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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>
265 lines
7.6 KiB
Python
265 lines
7.6 KiB
Python
import pytest
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from tilelang import tvm as tvm
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import tilelang as tl
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import tilelang.language as T
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import tilelang.testing
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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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k_pack=1,
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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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vec_size = 4 * k_pack
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@T.prim_func
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def main(A: T.Tensor(A_shape, in_dtype), B: T.Tensor(B_shape, in_dtype), C: T.Tensor((M, N), out_dtype)):
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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, coalesced_width=vec_size)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared, coalesced_width=vec_size)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared, coalesced_width=vec_size)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared, coalesced_width=vec_size)
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T.gemm(A_shared, B_shared, C_local, trans_A, trans_B, k_pack=k_pack)
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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(
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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=0,
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num_threads=128,
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k_pack=1,
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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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k_pack=k_pack,
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)
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kernel = tl.compile(program, out_idx=[2])
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profiler = kernel.get_profiler()
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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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return (A @ B).to(torch.__getattribute__(out_dtype))
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profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
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@pytest.mark.parametrize(
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"trans_A, trans_B, k_pack",
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[
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(False, False, 1),
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(False, True, 1),
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(True, True, 1),
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(True, False, 1),
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(False, True, 2),
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],
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)
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@tilelang.testing.requires_rocm
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def test_gemm_f16f32f32_nt(trans_A, trans_B, k_pack):
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run_gemm(1024, 1024, 1024, trans_A, trans_B, "float16", "float32", "float32", 128, 128, 32, k_pack=k_pack)
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@pytest.mark.parametrize(
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"trans_A, trans_B, k_pack",
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[
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(False, False, 1),
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(False, True, 1),
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(True, True, 1),
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(True, False, 1),
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(False, True, 2),
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],
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)
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@tilelang.testing.requires_rocm
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def test_gemm_bf16f32f32_nt(trans_A, trans_B, k_pack):
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run_gemm(1024, 1024, 1024, trans_A, trans_B, "bfloat16", "float32", "float32", 128, 128, 32, k_pack=k_pack)
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@pytest.mark.parametrize(
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"trans_A, trans_B, k_pack",
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[
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(False, False, 1),
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(False, True, 1),
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(True, True, 1),
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(True, False, 1),
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(False, True, 2),
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],
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)
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@tilelang.testing.requires_rocm
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def test_gemm_bf16bf16f32(trans_A, trans_B, k_pack):
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run_gemm(1024, 1024, 1024, trans_A, trans_B, "bfloat16", "bfloat16", "float32", 128, 128, 32, k_pack=k_pack)
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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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k_pack=1,
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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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vec_size = 4 * k_pack
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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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A_local = T.alloc_fragment(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, coalesced_width=vec_size)
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T.copy(A_shared, A_local)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared, coalesced_width=vec_size)
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T.copy(A_shared, A_local)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared, coalesced_width=vec_size)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared, coalesced_width=vec_size)
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T.gemm(A_local, B_shared, C_local, trans_A, trans_B, k_pack=k_pack)
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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=0,
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num_threads=128,
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k_pack=1,
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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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k_pack=k_pack,
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)
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kernel = tl.compile(program, out_idx=[2])
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profiler = kernel.get_profiler()
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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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return (A @ B).to(torch.__getattribute__(out_dtype))
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profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
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# @tilelang.testing.requires_rocm
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# def test_gemm_rs_f16f32f32_nt():
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# run_gemm_rs(1024, 1024, 1024, False, False, "float16", "float32", "float32", 128, 128, 32)
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# run_gemm_rs(1024, 1024, 1024, False, True, "float16", "float32", "float32", 128, 128, 32)
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# run_gemm_rs(1024, 1024, 1024, True, True, "float16", "float32", "float32", 128, 128, 32)
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# run_gemm_rs(1024, 1024, 1024, True, False, "float16", "float32", "float32", 128, 128, 32)
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# @tilelang.testing.requires_rocm
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# def test_gemm_rs_bf16f32f32_nt():
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# run_gemm_rs(1024, 1024, 1024, False, False, "bfloat16", "float32", "float32", 128, 128, 32)
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# run_gemm_rs(1024, 1024, 1024, False, True, "bfloat16", "float32", "float32", 128, 128, 32)
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# run_gemm_rs(1024, 1024, 1024, True, True, "bfloat16", "float32", "float32", 128, 128, 32)
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# run_gemm_rs(1024, 1024, 1024, True, False, "bfloat16", "float32", "float32", 128, 128, 32)
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# @tilelang.testing.requires_rocm
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# def test_gemm_rs_bf16bf16f32_nt():
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# run_gemm_rs(1024, 1024, 1024, False, False, "bfloat16", "bfloat16", "float32", 128, 128, 32)
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# run_gemm_rs(1024, 1024, 1024, False, True, "bfloat16", "bfloat16", "float32", 128, 128, 32)
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# run_gemm_rs(1024, 1024, 1024, True, True, "bfloat16", "bfloat16", "float32", 128, 128, 32)
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# run_gemm_rs(1024, 1024, 1024, True, False, "bfloat16", "bfloat16", "float32", 128, 128, 32)
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if __name__ == "__main__":
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tilelang.testing.main()
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