[FFI] Use tvm ffi as the default execution backend (#1259)

* [Refactor] Update FFI type handling and simplify argument management

* Refactored FFI type definitions in runtime and code generation files to use `TVMFFIAny` instead of `TVMValue`, enhancing type clarity.
* Updated function registration in `runtime.cc` to utilize canonical names for better consistency.
* Simplified argument handling in the `simplify` transformation, ensuring unused buffer parameters are removed only when simplification is enabled.
* Adjusted autotuner and profiler parameters to standardize the execution backend to `tvm_ffi`, improving clarity in backend selection.
* Removed obsolete `adapt_torch2tvm` function from tensor utilities to streamline the codebase and reduce complexity.

* [Update] Sync TVM submodule and enhance kernel source handling

* Updated the TVM submodule to commit cdc2aced, ensuring compatibility with recent changes.
* Added functionality to print kernel source in `example_blocksparse_gemm.py` for better debugging.
* Commented out the main execution call in test files to prevent unintended execution during testing.
* Introduced `tilelang.disable_cache()` in various test files to streamline testing and avoid cache-related issues.
* Refactored kernel source retrieval methods to improve clarity and consistency across different execution backends.

* [Refactor] Clean up imports and improve code formatting

* Removed unused import of `tilelang.testing` in `test_example_blocksparse_gemm.py` to streamline the code.
* Reformatted several lines in `arg_binder.cc`, `make_packed_api.cc`, `tvm_ffi.py`, and `adapter.py` for improved readability and consistency.
* Updated comments and spacing in `tvm_ffi.py` to enhance clarity without altering functionality.

* Update execution backend options and improve resolution logic

- Changed default execution backend from "cython" to "auto" in multiple locations to allow automatic selection based on the target.
- Expanded the list of supported execution backends to include "torch" and "nvrtc" across various classes and functions.
- Enhanced backend resolution logic in `KernelCache` and `AutoTuner` to ensure appropriate backend selection based on the target.
- Updated documentation to reflect changes in execution backend options and their defaults.

* lint fix

* fix

* Enhance argument handling in CUDA and HIP runtime modules

- Updated `ExtractFuncInfo` in `rt_mod_cuda.cc` and `rt_mod_hip.cc` to map boolean argument types to int32, ensuring compatibility with device runtime.
- Refactored `BindDLTensor` in `arg_binder.cc` to improve null handling and validation checks for DLTensor parameters, utilizing expression-level guards to prevent dereferencing null pointers.
- Enhanced error checking for buffer shape, strides, and data fields, ensuring robust handling of optional inputs and maintaining consistency across various checks.

* lint fix

* lint fix

* lint fix

* lint fix

* minor fix

* fix

* recover check

* Refactor argument binding and validation in `arg_binder.cc`

- Improved null handling and validation checks in `BindDLTensor`, ensuring safe dereferencing of pointers.
- Enhanced consistency checks for buffer shape, strides, and data fields, utilizing expression-level guards.
- Updated `MakePackedAPI` to maintain code clarity and consistency in argument handling.
- Minor adjustments in test files to streamline kernel execution and improve readability.

* lint fix

* stride fix

* minor fix

* fix

* lint fix

* lint fix

* Add CUDA stream access policy window helpers and integrate with L2 persistent cache management

- Introduced functions to set and reset the CUDA stream access policy window, allowing for better control over L2 cache usage.
- Updated runtime files to include new FFI packed functions for managing stream attributes.
- Modified lower_hopper_intrin to incorporate prologue and epilogue statements for L2 cache setup and teardown.
- Enhanced tests to verify the inclusion of new FFI calls in the generated kernel source.

* check with symbolic

* support null ptr

* Update CMakeLists and lower.py for code generation and subproject status

- Added `codegen_c_host.cc` to the list of source files in CMakeLists.txt for improved code generation support.
- Updated the function call in `lower.py` to use `target.build.tilelang_c` for C target host code generation, enhancing compatibility.
- Marked the TVM subproject as dirty to indicate local modifications.

* lint fix

* Update comments for clarity in quickstart.py

tilelang/profiler/__init__.py

@@ -10,7 +10,6 @@ from tilelang.utils.tensor import (
     get_tensor_supply,
     TensorSupplyType,
     torch_assert_close,
-    adapt_torch2tvm,
 )
 from tilelang.engine.param import KernelParam
 from tilelang.jit.adapter import BaseKernelAdapter
@@ -274,9 +273,8 @@ class Profiler:
             device = tvm.cuda(0) if target == "cuda" else tvm.rocm(0)
             time_evaluator = self.mod.time_evaluator(
                 self.mod.entry_name, device, number=rep, repeat=n_repeat)
-            tvm_inputs = [adapt_torch2tvm(inp) for inp in ins]
             # Transform Latency to ms
-            return time_evaluator(*tvm_inputs).mean * 1e3
+            return time_evaluator(*ins).mean * 1e3
         else:
             raise ValueError(f"Unknown profiler: {profiler}")
 

tilelang/utils/tensor.py

 """The profiler and convert to torch utils"""
 from enum import Enum
 import torch
-from tvm import runtime
 from tvm import tir
-from torch.utils.dlpack import to_dlpack
 import numpy as np
 
 
@@ -37,23 +35,6 @@ def map_torch_type(intype: str) -> torch.dtype:
         return getattr(torch, intype)
 
 
-def adapt_torch2tvm(arg):
-    float8_dtype_map = {
-        torch.float8_e4m3fn: "float8_e4m3",
-        torch.float8_e4m3fnuz: "float8_e4m3",
-        torch.float8_e5m2: "float8_e5m2",
-        torch.float8_e5m2fnuz: "float8_e5m2",
-    }
-    if isinstance(arg, torch.Tensor):
-        if arg.dtype in {
-                torch.float8_e4m3fn, torch.float8_e4m3fnuz, torch.float8_e5m2, torch.float8_e5m2fnuz
-        }:
-            return runtime.from_dlpack(to_dlpack(arg.view(torch.int8)))._create_view(
-                shape=arg.shape, dtype=float8_dtype_map[arg.dtype])
-        return runtime.from_dlpack(to_dlpack(arg))
-    return arg
-
-
 def get_tensor_supply(supply_type: TensorSupplyType = TensorSupplyType.Integer):
 
     from tilelang.engine.param import KernelParam