mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-21 06:40:23 +08:00
Lei Wang
3471904f68
* [Feature] Add CTypes JIT kernel support for dynamic shapes and multi-stream execution - Enhance CtypesKernelAdapter to handle dynamic symbolic shapes - Add support for multi-stream kernel execution in CTypes backend - Implement dynamic shape handling in test_tilelang_jit_gemm_ctypes.py - Add symbolic shape utility function in tilelang.language - Update profiler to improve flexibility in benchmark selection * Remove redundant thread binding in GEMM kernel implementations - Remove unnecessary `thread_binding` line in GEMM kernel functions - Clean up code in `examples/gemm/README.md` and `testing/python/kernel/test_tilelang_kernel_int4_gemm_mma.py` - Enhance code readability by removing redundant thread binding annotation * Fix indentation in int4 GEMM kernel test file - Correct indentation for function calls in `test_tilelang_kernel_int4_gemm_mma.py` - Remove extra indentation in `mma_emitter.ldmatrix_a()` and `mma_emitter.ldmatrix_b()` calls - Improve code formatting for better readability * [Feature] Add Cython JIT kernel support for dynamic shapes and multi-stream execution - Implement CythonKernelAdapter to handle dynamic symbolic shapes - Add support for multi-stream kernel execution in Cython backend - Create comprehensive test suite for Cython GEMM kernel in test_tilelang_jit_gemm_cython.py - Update JITKernel to include "cython" as a valid execution backend - Add Cython-specific wrapper and library generation modules - Update .gitignore to exclude Cython cache directory - Modify setup.py to include Cython source files in package data * lint fix * [Refactor] Replace JITKernel with compile() function for kernel compilation - Add new `compile()` function in tilelang/jit/__init__.py as a wrapper for JITKernel - Update multiple test files and examples to use `tilelang.compile()` instead of `tilelang.JITKernel()` - Modify kernel adapters to support optional kernel-only source retrieval - Update `__init__.py` to import the new `compile()` function - Improve kernel source retrieval for different execution backends * lint fix * remove debug print * Add C/C++ compiler utility module and update Cython JIT kernel support - Introduce new `tilelang/contrib/cc.py` module with cross-platform C/C++ compiler utilities - Add functions to detect and retrieve system C/C++ compilers - Implement cross-compilation and shared library creation support - Update Cython JIT kernel to validate C++ compiler availability - Modify Cython adapter to use detected C++ compiler for library generation * Refactor float8 dtype mapping in tensor utility module - Move float8_dtype_map inside adapt_torch2tvm function - Simplify global scope by localizing the dtype mapping - Maintain existing functionality for converting torch float8 tensors to TVM ndarray * Refactor float8 dtype mapping in tensor utility module - Move float8_dtype_map inside adapt_torch2tvm function - Simplify global scope by localizing the dtype mapping - Maintain existing functionality for converting torch float8 tensors to TVM ndarray * revert * Enhance Cython JIT adapter with Cython compiler detection - Add `get_cython_compiler()` function to dynamically locate Cython executable - Update Cython adapter to use detected Cython compiler instead of hardcoded command - Raise an exception if no Cython compiler is found - Update requirements.txt to specify minimum PyTorch version (>=2.2.0) * Fix Cython kernel wrapper stream handling and type annotations - Update stream parameter type to int64_t for better compatibility - Directly use torch.cuda.current_stream().cuda_stream instead of casting - Improve type safety and precision in Cython kernel wrapper
69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import tilelang
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import tilelang.testing
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import tilelang.language as T
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import torch
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def matmul(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.Buffer((M, K), dtype),
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B: T.Buffer((N, K), dtype),
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C: T.Buffer((M, N), dtype),
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):
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with T.Kernel(
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T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (
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bx,
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by,
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):
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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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# changing num_stages to 0 gives correct results
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for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=1):
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T.copy(A[by * block_M, ko * block_K], A_shared)
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for j, k in T.Parallel(block_N, block_K):
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B_shared[j, k] = B[bx * block_N + j, ko * block_K + k]
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T.gemm(A_shared, B_shared, C_local, transpose_B=True)
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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_gemm_pipeline_test(N, block_M=128, block_N=128, block_K=32):
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func = matmul(N, N, N, block_M, block_N, block_K)
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jit_kernel = tilelang.compile(func, out_idx=[2], target="cuda")
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torch.manual_seed(0)
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a = torch.randn(N, N, device="cuda", dtype=torch.float16)
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b = torch.randn(N, N, device="cuda", dtype=torch.float16)
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ref_c = a @ b.T
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c = jit_kernel(a, b)
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tilelang.testing.torch_assert_close(c, ref_c, rtol=1e-2, atol=0.2)
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def test_pipeline_large_matrix():
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"""Test pipeline stages with large matrix multiplication (8192x8192)"""
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run_gemm_pipeline_test(8192)
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def test_pipeline_small_matrix():
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"""Test pipeline stages with smaller matrix multiplication"""
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run_gemm_pipeline_test(1024)
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if __name__ == "__main__":
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tilelang.testing.main()
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