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>
635 lines
22 KiB
Python
635 lines
22 KiB
Python
import pytest
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from tilelang import tvm as tvm
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from tilelang.utils.sparse import compress, randn_semi_sparse, randint_semi_sparse
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from tilelang.utils.tensor import torch_assert_close, map_torch_type
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from tilelang.layout import make_cutlass_metadata_layout
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from tilelang.intrinsics.mma_sp_macro_generator import SparseTensorCoreIntrinEmitter
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import tilelang.testing
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import torch
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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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metadata_dtype,
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E_factor,
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num_stages,
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threads,
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):
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A_sparse_shape = (M, K // 2) if not trans_A else (K // 2, M)
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B_shape = (N, K) if trans_B else (K, N)
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A_shared_shape = (block_M, block_K // 2) if not trans_A else (block_K // 2, block_M)
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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_sparse: T.Tensor(A_sparse_shape, in_dtype),
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E: T.Tensor((M, K // E_factor), metadata_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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E_shared = T.alloc_shared((block_M, block_K // E_factor), metadata_dtype)
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C_frag = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.annotate_layout(
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{
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E: make_cutlass_metadata_layout(E, mma_dtype=in_dtype, arch="8.0"),
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E_shared: make_cutlass_metadata_layout(E_shared, mma_dtype=in_dtype, arch="8.0"),
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}
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)
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T.clear(C_frag)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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T.copy(E[by * block_M, k * block_K // E_factor], E_shared)
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if trans_A:
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T.copy(A_sparse[k * block_K // 2, by * block_M], A_shared)
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else:
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T.copy(A_sparse[by * block_M, k * block_K // 2], 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_sp_v2(A_shared, E_shared, B_shared, C_frag, trans_A, trans_B)
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T.copy(C_frag, 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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metadata_dtype = "int32" if ("8" in in_dtype) else "int16"
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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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metadata_dtype,
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SparseTensorCoreIntrinEmitter.E_FACTOR_MAP[in_dtype][metadata_dtype], # E_factor
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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=[3],
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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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A, B = generate_dense_input(M, N, K, trans_A, trans_B, in_dtype)
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A_sparse, E = compress(A, transposed=trans_A, block_k=block_K, arch="8.0")
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C_sp = kernel(A_sparse, E, B)
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def _matmul(A, B):
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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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A = A.to(torch.float32)
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B = B.to(torch.float32)
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return torch.matmul(A, B)
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C = _matmul(A, B)
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torch_assert_close(
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C_sp.to(map_torch_type(out_dtype)).to(torch.float32),
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C.to(map_torch_type(out_dtype)).to(torch.float32),
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rtol=1e-3,
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atol=1e-3,
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base_name="tilelang_sp",
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ref_name="ref_dense",
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)
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print("pass")
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def generate_dense_input(M, N, K, trans_A, trans_B, in_dtype):
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is_8bit = "8" in in_dtype
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is_unsigned = "uint" in in_dtype
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is_int = "int" in in_dtype
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if is_int:
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if is_8bit:
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low, high = (0, 4) if is_unsigned else (-2, 2)
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else:
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low, high = (0, 128) if is_unsigned else (-64, 64)
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A = randint_semi_sparse(M, K, low=low, high=high, dtype=map_torch_type(in_dtype), device="cuda", transposed=trans_A)
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B = torch.randint(size=(N, K) if trans_B else (K, N), low=low, high=high, dtype=map_torch_type(in_dtype), device="cuda")
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else:
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A = randn_semi_sparse(M, K, dtype=map_torch_type(in_dtype), device="cuda", transposed=trans_A)
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B = torch.randn((N, K) if trans_B else (K, N), device="cuda", dtype=torch.float32).to(map_torch_type(in_dtype))
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return A, B
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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", "float", 128, 128, 32, 2, 128),
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(512, 1024, 768, False, False, "float16", "float16", "float", 128, 128, 32, 2, 128),
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(512, 1024, 768, True, False, "float16", "float16", "float", 128, 128, 32, 2, 128),
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(512, 1024, 768, True, True, "float16", "float16", "float", 128, 128, 32, 2, 128),
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(128, 8, 64, False, True, "float16", "float16", "float", 128, 8, 32, 0, 128),
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(128, 128, 128, False, True, "int8", "int32", "int32", 128, 128, 64, 2, 128),
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(128, 128, 128, False, False, "int8", "int8", "int32", 128, 128, 64, 2, 128),
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(128, 128, 128, True, False, "int8", "int8", "int32", 128, 128, 64, 2, 128),
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(128, 128, 128, True, True, "int8", "int8", "int32", 128, 128, 64, 2, 128),
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(128, 128, 128, False, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 64, 2, 128),
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(128, 128, 128, True, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 64, 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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metadata_dtype,
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E_factor,
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num_stages,
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threads,
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):
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A_sparse_shape = (M, K // 2) if not trans_A else (K // 2, M)
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B_shape = (N, K) if trans_B else (K, N)
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A_shared_shape = (block_M, block_K // 2) if not trans_A else (block_K // 2, block_M)
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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_sparse: T.Tensor(A_sparse_shape, in_dtype),
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E: T.Tensor((M, K // E_factor), metadata_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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E_shared = T.alloc_shared((block_M, block_K // E_factor), metadata_dtype)
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A_frag = T.alloc_fragment(A_frag_shape, in_dtype)
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C_frag = T.alloc_fragment((block_M, block_N), accum_dtype)
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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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E: make_cutlass_metadata_layout(E, mma_dtype=in_dtype, arch="8.0"),
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E_shared: make_cutlass_metadata_layout(E_shared, mma_dtype=in_dtype, arch="8.0"),
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}
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)
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T.clear(C_frag)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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T.copy(E[by * block_M, k * block_K // E_factor], E_shared)
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if trans_A:
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T.copy(A_sparse[k * block_K // 2, by * block_M], A_shared)
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else:
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T.copy(A_sparse[by * block_M, k * block_K // 2], 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_sp_v2(A_frag, E_shared, B_shared, C_frag, trans_A, trans_B)
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T.copy(C_frag, 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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metadata_dtype = "int32" if ("8" in in_dtype) else "int16"
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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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metadata_dtype,
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SparseTensorCoreIntrinEmitter.E_FACTOR_MAP[in_dtype][metadata_dtype], # E_factor
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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=[3],
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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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A, B = generate_dense_input(M, N, K, trans_A, trans_B, in_dtype)
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A_sparse, E = compress(A, transposed=trans_A, block_k=block_K, arch="8.0")
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C_sp = kernel(A_sparse, E, B)
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def _matmul(A, B):
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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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A = A.to(torch.float32)
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B = B.to(torch.float32)
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return torch.matmul(A, B)
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C = _matmul(A, B)
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torch_assert_close(
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C_sp.to(map_torch_type(out_dtype)).to(torch.float32),
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C.to(map_torch_type(out_dtype)).to(torch.float32),
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rtol=1e-3,
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atol=1e-3,
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base_name="tilelang_sp",
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ref_name="ref_dense",
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)
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print("pass")
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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", "float", 128, 256, 32, 2, 128),
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(512, 1024, 768, False, True, "float16", "float16", "float", 128, 256, 32, 2, 128),
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(512, 1024, 768, True, False, "float16", "float16", "float", 128, 256, 32, 2, 128),
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(512, 1024, 768, True, True, "float16", "float16", "float", 128, 256, 32, 2, 128),
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(128, 8, 64, False, True, "float16", "float16", "float", 128, 8, 32, 0, 128),
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(128, 128, 128, False, True, "int8", "int8", "int32", 128, 128, 64, 2, 128),
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(128, 128, 128, False, False, "int8", "int8", "int32", 128, 128, 64, 2, 128),
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(128, 128, 128, True, False, "int8", "int8", "int32", 128, 128, 64, 2, 128),
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(128, 128, 128, True, True, "int8", "int8", "int32", 128, 128, 64, 2, 128),
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(128, 128, 128, True, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 64, 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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metadata_dtype,
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E_factor,
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num_stages,
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threads,
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):
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A_sparse_shape = (M, K // 2) if not trans_A else (K // 2, M)
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B_shape = (N, K) if trans_B else (K, N)
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A_shared_shape = (block_M, block_K // 2) if not trans_A else (block_K // 2, block_M)
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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_sparse: T.Tensor(A_sparse_shape, in_dtype),
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E: T.Tensor((M, K // E_factor), metadata_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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E_shared = T.alloc_shared((block_M, block_K // E_factor), metadata_dtype)
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B_frag = T.alloc_fragment(B_frag_shape, in_dtype)
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C_frag = T.alloc_fragment((block_M, block_N), accum_dtype)
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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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E: make_cutlass_metadata_layout(E, mma_dtype=in_dtype, arch="8.0"),
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E_shared: make_cutlass_metadata_layout(E_shared, mma_dtype=in_dtype, arch="8.0"),
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}
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)
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T.clear(C_frag)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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T.copy(E[by * block_M, k * block_K // E_factor], E_shared)
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if trans_A:
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T.copy(A_sparse[k * block_K // 2, by * block_M], A_shared)
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else:
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T.copy(A_sparse[by * block_M, k * block_K // 2], 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_sp_v2(A_shared, E_shared, B_frag, C_frag, trans_A, trans_B)
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T.copy(C_frag, 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,
|
|
block_K,
|
|
num_stages=3,
|
|
num_threads=128,
|
|
):
|
|
metadata_dtype = "int32" if ("8" in in_dtype) else "int16"
|
|
program = matmul_sr(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
metadata_dtype,
|
|
SparseTensorCoreIntrinEmitter.E_FACTOR_MAP[in_dtype][metadata_dtype], # E_factor
|
|
num_stages,
|
|
num_threads,
|
|
)
|
|
|
|
kernel = tilelang.compile(
|
|
program,
|
|
out_idx=[3],
|
|
pass_configs={
|
|
tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
|
|
tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
|
|
},
|
|
)
|
|
A, B = generate_dense_input(M, N, K, trans_A, trans_B, in_dtype)
|
|
A_sparse, E = compress(A, transposed=trans_A, block_k=block_K, arch="8.0")
|
|
C_sp = kernel(A_sparse, E, B)
|
|
|
|
def _matmul(A, B):
|
|
if trans_A:
|
|
A = A.T
|
|
if trans_B:
|
|
B = B.T
|
|
A = A.to(torch.float32)
|
|
B = B.to(torch.float32)
|
|
return torch.matmul(A, B)
|
|
|
|
C = _matmul(A, B)
|
|
|
|
torch_assert_close(
|
|
C_sp.to(map_torch_type(out_dtype)).to(torch.float32),
|
|
C.to(map_torch_type(out_dtype)).to(torch.float32),
|
|
rtol=1e-3,
|
|
atol=1e-3,
|
|
base_name="tilelang_sp",
|
|
ref_name="ref_dense",
|
|
)
|
|
print("pass")
|
|
|
|
|
|
@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", "float", 128, 256, 32, 2, 128),
|
|
(512, 1024, 768, False, True, "float16", "float16", "float", 128, 256, 32, 2, 128),
|
|
(512, 1024, 768, True, False, "float16", "float16", "float", 128, 256, 32, 2, 128),
|
|
(512, 1024, 768, True, True, "float16", "float16", "float", 128, 256, 32, 2, 128),
|
|
(128, 8, 64, False, True, "float16", "float16", "float", 128, 8, 32, 0, 128),
|
|
(128, 128, 128, False, True, "int8", "int8", "int32", 128, 128, 128, 2, 128),
|
|
(128, 128, 128, False, False, "int8", "int8", "int32", 128, 128, 128, 2, 128),
|
|
(128, 128, 128, True, False, "int8", "int8", "int32", 128, 128, 64, 2, 128),
|
|
(128, 128, 128, True, True, "int8", "int8", "int32", 128, 128, 64, 2, 128),
|
|
(128, 128, 128, True, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 64, 2, 128),
|
|
],
|
|
)
|
|
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):
|
|
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)
|
|
|
|
|
|
def matmul_rr(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
accum_dtype,
|
|
metadata_dtype,
|
|
E_factor,
|
|
num_stages,
|
|
threads,
|
|
):
|
|
A_sparse_shape = (M, K // 2) if not trans_A else (K // 2, M)
|
|
B_shape = (N, K) if trans_B else (K, N)
|
|
A_shared_shape = (block_M, block_K // 2) if not trans_A else (block_K // 2, block_M)
|
|
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_sparse: T.Tensor(A_sparse_shape, in_dtype),
|
|
E: T.Tensor((M, K // E_factor), metadata_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)
|
|
E_shared = T.alloc_shared((block_M, block_K // E_factor), metadata_dtype)
|
|
A_frag = T.alloc_fragment(A_frag_shape, in_dtype)
|
|
B_frag = T.alloc_fragment(B_frag_shape, in_dtype)
|
|
C_frag = T.alloc_fragment((block_M, block_N), accum_dtype)
|
|
T.annotate_layout(
|
|
{
|
|
A_shared: tilelang.layout.make_swizzled_layout(A_shared),
|
|
B_shared: tilelang.layout.make_swizzled_layout(B_shared),
|
|
E: make_cutlass_metadata_layout(E, mma_dtype=in_dtype, arch="8.0"),
|
|
E_shared: make_cutlass_metadata_layout(E_shared, mma_dtype=in_dtype, arch="8.0"),
|
|
}
|
|
)
|
|
T.clear(C_frag)
|
|
for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
|
|
T.copy(E[by * block_M, k * block_K // E_factor], E_shared)
|
|
if trans_A:
|
|
T.copy(A_sparse[k * block_K // 2, by * block_M], A_shared)
|
|
else:
|
|
T.copy(A_sparse[by * block_M, k * block_K // 2], 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_sp_v2(A_frag, E_shared, B_frag, C_frag, trans_A, trans_B)
|
|
T.copy(C_frag, 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,
|
|
):
|
|
metadata_dtype = "int32" if ("8" in in_dtype) else "int16"
|
|
program = matmul_rr(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
trans_A,
|
|
trans_B,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
metadata_dtype,
|
|
SparseTensorCoreIntrinEmitter.E_FACTOR_MAP[in_dtype][metadata_dtype], # E_factor
|
|
num_stages,
|
|
num_threads,
|
|
)
|
|
|
|
kernel = tilelang.compile(
|
|
program,
|
|
out_idx=[3],
|
|
pass_configs={
|
|
tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
|
|
tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
|
|
},
|
|
)
|
|
A, B = generate_dense_input(M, N, K, trans_A, trans_B, in_dtype)
|
|
A_sparse, E = compress(A, transposed=trans_A, block_k=block_K, arch="8.0")
|
|
C_sp = kernel(A_sparse, E, B)
|
|
|
|
def _matmul(A, B):
|
|
if trans_A:
|
|
A = A.T
|
|
if trans_B:
|
|
B = B.T
|
|
A = A.to(torch.float32)
|
|
B = B.to(torch.float32)
|
|
return torch.matmul(A, B)
|
|
|
|
C = _matmul(A, B)
|
|
|
|
torch_assert_close(
|
|
C_sp.to(map_torch_type(out_dtype)).to(torch.float32),
|
|
C.to(map_torch_type(out_dtype)).to(torch.float32),
|
|
rtol=1e-3,
|
|
atol=1e-3,
|
|
base_name="tilelang_sp",
|
|
ref_name="ref_dense",
|
|
)
|
|
print("pass")
|
|
|
|
|
|
@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", "float", 128, 256, 32, 2, 128),
|
|
(512, 1024, 768, False, True, "float16", "float16", "float", 128, 256, 32, 2, 128),
|
|
(512, 1024, 768, True, False, "float16", "float16", "float", 128, 256, 32, 2, 128),
|
|
(512, 1024, 768, True, True, "float16", "float16", "float", 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", "float", 128, 8, 32, 2, 128),
|
|
(128, 8, 128, False, True, "int8", "int8", "int32", 128, 8, 64, 2, 128),
|
|
(128, 128, 128, False, True, "int8", "int8", "int32", 128, 128, 64, 2, 128),
|
|
(128, 128, 128, False, False, "int8", "int8", "int32", 128, 128, 64, 2, 128),
|
|
(128, 128, 128, True, False, "int8", "int8", "int32", 128, 128, 64, 2, 128),
|
|
(128, 128, 128, True, True, "int8", "int8", "int32", 128, 128, 64, 2, 128),
|
|
(128, 128, 128, True, True, "float8_e5m2", "float8_e5m2", "float32", 128, 128, 64, 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()
|