code as follows:
+  //
+  // for (int i = 0; i < 100; ++i) {
+  //   for (int j = 0; j < 200; ++j) {
+  //     A[0,j] = sin(i*j)
+  //   }
+  //   for (int j = 0; j < 199; ++j) {
+  //     B[i,j] = A[0,j] + A[0, j+1]
+  //   }
+  // }
+  static void compressBuffer(BufPtr buf, StmtPtr stmt);
+
+  // Compresses all buffers in the given statement.
+  //
+  // NOTE: This API assumes that there are no accesses to buffers outside
+  // the given statement. So, this should be called with the entire
+  // kernel statement to avoid incorrect buffer compressions.
+  //
+  // TODO: Add an IR verifier check to detect invalidly compressed buffers.
+  static void compressAllBuffers(StmtPtr stmt);
+
+  // Get 'num' loops from the loopnest starting at 'f'.
+  static std::vector<ForPtr> getLoopStmtsInLoopNest(ForPtr f, size_t num);
+
+  // LoopOptions are propagated to tail.
+  static void sliceHead(ForPtr f, int factor, ForPtr* head, ForPtr* tail);
+  static void sliceHead(ForPtr f, int factor);
+  // LoopOptions are propagated to head.
+  static void sliceTail(ForPtr f, int factor, ForPtr* head, ForPtr* tail);
+  static void sliceTail(ForPtr f, int factor);
+
+  using AccessResult = std::pair<BufPtr, StmtPtr>;
+  // Insert a cache for the consumer's usages of the buffer produced in
+  // consumer, and redirect reads and writes in the consumer to that cache.
+  // Returns a pair of the new cache buffer, and the new rewritten consumer.
+  static AccessResult cacheAccesses(
+      BufPtr producer,
+      const std::string& name,
+      StmtPtr consumer);
+
+  // Insert a temporary computation of statement S in the scope of loop AT.
+  // S is assumed to be a Store or a Block containing a Store. Along with the
+  // computation itself, this transformation inserts Alloc/Free statements for
+  // the temporary buffer used in the computation.
+  static void computeAt(StmtPtr s, ForPtr at);
+
+  // Rfactor a reduction axis into a normal axis.
+  //
+  // Requirements:
+  //  * S is the reduction store
+  //  * S is the only statement in the innermost loop
+  //  * There is at least two reduction arguments in S
+  //  * OUTER_REDUCTION_FOR loop corresponds to the outermost reduction variable
+  //  used in the store and all other reduction variables are index variables of
+  //  children loops of OUTER_REDUCTION_FOR
+  //  * OUTER_REDUCTION_FOR is a perfect loop nest, i.e. it has only loops
+  //  corresponding to the other reduction variables and the store, nested into
+  //  each other
+  //
+  // What it does:
+  //   * Introduce a new buffer with an extra dimension of a size equal to the
+  //   span of the loop OUTER_REDUCTION_FOR (the new buffer is returned via
+  //   RFAC_BUF_PTR)
+  //   * Insert an initialization store for the new buffer in
+  //   OUTER_REDUCTION_FOR before its nested loop
+  //   * Replace the reduction store to the original buffer with the reduction
+  //   store to the temp buffer, removing the index var of OUTER_REDUCTION_FOR
+  //   from reduction arguments
+  //   * Insert a final reduction store over the extra dimension of the new
+  //   buffer to the original buffer
+  //   * Returns TRUE if the transformation succeeded and FALSE otherwise
+  //
+  // Example:
+  // Original IR:
+  // S1: for i      # normal axis
+  // S2:   X[i] = 0
+  // S3:   for j    # reduction axis
+  // S4:     for k  # reduction axis
+  // S5:       X[i] = ReduceOp(X[i] + Y[i,j,k], reduce_axis={j,k})
+  //
+  // After RFACTOR(S5, S3)
+  // S1: for i               # normal axis
+  // S2:   X[i] = 0
+  // S3:   for j             # reduction axis for X, normal axis for X_rfac
+  //         X_rfac[i,j] = 0
+  // S4:     for k           # reduction axis
+  //           X_rfac[i,j] = ReduceOp(X_rfac[i,j] + Y[i,j,k], reduce_axis={k})
+  //         X[i] = ReduceOp(X[i] + X_rfac[i,j], reduce_axis={j})
+  static bool rfactor(StmtPtr s, ForPtr outer_reduction_for);
+  static bool rfactor(
+      StmtPtr s,
+      ForPtr outer_reduction_for,
+      BufPtr* rfac_buf_ptr);
+
+  // Vectorize the given loop. This method requires that the given loop
+  // does not perform a reduction.
+  // It returns true if vectorization is successful and false otherwise.
+  static bool vectorize(ForPtr);
+
+  // Find the inner-most loops and vectorize them. Currently, this only works
+  // for the LLVM backend, when no reductions are involved.
+  void vectorizeInnerLoops();
+
+  void eliminateDeadStores();
+
+  void prepareForCodegen();
+
+  const std::unordered_set<BufPtr> getInputBufs() const;
+  const std::unordered_set<BufPtr> getOutputBufs() const {
+    return output_bufs_;
+  }
+  std::vector<BufPtr> getIntermediateBufs() const;
+
+  // Finds which is the outer For between a and b for loops. If neither of the 2
+  // Fors is an ancestor of the other, it returns nullptr.
+  static ForPtr findOuterFor(ForPtr a, ForPtr b);
+
+ private:
+  void initialize(
+      const std::vector<Tensor>& output_tensors,
+      const std::vector<Tensor>& tensors_to_compute);
+
+  StmtPtr root_stmt_;
+
+  std::unordered_set<BufPtr> output_bufs_;
+};
+
+TORCH_API StmtPtr FlattenIndexes(StmtPtr s);
+
+// TODO: Revisit this once we decide on how dependencies analysis should look
+// like. Maybe we would choose to use a different API and BufUse would be
+// removed, or if we decide to keep it we need to properly document its API.
+struct BufLoadOrStoreUse {
+  StmtPtr s;
+  bool isStore;
+};
+
+/*
+ * Returns a map ( Buf -> uses of this Buf), uses are represented as vectors of
+ * BufUse elements, which are StmtPtr and a bool isStore flag. The order of uses
+ * in the vectors reflects the order in which the uses appear in the given
+ * statement.
+ */
+std::unordered_map<BufPtr, std::vector<BufLoadOrStoreUse>> findLoadOrStoreUses(
+    StmtPtr s);
+
+// replaces all invalid characters with underscore
+TORCH_API std::string sanitizeName(const std::string& input_name);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/loopnest_randomization.h

@@ -0,0 +1,13 @@
+#pragma once
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// Applies a series of loop optimizations chosen randomly. This is only for
+// testing purposes. This allows automatic stress testing of NNC loop
+// transformations.
+void loopnestRandomization(int64_t seed, LoopNest& l);
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/lowerings.h

@@ -0,0 +1,49 @@
+// This file defines classes for registering standard lowerings from JIT to TE
+// IR.
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+#include <torch/csrc/jit/runtime/interpreter.h>
+#include <torch/csrc/jit/tensorexpr/analysis.h>
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+using ArgNone = std::monostate;
+using BufList = std::vector<tensorexpr::BufHandle>;
+using DoubleList = std::vector<double>;
+using IntList = std::vector<int64_t>;
+using ArgValue = std::variant<
+    tensorexpr::BufHandle,
+    tensorexpr::VarHandle,
+    double,
+    int64_t,
+    bool,
+    BufList,
+    DoubleList,
+    IntList,
+    std::string,
+    ArgNone>;
+
+using NNCLoweringFunction = std::function<Tensor(
+    const std::vector<ArgValue>&,
+    const std::vector<ExprHandle>&,
+    const std::vector<ExprHandle>&,
+    const c10::optional<ScalarType>&,
+    at::Device)>;
+
+TORCH_API FunctionSchemaMap<NNCLoweringFunction>& getNNCLoweringRegistry();
+TORCH_API NNCLoweringFunction getStandardLoweringFor(const std::string& op);
+
+struct RegisterNNCLoweringsFunction {
+  RegisterNNCLoweringsFunction(
+      const std::vector<std::string>& schemas,
+      NNCLoweringFunction fn);
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/mem_dependency_checker.h

@@ -0,0 +1,415 @@
+#pragma once
+#include <c10/core/ScalarType.h>
+#include <torch/csrc/Export.h>
+#include <utility>
+#include <vector>
+
+#include <torch/csrc/jit/tensorexpr/bounds_overlap.h>
+#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
+#include <torch/csrc/jit/tensorexpr/ir_simplifier.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/stmt.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+namespace analysis {
+
+enum class AccessType {
+  Input,
+  Output,
+  Load,
+  Store,
+  Call,
+  AtomicAdd,
+  Alloc,
+  Free
+};
+const char* AccessToString(AccessType a);
+
+class AccessInfo;
+using DependencySet = std::unordered_set<std::shared_ptr<AccessInfo>>;
+
+/* AccessInfo
+ *
+ * Represents a single bounded memory access to a buffer, for instance a Load or
+ * a Store. Holds information relating to the specific access and links to
+ * connected accesses in the dependency graph.
+ */
+class TORCH_API AccessInfo {
+ public:
+  AccessInfo(
+      size_t id,
+      AccessType type,
+      StmtPtr stmt,
+      VarPtr var,
+      IndexBounds bounds)
+      : id_(id),
+        type_(type),
+        stmt_(std::move(stmt)),
+        expr_(nullptr),
+        var_(std::move(var)),
+        bounds_(std::move(bounds)) {}
+
+  AccessInfo(
+      size_t id,
+      AccessType type,
+      ExprPtr expr,
+      StmtPtr stmt,
+      VarPtr var,
+      IndexBounds bounds)
+      : id_(id),
+        type_(type),
+        stmt_(std::move(stmt)),
+        expr_(std::move(expr)),
+        var_(std::move(var)),
+        bounds_(std::move(bounds)) {}
+
+  // Id is a unique int representing the order this access occurred in the
+  // graph.
+  size_t id() const {
+    return id_;
+  }
+
+  // The type of the access (Load, Store, etc).
+  AccessType type() const {
+    return type_;
+  }
+
+  // The enclosing Stmt this access represents. E.g. if this is a Store then
+  // Stmt is the Store itself, while if the access is caused by an Expr, this is
+  // the most immediate parent Stmt.
+  StmtPtr stmt() const {
+    return stmt_;
+  }
+
+  // If the access is represented by an Expr (such as Load or Call) then this is
+  // it, otherwise it's nullptr.
+  ExprPtr expr() const {
+    return expr_;
+  }
+
+  // The Var representing the underlying Buffer.
+  VarPtr var() const {
+    return var_;
+  }
+
+  // A vector of Bounds representing the start and end expression for each
+  // dimension.
+  IndexBounds& bounds() {
+    return bounds_;
+  }
+
+  // Each access that this depends upon,
+  // eg. if this is a Load, then it contains every Store that immediately
+  // contributes to a load of the bounds.
+  // or: if this is a Store, it contains all reads on the RHS of the Store.
+  const std::map<size_t, std::shared_ptr<AccessInfo>>& dependencies() const {
+    return dependencies_;
+  }
+
+  // Each access that depends on this one.
+  // ie. this access is present in the dependencies map of all accesses that are
+  // dependent.
+  std::map<size_t, std::shared_ptr<AccessInfo>> dependents() const {
+    std::map<size_t, std::shared_ptr<AccessInfo>> res;
+    for (const auto& kv : dependents_) {
+      res.emplace(kv.first, kv.second.lock());
+    }
+    return res;
+  }
+
+  // Returns the symbolic expression of the indices of this access.
+  std::vector<ExprPtr> getIndices() const;
+
+  // Establishes a dependency or dependent relationship with another access.
+  void addDependency(const std::shared_ptr<AccessInfo>& write);
+  void addDependent(const std::shared_ptr<AccessInfo>& read);
+
+  // helper for checking dependencies.
+  bool hasDependency(const std::shared_ptr<AccessInfo>& info) const;
+
+  // Returns the set of all nodes that are direct (immediate) dependencies of
+  // this access.
+  DependencySet getDirectDependencies();
+  // likewise, returns all nodes that directly depend on this one.
+  DependencySet getDirectDependents();
+
+  // Returns the full list of all nodes in the graph that this access depends
+  // on, and all nodes they depend on, and so forth, back to the inputs.
+  DependencySet getIndirectDependencies();
+  // likewise, returns the full list of all nodes that depend on this node, and
+  // all nodes that depend on those nodes and so on down to the outputs.
+  DependencySet getIndirectDependents();
+
+  // Does this access represent a read of memory (Load, ReduceOp, Call, etc).
+  bool isRead() const;
+  // Does this access represent a write of memory (Store, etc).
+  bool isWrite() const;
+
+  // Helpers for dumping accesses in various formats.
+  void print() const;
+  void dumpDOT(std::ostream& os) const;
+  const char* AccessTypeColour() const;
+
+ private:
+  size_t id_;
+  AccessType type_;
+  StmtPtr stmt_;
+  ExprPtr expr_;
+  VarPtr var_;
+  IndexBounds bounds_;
+
+  // Yes these should be sorted.
+  std::map<size_t, std::shared_ptr<AccessInfo>> dependencies_;
+  std::map<size_t, std::weak_ptr<AccessInfo>> dependents_;
+};
+
+using VarBoundMap = std::unordered_map<VarPtr, Bound>;
+
+/* MemDependencyChecker analyses a IR fragment and builds a dependency graph of
+ * accesses contained within.
+ *
+ * It's possible to retrieve the entire graph in node-object form, or can be
+ * used as an oracle for answering dependency questions. e.g:
+ *
+ *  analyzer.hasIndirectDependency(BufA, BufB); or,
+ *  analyzer.hasDirectDependency(LoadA, StoreB);
+ */
+class TORCH_API MemDependencyChecker : public IRVisitor {
+  struct Scope;
+
+ public:
+  MemDependencyChecker();
+  MemDependencyChecker(
+      const std::unordered_set<BufPtr>& inputs,
+      const std::unordered_set<BufPtr>& outputs);
+  MemDependencyChecker(
+      const std::vector<BufHandle>& inputs,
+      const std::vector<BufHandle>& outputs);
+
+  ~MemDependencyChecker() override = default;
+
+  // Whether or not to allow loop execution order to influence dependency
+  // calculation. If the loop may later be parallelized you don't want this.
+  bool allowLoopExecutionOrderAnalysis(bool allow = true);
+
+  // Dependency Checking API.
+  // The goal is to have enough overloads here so you don't really have to think
+  // about it.
+
+  // Returns true if any read in A has a direct dependence on a write in B.
+  bool dependsDirectly(StmtPtr A, StmtPtr B);
+  bool dependsDirectly(ExprPtr A, StmtPtr B);
+
+  // Returns true of the output depends directly on a write contained in B.
+  bool dependsDirectly(BufPtr output, StmtPtr B);
+
+  // Returns true if a read in A depends directly on the provided input.
+  bool dependsDirectly(StmtPtr A, BufPtr input);
+  bool dependsDirectly(ExprPtr A, BufPtr input);
+
+  // Outputs/inputs cannot depend directly.
+
+  // Returns true if the access A has B as an immediate dependency.
+  bool dependsDirectly(
+      const std::shared_ptr<AccessInfo>& A,
+      const std::shared_ptr<AccessInfo>& B);
+
+  // Returns true if any read in A has an ancestor write contained in B.
+  bool dependsIndirectly(StmtPtr A, StmtPtr B);
+  bool dependsIndirectly(ExprPtr A, StmtPtr B);
+
+  // Returns true of the output depends indirectly on a write contained in B.
+  bool dependsIndirectly(BufPtr output, StmtPtr B);
+
+  // Returns true if a read in A depends indirectly on the provided input.
+  bool dependsIndirectly(StmtPtr A, BufPtr input);
+  bool dependsIndirectly(ExprPtr A, BufPtr input);
+
+  // returns true if the output uses any load of the input.
+  bool dependsIndirectly(BufPtr output, BufPtr input);
+
+  // Returns true if the access A has a dependency chain to access B.
+  bool dependsIndirectly(
+      const std::shared_ptr<AccessInfo>& A,
+      const std::shared_ptr<AccessInfo>& B);
+
+  // Returns the AccessInfo
+  std::shared_ptr<AccessInfo> accessFor(StmtPtr A) const;
+  std::shared_ptr<AccessInfo> accessFor(ExprPtr A) const;
+
+  // Returns all AccessInfos.
+  std::unordered_set<std::shared_ptr<AccessInfo>> accessesWithin(
+      StmtPtr A) const;
+  // TODO: this will return only the AccessInfo for A. It's included for
+  // completeness but be aware it wont return accesses used in the computation
+  // of A.
+  std::unordered_set<std::shared_ptr<AccessInfo>> accessesWithin(
+      ExprPtr A) const;
+
+  // Accesses relating to input and output buffers.
+  std::shared_ptr<AccessInfo> input(BufPtr B) const;
+  std::shared_ptr<AccessInfo> output(BufPtr B) const;
+
+  // Returns the full history of reads and writes.
+  const std::vector<std::shared_ptr<AccessInfo>>& getHistory() const;
+
+  // Dumps the dependency graph in DOT format.
+  void dumpDAG(const std::string& filename) const;
+
+ private:
+  // Node visitors.
+  void visit(StorePtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(ForPtr v) override;
+  void visit(CondPtr v) override;
+  void visit(IfThenElsePtr v) override;
+  void visit(CompareSelectPtr v) override;
+  void visit(BlockPtr v) override;
+  void visit(LetPtr v) override;
+  void visit(AtomicAddPtr v) override;
+  void visit(AllocatePtr v) override;
+  void visit(FreePtr v) override;
+
+  using BoundRelationship = std::pair<IndexBounds, std::shared_ptr<AccessInfo>>;
+
+  // An internal struct holding the accesses found within a scope Block.
+  struct Scope {
+    Scope(BlockPtr b, std::shared_ptr<Scope> p)
+        : block(std::move(b)), parent(std::move(p)) {}
+
+    BlockPtr block;
+    std::shared_ptr<Scope> parent;
+
+    std::unordered_map<VarPtr, Bound> shadowedVarBounds;
+    std::unordered_set<VarPtr> localVars;
+
+    std::vector<std::shared_ptr<AccessInfo>> accesses_;
+
+    std::unordered_map<VarPtr, std::list<BoundRelationship>> openWrites_;
+  };
+  std::shared_ptr<Scope> currentScope_;
+
+  bool allowExecutionOrderAnalysis_{false};
+
+  std::unordered_multimap<StmtPtr, std::shared_ptr<AccessInfo>> stmtToAccess_;
+  std::unordered_multimap<ExprPtr, std::shared_ptr<AccessInfo>> exprToAccess_;
+  std::unordered_map<StmtPtr, std::vector<std::shared_ptr<AccessInfo>>>
+      scopeToAccesses_;
+
+  VarBoundMap knownVarBounds_;
+
+  // Finds all accesses that are reads within the scope of v.
+  template <typename StmtOrExprPtr>
+  DependencySet getAllReadsWithin(StmtOrExprPtr v) {
+    DependencySet reads;
+    auto insertAllReads = [&](const auto& nodes) {
+      for (const auto& l : nodes) {
+        auto bound = exprToAccess_.equal_range(l);
+        for (auto it = bound.first; it != bound.second; ++it) {
+          if (it->second->isRead()) {
+            reads.insert(it->second);
+          }
+        }
+      }
+    };
+
+    // Look for and insert accesses belonging to all nodes that act like
+    // reads.
+    insertAllReads(NodeFinder<Load>::find(v));
+    insertAllReads(NodeFinder<ReduceOp>::find(v));
+
+    return reads;
+  }
+
+  // Finds all accesses that are writes within the scope of v.
+  // Writes cannot occur in Exprs, so this is a little simpler.
+  DependencySet getAllWritesWithin(StmtPtr v) {
+    DependencySet writes;
+
+    // writes just Store currently.
+    auto stores = NodeFinder<Store>::find(std::move(v));
+    for (const auto& s : stores) {
+      auto bound = stmtToAccess_.equal_range(s);
+      for (auto it = bound.first; it != bound.second; ++it) {
+        if (it->second->isWrite()) {
+          writes.insert(it->second);
+        }
+      }
+    }
+    return writes;
+  }
+
+  // Templated helpers to work on either Exprs or Stmts.
+  template <typename StmtOrExprPtr>
+  bool dependsDirectlyHelper(StmtOrExprPtr A, StmtPtr B) {
+    auto aReads = getAllReadsWithin(A);
+    auto bWrites = getAllWritesWithin(B);
+
+    for (auto& read : aReads) {
+      for (auto& depPair : read->dependencies()) {
+        if (bWrites.count(depPair.second) != 0) {
+          return true;
+        }
+      }
+    }
+
+    return false;
+  }
+
+  template <typename StmtOrExprPtr>
+  bool dependsIndirectlyHelper(StmtOrExprPtr A, StmtPtr B) {
+    auto aReads = getAllReadsWithin(A);
+    auto bWrites = getAllWritesWithin(B);
+
+    auto aDeps = getAllWriteDependencies(aReads);
+
+    for (auto& dependency : aDeps) {
+      if (bWrites.count(dependency) != 0) {
+        return true;
+      }
+    }
+
+    return false;
+  }
+
+  DependencySet getAllWriteDependencies(const DependencySet& products);
+
+  // Maps for inputs and outputs, since they aren't present directly in the IR.
+  std::unordered_map<BufPtr, std::shared_ptr<AccessInfo>> inputs_;
+  std::unordered_map<BufPtr, std::shared_ptr<AccessInfo>> outputs_;
+  std::unordered_map<VarPtr, std::shared_ptr<AccessInfo>> intermediates_;
+
+  // Inserts accesses for Buf's: specifically for inputs and outputs.
+  void insertBuffers(
+      std::unordered_map<BufPtr, std::shared_ptr<AccessInfo>>& bufs,
+      AccessType type);
+
+  // Update the write history with a new write, adding dependencies and closing
+  // any overlapped writes (if possible).
+  void updateWriteHistory(
+      std::list<BoundRelationship>& writeHistory,
+      const std::shared_ptr<AccessInfo>& info,
+      size_t latestAccessToClose,
+      bool closeOverlapped = true,
+      bool insert = true);
+
+  // Merge a child scope into a parent scope, adding dependencies for open
+  // writes in the parent to accesses in the child.
+  void mergeScope(
+      const std::shared_ptr<Scope>& child,
+      const std::shared_ptr<Scope>& parent,
+      bool closeOverlapped = true);
+
+  // Binds symbolic vars in indices with the low and high bound for those vars.
+  std::vector<Bound> getIndicesBounds(const std::vector<ExprPtr>& indices);
+
+  size_t nextAccess_{0};
+  StmtPtr lastStmt_{nullptr};
+};
+
+} // namespace analysis
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/operators/conv2d.h

@@ -0,0 +1,105 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/operators/misc.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// An API to compute 2D depthwise convolutions with bias.
+TORCH_API Tensor conv2d_depthwise(
+    BufHandle input,
+    BufHandle weight,
+    BufHandle bias,
+    int stride,
+    int pad,
+    int groups);
+
+// An API to compute 2D depthwise convolutions without bias.
+TORCH_API Tensor conv2d_depthwise(
+    BufHandle input,
+    BufHandle weight,
+    int stride,
+    int pad,
+    int groups);
+
+TORCH_API Tensor conv2d_depthwise(
+    BufHandle input,
+    BufHandle weight,
+    BufHandle bias,
+    ExprHandle N,
+    ExprHandle C,
+    ExprHandle H,
+    ExprHandle W,
+    ExprHandle K,
+    ExprHandle CperG,
+    ExprHandle R,
+    ExprHandle S,
+    ExprHandle stride,
+    ExprHandle pad,
+    ExprHandle groups);
+
+TORCH_API Tensor conv2d_depthwise(
+    BufHandle input,
+    BufHandle weight,
+    ExprHandle N,
+    ExprHandle C,
+    ExprHandle H,
+    ExprHandle W,
+    ExprHandle K,
+    ExprHandle CperG,
+    ExprHandle R,
+    ExprHandle S,
+    ExprHandle stride,
+    ExprHandle pad,
+    ExprHandle groups);
+
+bool conv2dIsSupported(
+    const TensorInfo& input,
+    const TensorInfo& weight,
+    const TensorInfo& bias,
+    const std::vector<int64_t>& stride,
+    const std::vector<int64_t>& pad,
+    const std::vector<int64_t>& dilation,
+    int64_t groups);
+bool mkldnnPrepackedConvIsSupported(
+    const TensorInfo& input,
+    const TensorInfo& weight,
+    const std::vector<int64_t>& stride,
+    const std::vector<int64_t>& pad,
+    const std::vector<int64_t>& dilation,
+    int64_t groups);
+Tensor computeConv2d(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+Tensor computeConv1d(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+Tensor computePrepackedConv2dClampRun(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+Tensor computePrepackedLinearClampRun(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+Tensor computeMkldnnPrepackedConvRun(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/operators/matmul.h

@@ -0,0 +1,24 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/kernel.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+Tensor computeMatmul(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+Tensor computeAddMM(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

videollama2/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/operators/misc.h

@@ -0,0 +1,98 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+#include <torch/csrc/jit/tensorexpr/lowerings.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+struct TensorInfo {
+  std::vector<int64_t> dims;
+  c10::ScalarType dtype;
+};
+c10::optional<TensorInfo> getTensorInfo(BufHandle b);
+
+int64_t normalizeAndCheckIndex(int64_t idx, int64_t list_size);
+
+// Convert boolean to integer, if needed.
+ExprHandle boolToInteger(const ExprHandle& x);
+ExprHandle promoteToDtype(ExprHandle e, ScalarType dt);
+void promoteInputs(
+    std::vector<ExprHandle>& inputs,
+    const int typeConstraints = kAllTypes);
+ExprHandle promoteIntegerToDefaultType(const ExprHandle& e);
+ExprHandle promoteHalfToFloat(const ExprHandle& e);
+ExprHandle demoteOutput(
+    const ExprHandle& e,
+    const c10::optional<ScalarType> type);
+
+std::vector<ExprHandle> broadcastShapes(
+    std::vector<std::vector<ExprHandle>> shapes);
+std::vector<ExprHandle> broadcastShapes(
+    const std::vector<ExprHandle>& a,
+    const std::vector<ExprHandle>& b);
+
+std::vector<ExprHandle> valueShape(const ArgValue& v);
+ExprHandle tensorOrConstant(
+    const ArgValue& v,
+    const std::vector<ExprHandle>& axes);
+ExprHandle scalarOrConstant(const ArgValue& v);
+ExprHandle broadcast(BufHandle b, const std::vector<ExprHandle>& axes);
+ExprHandle constant(const ArgValue& v);
+
+ExprHandle clamp(
+    const ExprHandle& cmin,
+    const ExprHandle& cmax,
+    const ExprHandle& input);
+
+Tensor computeChunk(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+Tensor computeTranspose(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+Tensor computeExpand(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+Tensor computeReshape(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+Tensor computeFlatten(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+Tensor computeCatWoConditionals(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape);
+Tensor computeCat(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+Tensor computeEmbedding(
+    const std::vector<ArgValue>& inputs,
+    const std::vector<ExprHandle>& outputShape,
+    const std::vector<ExprHandle>& outputStrides,
+    const c10::optional<ScalarType>& outputType,
+    at::Device device);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch