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* Add experimental IKET CUDA backend hooks * Keep IKET APIs under namespace * Use real Perfetto screenshot for IKET example * Show IKET events in Perfetto timeline screenshot * Add IKET validation overhead example * Remove IKET validation overhead example * Fix IKET lint issues * Move IKET integration to CUDA tools * Add tools documentation and fix workflow regressions --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
import tilelang
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import tilelang.language as T
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from tilelang.tools import Analyzer
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class _Device:
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compute_capability = "80"
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bandwidth = [750, 12080]
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@tilelang.jit
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def _gemm(A, B):
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M = N = K = 64
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block_M = block_N = block_K = 32
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A: T.Tensor((M, K), T.float16)
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B: T.Tensor((N, K), T.float16)
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C = T.empty((M, N), T.float16)
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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), T.float16)
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B_shared = T.alloc_shared((block_N, block_K), T.float16)
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C_local = T.alloc_fragment((block_M, block_N), T.float32)
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T.clear(C_local)
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for k in T.serial(T.ceildiv(K, block_K)):
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T.copy(A[by * block_M, k * block_K], A_shared)
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T.copy(B[bx * block_N, k * block_K], B_shared)
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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 C
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@tilelang.jit
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def _single_block_gemm(A, B):
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M = N = K = 32
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A: T.Tensor((M, K), T.float16)
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B: T.Tensor((N, K), T.float16)
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C = T.empty((M, N), T.float16)
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with T.Kernel(1, threads=128):
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A_shared = T.alloc_shared((M, K), T.float16)
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B_shared = T.alloc_shared((N, K), T.float16)
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C_local = T.alloc_fragment((M, N), T.float32)
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T.copy(A, A_shared)
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T.copy(B, B_shared)
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T.clear(C_local)
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T.gemm(A_shared, B_shared, C_local, transpose_B=True)
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T.copy(C_local, C)
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return C
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def test_analyzer_recognizes_tileop_names():
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result = Analyzer.analysis(_gemm.get_tir(), _Device())
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assert result.total_flops == 2 * 64 * 64 * 64
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assert result.total_global_bytes == 40_960
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def test_analyzer_resets_grid_extents_for_each_prim_func():
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analyzer = Analyzer(_single_block_gemm.get_tir(), _Device())
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analyzer.block_counts["blockIdx.y"] = 7
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result = analyzer.ir_pass().calculate()
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assert result.total_flops == 2 * 32 * 32 * 32
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assert result.total_global_bytes == 6_144
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