mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-22 06:50:25 +08:00
Lei Wang
74da369695
* [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
116 lines
3.9 KiB
Python
116 lines
3.9 KiB
Python
import torch
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from tilelang import tvm as tvm
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import tilelang.testing
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import tilelang as tl
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import tilelang.language as T
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from tilelang.utils import map_torch_type
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@tl.jit
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def ptr_null_test(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
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@T.prim_func
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def main(
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a_ptr: T.ptr,
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b_ptr: T.ptr,
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c_ptr: T.ptr,
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bias_ptr: T.ptr,
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m: T.int32,
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n: T.int32,
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k: T.int32,
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with_bias: T.bool,
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):
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A = T.make_tensor(a_ptr, (m, k), dtype)
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B = T.make_tensor(b_ptr, (k, n), dtype)
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C = T.make_tensor(c_ptr, (m, n), accum_dtype)
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Bias = T.make_tensor(bias_ptr, (n), accum_dtype)
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# Initialize Kernel Context
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
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A_shared = T.alloc_shared((block_M, block_K), dtype)
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B_shared = T.alloc_shared((block_N, block_K), dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for ko in T.Pipelined(T.ceildiv(k, block_K), num_stages=3):
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# Copy tile of A
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T.copy(A[by * block_M, ko * block_K], A_shared)
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T.copy(B[bx * block_N, ko * block_K], B_shared)
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T.gemm(A_shared, B_shared, C_local, transpose_B=True)
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if with_bias:
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for i, j in T.Parallel(block_M, block_N):
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C_local[i, j] += Bias[bx * block_N + j]
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T.copy(C_local, C[by * block_M, bx * block_N])
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return main
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@tl.jit
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def tensor_null_test(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
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@T.prim_func
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def main(
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A: T.Tensor((M, K), dtype),
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B: T.Tensor((K, N), dtype),
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C: T.Tensor((M, N), accum_dtype),
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Bias: T.Tensor((N), accum_dtype),
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with_bias: T.bool,
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):
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# Initialize Kernel Context
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
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A_shared = T.alloc_shared((block_M, block_K), dtype)
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B_shared = T.alloc_shared((block_N, block_K), dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
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# Copy tile of A
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T.copy(A[by * block_M, ko * block_K], A_shared)
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T.copy(B[bx * block_N, ko * block_K], B_shared)
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T.gemm(A_shared, B_shared, C_local, transpose_B=True)
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if with_bias:
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for i, j in T.Parallel(block_M, block_N):
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C_local[i, j] += Bias[bx * block_N + j]
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T.copy(C_local, C[by * block_M, bx * block_N])
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return main
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def run_test(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
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kernel = ptr_null_test(M, N, K, block_M, block_N, block_K, dtype, accum_dtype)
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a = torch.randn(M, K, device="cuda", dtype=map_torch_type(dtype))
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b = torch.randn(N, K, device="cuda", dtype=map_torch_type(dtype))
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c = torch.zeros(M, N, device="cuda", dtype=map_torch_type(accum_dtype))
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d = torch.randn(N, device="cuda", dtype=map_torch_type(accum_dtype))
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kernel(a, b, c, None, M, N, K, False)
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ref_no_bias = (a @ b.T).to(map_torch_type(accum_dtype))
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ref_with_bias = ref_no_bias + d
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torch.testing.assert_close(c, ref_no_bias, atol=1e-2, rtol=1e-2)
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kernel(a, b, c, d, M, N, K, True)
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torch.testing.assert_close(c, ref_with_bias, atol=1e-2, rtol=1e-2)
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kernel = tensor_null_test(M, N, K, block_M, block_N, block_K, dtype, accum_dtype)
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kernel(a, b, c, None, False)
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torch.testing.assert_close(c, ref_no_bias, atol=1e-2, rtol=1e-2)
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kernel(a, b, c, d, True)
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torch.testing.assert_close(c, ref_with_bias, atol=1e-2, rtol=1e-2)
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def test_nullptr():
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run_test(1024, 1024, 1024, 128, 128, 32)
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if __name__ == "__main__":
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tilelang.testing.main()
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