Files
tilelang/testing/python/language/test_tilelang_language_alloc.py
T
Lei Wang 74da369695 [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
2025-11-18 23:50:57 +08:00

164 lines
3.6 KiB
Python

import tilelang.testing
def alloc_var(
N,
block_N,
dtype,
):
import tilelang.language as T
@T.prim_func
def main(
A: T.Tensor((N,), dtype),
B: T.Tensor((N,), dtype),
):
with T.Kernel(T.ceildiv(N, block_N), threads=block_N) as bx:
A_shared = T.alloc_shared([block_N], dtype)
tmp = T.alloc_var(dtype)
tmp = 1 # noqa: F841
T.copy(A[bx * block_N], A_shared)
T.copy(A_shared, B[bx * block_N])
return main
def run_alloc_var(
N,
block_N,
dtype,
min=None,
max=None,
):
program = alloc_var(N, block_N, dtype)
kernel = tilelang.compile(program, out_idx=[1])
code = kernel.get_kernel_source()
assert "tmp =" in code
def test_alloc_var():
run_alloc_var(1024, 128, "float16")
def alloc_var_add(
N,
block_N,
dtype,
):
import tilelang.language as T
@T.prim_func
def main(
A: T.Tensor((N,), dtype),
B: T.Tensor((N,), dtype),
):
with T.Kernel(T.ceildiv(N, block_N), threads=block_N) as bx:
A_shared = T.alloc_shared([block_N], dtype)
tmp = T.alloc_var(dtype)
tmp = 1 # noqa: F841
T.copy(A[bx * block_N], A_shared)
for i in T.Parallel(block_N):
A_shared[i] = A_shared[i] + tmp
T.copy(A_shared, B[bx * block_N])
return main
def run_alloc_var_add(
N,
block_N,
dtype,
):
program = alloc_var_add(N, block_N, dtype)
kernel = tilelang.compile(program, out_idx=[1])
code = kernel.get_kernel_source()
assert "tmp =" in code
def test_alloc_var_add():
run_alloc_var_add(1024, 128, "float16")
def alloc_var_with_initializer(
N,
block_N,
dtype,
init_value,
):
import tilelang.language as T
@T.prim_func
def main(
A: T.Tensor((N,), dtype),
B: T.Tensor((N,), dtype),
):
with T.Kernel(T.ceildiv(N, block_N), threads=block_N) as bx:
tmp = T.alloc_var(dtype, init_value)
T.copy(A[bx * block_N], B[bx * block_N])
for i in T.Parallel(block_N):
B[bx * block_N + i] = tmp
return main
def run_alloc_var_with_initializer(
N,
block_N,
dtype,
init_value,
):
program = alloc_var_with_initializer(N, block_N, dtype, init_value)
kernel = tilelang.compile(program, out_idx=[1])
code = kernel.get_kernel_source()
assert f"= {init_value};" in code
def test_alloc_var_with_initializer():
run_alloc_var_with_initializer(256, 64, "int32", 5)
def alloc_multi_vars_with_initializer(
N,
block_N,
dtype,
):
import tilelang.language as T
@T.prim_func
def main(
A: T.Tensor((N,), dtype),
B: T.Tensor((N,), dtype),
):
with T.Kernel(T.ceildiv(N, block_N), threads=block_N) as bx:
tmp0 = T.alloc_var(dtype, 1)
tmp1 = T.alloc_var(dtype, 2)
T.copy(A[bx * block_N], B[bx * block_N])
for i in T.Parallel(block_N):
B[bx * block_N + i] = tmp0 + tmp1
return main
def run_alloc_multi_vars_with_initializer(
N,
block_N,
dtype,
):
program = alloc_multi_vars_with_initializer(N, block_N, dtype)
kernel = tilelang.compile(program, out_idx=[1])
code = kernel.get_kernel_source(kernel_only=True)
assert code.count("= 1;") == 1
assert code.count("= 2;") == 1
def test_alloc_multi_vars_with_initializer():
run_alloc_multi_vars_with_initializer(256, 64, "int32")
if __name__ == "__main__":
tilelang.testing.main()