mirror of
https://github.com/tile-ai/tilelang.git
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Kurisandcoderabbitai[bot]
0b6336b583
* Refactor GEMM tests to use parameterized pytest fixtures - Converted multiple test cases for GEMM operations in `test_tilelang_tilelibrary_gemm_sp.py` to use `pytest.mark.parametrize` for better maintainability and readability. - Similar refactoring applied to `test_tilelang_tilelibrary_gemm_sp_v2.py`, consolidating test cases for `run_gemm_ss`, `run_gemm_rs`, `run_gemm_sr`, and `run_gemm_rr` into parameterized tests. - This change reduces code duplication and enhances the clarity of test configurations. * Update testing/python/amd/test_tilelang_gemm_mfma_preshuffle.py Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> --------- Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
546 lines
18 KiB
Python
546 lines
18 KiB
Python
from tilelang import tvm as tvm
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import tilelang.testing
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import pytest
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def matmul(
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M,
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N,
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K,
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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accum_dtype,
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num_stages,
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threads,
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):
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A_shape = (K, M) if trans_A else (M, K)
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B_shape = (N, K) if trans_B else (K, N)
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A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
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B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
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import tilelang.language as T
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@T.prim_func
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def main(
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A: T.Tensor(A_shape, in_dtype),
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B: T.Tensor(B_shape, in_dtype),
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C: T.Tensor((M, N), out_dtype),
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):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
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A_shared = T.alloc_shared(A_shared_shape, in_dtype)
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B_shared = T.alloc_shared(B_shared_shape, in_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 k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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if trans_A:
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T.copy(A[k * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm_v2(A_shared, B_shared, C_local, trans_A, trans_B)
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# T.gemm(A_shared, B_shared, C_local, trans_A, trans_B)
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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_ss(
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M,
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N,
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K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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block_M,
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block_N,
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block_K,
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num_stages=3,
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num_threads=128,
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):
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program = matmul(
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M,
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N,
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K,
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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num_stages,
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num_threads,
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)
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kernel = tilelang.compile(
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program,
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out_idx=[2],
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pass_configs={
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tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
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tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
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},
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)
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profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
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def ref_program(A, B):
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import torch
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if trans_A:
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A = A.T
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if trans_B:
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B = B.T
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.__getattribute__(out_dtype))
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return C
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profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
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@pytest.mark.skip(reason="Temporarily disabling until GEMM SS issues are resolved")
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@pytest.mark.parametrize(
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"M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages, num_threads",
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[
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(512, 1024, 768, False, True, "float16", "float16", "float16", 128, 128, 32, 2, 128),
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(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 128, 32, 2, 128),
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(512, 1024, 768, True, False, "float16", "float16", "float16", 128, 128, 32, 2, 128),
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(512, 1024, 768, True, True, "float16", "float16", "float16", 128, 128, 32, 2, 128),
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(128, 8, 32, False, True, "float16", "float16", "float16", 128, 8, 32, 0, 128),
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(128, 128, 128, False, True, "int8", "int8", "int32", 128, 128, 32, 2, 128),
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(128, 128, 128, False, False, "int8", "int8", "int32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, False, "int8", "int8", "int32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, True, "int8", "int8", "int32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 32, 2, 128),
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(128, 128, 128, False, False, "float", "float", "float32", 128, 128, 32, 2, 128),
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(128, 128, 128, False, True, "float", "float", "float32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, False, "float", "float", "float32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, True, "float", "float", "float32", 128, 128, 32, 2, 128),
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],
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)
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def test_gemm_ss(M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages, num_threads):
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run_gemm_ss(M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages, num_threads)
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def matmul_rs(
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M,
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N,
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K,
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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accum_dtype,
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num_stages,
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threads,
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):
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A_shape = (K, M) if trans_A else (M, K)
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B_shape = (N, K) if trans_B else (K, N)
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A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
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B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
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A_frag_shape = A_shared_shape
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import tilelang.language as T
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@T.prim_func
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def main(
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A: T.Tensor(A_shape, in_dtype),
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B: T.Tensor(B_shape, in_dtype),
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C: T.Tensor((M, N), out_dtype),
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):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
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A_shared = T.alloc_shared(A_shared_shape, in_dtype)
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B_shared = T.alloc_shared(B_shared_shape, in_dtype)
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A_frag = T.alloc_fragment(A_frag_shape, in_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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T.annotate_layout(
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{
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A_shared: tilelang.layout.make_swizzled_layout(A_shared),
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}
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)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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if trans_A:
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T.copy(A[k * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.copy(A_shared, A_frag)
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T.gemm_v2(A_frag, B_shared, C_local, trans_A, trans_B)
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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_rs(
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M,
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N,
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K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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block_M,
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block_N,
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block_K,
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num_stages=3,
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num_threads=128,
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):
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program = matmul_rs(
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M,
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N,
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K,
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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num_stages,
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num_threads,
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)
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kernel = tilelang.compile(
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program,
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out_idx=[2],
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pass_configs={
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tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
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tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
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},
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)
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profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
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def ref_program(A, B):
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import torch
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if trans_A:
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A = A.T
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if trans_B:
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B = B.T
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.__getattribute__(out_dtype))
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return C
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profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
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@pytest.mark.skip(reason="Temporarily disabling until GEMM RS issues are resolved")
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@pytest.mark.parametrize(
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"M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages, num_threads",
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[
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(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2, 128),
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(512, 1024, 768, False, True, "float16", "float16", "float16", 128, 256, 32, 2, 128),
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(512, 1024, 768, True, False, "float16", "float16", "float16", 128, 256, 32, 2, 128),
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(512, 1024, 768, True, True, "float16", "float16", "float16", 128, 256, 32, 2, 128),
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(128, 8, 32, False, True, "float16", "float16", "float16", 128, 8, 32, 0, 128),
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(128, 128, 128, False, True, "int8", "int8", "int32", 128, 128, 32, 2, 128),
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(128, 128, 128, False, False, "int8", "int8", "int32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, False, "int8", "int8", "int32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, True, "int8", "int8", "int32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 32, 2, 128),
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(128, 128, 128, False, False, "float", "float", "float32", 128, 128, 32, 2, 128),
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(128, 128, 128, False, True, "float", "float", "float32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, False, "float", "float", "float32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, True, "float", "float", "float32", 128, 128, 32, 2, 128),
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],
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)
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def test_gemm_rs(M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages, num_threads):
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run_gemm_rs(M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages, num_threads)
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def matmul_sr(
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M,
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N,
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K,
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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accum_dtype,
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num_stages,
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threads,
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):
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A_shape = (K, M) if trans_A else (M, K)
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B_shape = (N, K) if trans_B else (K, N)
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A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
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B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
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B_frag_shape = B_shared_shape
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import tilelang.language as T
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@T.prim_func
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def main(
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A: T.Tensor(A_shape, in_dtype),
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B: T.Tensor(B_shape, in_dtype),
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C: T.Tensor((M, N), out_dtype),
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):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
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A_shared = T.alloc_shared(A_shared_shape, in_dtype)
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B_shared = T.alloc_shared(B_shared_shape, in_dtype)
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B_frag = T.alloc_fragment(B_frag_shape, in_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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T.annotate_layout(
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{
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B_shared: tilelang.layout.make_swizzled_layout(B_shared),
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}
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)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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if trans_A:
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T.copy(A[k * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.copy(B_shared, B_frag)
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T.gemm_v2(A_shared, B_frag, C_local, trans_A, trans_B)
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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_sr(
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M,
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N,
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K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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block_M,
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block_N,
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block_K,
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num_stages=3,
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num_threads=128,
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):
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program = matmul_sr(
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M,
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N,
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K,
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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num_stages,
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num_threads,
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)
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kernel = tilelang.compile(
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program,
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out_idx=[2],
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pass_configs={
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tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
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tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
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},
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)
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profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
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def ref_program(A, B):
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import torch
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if trans_A:
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A = A.T
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if trans_B:
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B = B.T
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.__getattribute__(out_dtype))
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return C
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profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
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|
|
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@pytest.mark.parametrize(
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"M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages, num_threads",
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|
[
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(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2, 128),
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|
(512, 1024, 768, False, True, "float16", "float16", "float16", 128, 256, 32, 2, 128),
|
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(512, 1024, 768, True, False, "float16", "float16", "float16", 128, 256, 32, 2, 128),
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(512, 1024, 768, True, True, "float16", "float16", "float16", 128, 256, 32, 2, 128),
|
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(128, 8, 32, False, True, "float16", "float16", "float16", 128, 8, 32, 0, 128),
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(128, 128, 32, False, True, "int8", "int8", "int32", 128, 128, 32, 2, 128),
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(128, 128, 32, False, False, "int8", "int8", "int32", 128, 128, 32, 2, 128),
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(128, 128, 32, True, False, "int8", "int8", "int32", 128, 128, 32, 2, 128),
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(128, 128, 32, True, True, "int8", "int8", "int32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 32, 2, 128),
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(128, 128, 128, False, False, "float", "float", "float32", 128, 128, 32, 2, 128),
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(128, 128, 128, False, True, "float", "float", "float32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, False, "float", "float", "float32", 128, 128, 32, 2, 128),
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(128, 128, 128, True, True, "float", "float", "float32", 128, 128, 32, 2, 128),
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],
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)
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def test_gemm_sr(M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages, num_threads):
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run_gemm_sr(M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages, num_threads)
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def matmul_rr(
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M,
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N,
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K,
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block_M,
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block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
accum_dtype,
|
|
num_stages,
|
|
threads,
|
|
):
|
|
A_shape = (K, M) if trans_A else (M, K)
|
|
B_shape = (N, K) if trans_B else (K, N)
|
|
A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
|
|
B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
|
|
A_frag_shape = A_shared_shape
|
|
B_frag_shape = B_shared_shape
|
|
|
|
import tilelang.language as T
|
|
|
|
@T.prim_func
|
|
def main(
|
|
A: T.Tensor(A_shape, in_dtype),
|
|
B: T.Tensor(B_shape, in_dtype),
|
|
C: T.Tensor((M, N), out_dtype),
|
|
):
|
|
with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
|
|
A_shared = T.alloc_shared(A_shared_shape, in_dtype)
|
|
B_shared = T.alloc_shared(B_shared_shape, in_dtype)
|
|
A_frag = T.alloc_fragment(A_frag_shape, in_dtype)
|
|
B_frag = T.alloc_fragment(B_frag_shape, in_dtype)
|
|
C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
|
|
T.clear(C_local)
|
|
T.annotate_layout(
|
|
{
|
|
A_shared: tilelang.layout.make_swizzled_layout(A_shared),
|
|
B_shared: tilelang.layout.make_swizzled_layout(B_shared),
|
|
}
|
|
)
|
|
for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
|
|
if trans_A:
|
|
T.copy(A[k * block_K, by * block_M], A_shared)
|
|
else:
|
|
T.copy(A[by * block_M, k * block_K], A_shared)
|
|
if trans_B:
|
|
T.copy(B[bx * block_N, k * block_K], B_shared)
|
|
else:
|
|
T.copy(B[k * block_K, bx * block_N], B_shared)
|
|
T.copy(A_shared, A_frag)
|
|
T.copy(B_shared, B_frag)
|
|
T.gemm_v2(A_frag, B_frag, C_local, trans_A, trans_B)
|
|
T.copy(C_local, C[by * block_M, bx * block_N])
|
|
|
|
return main
|
|
|
|
|
|
def run_gemm_rr(
|
|
M,
|
|
N,
|
|
K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
num_stages=3,
|
|
num_threads=128,
|
|
):
|
|
program = matmul_rr(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
num_stages,
|
|
num_threads,
|
|
)
|
|
|
|
kernel = tilelang.compile(
|
|
program,
|
|
out_idx=[2],
|
|
pass_configs={
|
|
tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
|
|
tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
|
|
},
|
|
)
|
|
profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
|
|
|
|
def ref_program(A, B):
|
|
import torch
|
|
|
|
if trans_A:
|
|
A = A.T
|
|
if trans_B:
|
|
B = B.T
|
|
C = torch.matmul(A.to(torch.float), B.to(torch.float))
|
|
C = C.to(torch.__getattribute__(out_dtype))
|
|
return C
|
|
|
|
profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages, num_threads",
|
|
[
|
|
(512, 1024, 768, False, False, "float16", "float16", "float16", 128, 256, 32, 2, 128),
|
|
(512, 1024, 768, False, True, "float16", "float16", "float16", 128, 256, 32, 2, 128),
|
|
(512, 1024, 768, True, False, "float16", "float16", "float16", 128, 256, 32, 2, 128),
|
|
(512, 1024, 768, True, True, "float16", "float16", "float16", 128, 256, 32, 2, 128),
|
|
(512, 1024, 768, False, True, "bfloat16", "bfloat16", "float", 128, 256, 32, 2, 128),
|
|
(128, 8, 128, False, True, "float16", "float16", "float16", 128, 8, 32, 2, 128),
|
|
(128, 8, 128, False, True, "int8", "int8", "int32", 128, 8, 32, 2, 128),
|
|
(128, 128, 128, False, True, "int8", "int8", "int32", 128, 128, 32, 2, 128),
|
|
(128, 128, 128, False, False, "int8", "int8", "int32", 128, 128, 32, 2, 128),
|
|
(128, 128, 128, True, False, "int8", "int8", "int32", 128, 128, 32, 2, 128),
|
|
(128, 128, 128, True, True, "int8", "int8", "int32", 128, 128, 32, 2, 128),
|
|
(128, 128, 128, True, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 32, 2, 128),
|
|
(128, 128, 128, False, False, "float", "float", "float32", 128, 128, 32, 2, 128),
|
|
(128, 128, 128, False, True, "float", "float", "float32", 128, 128, 32, 2, 128),
|
|
(128, 128, 128, True, False, "float", "float", "float32", 128, 128, 32, 2, 128),
|
|
(128, 128, 128, True, True, "float", "float", "float32", 128, 128, 32, 2, 128),
|
|
],
|
|
)
|
|
def test_gemm_rr(M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages, num_threads):
|
|
run_gemm_rr(M, N, K, trans_A, trans_B, in_dtype, out_dtype, dtypeAccum, block_M, block_N, block_K, num_stages, num_threads)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tilelang.testing.main()
|