mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-21 06:40:23 +08:00
720 lines
20 KiB
Python
720 lines
20 KiB
Python
import tilelang
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import tilelang.language as T
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import torch
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import tilelang.testing
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import pytest
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tilelang.testing.set_random_seed()
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def _require_cuda_tensor(shape, dtype=torch.float32):
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if not torch.cuda.is_available():
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pytest.skip("CUDA not available")
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try:
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return torch.randn(*shape, device="cuda", dtype=dtype)
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except RuntimeError as err:
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pytest.skip(f"CUDA runtime unavailable: {err}")
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"""
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Nested Parallel cases:
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T.Parallel
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T.Parallel
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Rule:
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- continuous parallels is allowed and will be merged into one T.Parallel.
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- Non-continuous (e.g. with some statements in the outer-loop) are forbidden.
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"""
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@tilelang.jit(out_idx=[1])
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def nested_continuous_parallels(length=256, block=16, dtype="float32"):
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@T.prim_func
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def main(
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A: T.Tensor((length,), dtype),
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B: T.Tensor((length,), dtype),
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):
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with T.Kernel(1, threads=length) as _:
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for i in T.Parallel(length // block):
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for j in T.Parallel(block):
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B[i * block + j] = A[i * block + j] + 1.0
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return main
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@tilelang.jit(out_idx=[1])
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def nested_triple_continuous_parallels(length=256, block1=8, block2=2, dtype="float32"):
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@T.prim_func
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def main(
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A: T.Tensor((length,), dtype),
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B: T.Tensor((length,), dtype),
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):
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with T.Kernel(1, threads=length) as _:
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for i in T.Parallel(length // block1 // block2):
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for j in T.Parallel(block1):
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for k in T.Parallel(block2):
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B[i * block1 * block2 + j * block2 + k] = A[i * block1 * block2 + j * block2 + k] + 1.0
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return main
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@tilelang.jit(out_idx=[1])
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def nested_noncontinuous_parallels(length=256, block=16, dtype="float32"):
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@T.prim_func
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def main(
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A: T.Tensor((length,), dtype),
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B: T.Tensor((length,), dtype),
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):
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with T.Kernel(1, threads=length) as _:
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for i in T.Parallel(length // block):
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B[i] = 0
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for j in T.Parallel(block):
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B[i * block + j] = A[i * block + j] + 1.0
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return main
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def test_nested_parallels():
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kernel1 = nested_continuous_parallels(length=256, block=16)
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kernel2 = nested_triple_continuous_parallels(length=256, block1=8, block2=2)
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data = _require_cuda_tensor((256,), torch.float32)
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result1 = kernel1(data)
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result2 = kernel2(data)
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torch.testing.assert_close(result1, data + 1.0, atol=1e-5, rtol=1e-5)
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torch.testing.assert_close(result2, data + 1.0, atol=1e-5, rtol=1e-5)
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# This is invalid
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with pytest.raises(ValueError):
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nested_noncontinuous_parallels(length=256, block=16)
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"""
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Nested Pipeline cases:
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T.Pipeline
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T.Pipeline
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is OK.
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"""
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def matmul_nested_pipelines(
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M, N, K, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, accum_dtype, threads, order, stage, extra_pipeline_repeats
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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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for _ in T.Pipelined(extra_pipeline_repeats):
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T.clear(C_local)
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for k in T.Pipelined(T.ceildiv(K, block_K), order=order, stage=stage):
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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(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_nested_pipelines(
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order,
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stage,
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extra_pipeline_repeats,
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):
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M = 1024
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N = 1024
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K = 1024
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block_M = 128
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block_N = 128
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block_K = 32
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trans_A = False
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trans_B = False
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in_dtype = "float16"
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out_dtype = "float16"
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dtypeAccum = "float32"
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num_threads = 128
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program = matmul_nested_pipelines(
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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_threads,
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order,
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stage,
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extra_pipeline_repeats,
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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()
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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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if in_dtype == "float32":
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# Convert float32 to tfloat32 because tfloat32 mma cannot truncate
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# float32 automatically, -0x1000 meas
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A = (A.view(torch.int32) - 0x1000).view(torch.float32)
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B = (B.view(torch.int32) - 0x1000).view(torch.float32)
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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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def test_nested_pipelines():
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run_gemm_nested_pipelines(order=[0, 1, 2], stage=[0, 0, 1], extra_pipeline_repeats=3)
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"""
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Nested serial cases:
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T.serial
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T.serial
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is OK.
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"""
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@tilelang.jit(out_idx=[1])
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def nested_continuous_serials(length=256, block=16, dtype="float32"):
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@T.prim_func
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def main(
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A: T.Tensor((length,), dtype),
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B: T.Tensor((length,), dtype),
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):
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with T.Kernel(1, threads=length) as _:
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for i in T.serial(length // block):
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for j in T.serial(block):
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B[i * block + j] = A[i * block + j] + 1.0
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return main
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@tilelang.jit(out_idx=[1])
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def nested_noncontinuous_serials(length=256, block=16, dtype="float32"):
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@T.prim_func
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def main(
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A: T.Tensor((length,), dtype),
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B: T.Tensor((length,), dtype),
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):
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with T.Kernel(1, threads=length) as _:
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for i in T.serial(length // block):
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B[i] = 0
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for j in T.serial(block):
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B[i * block + j] = A[i * block + j] + 1.0
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return main
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def test_nested_serials():
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kernel1 = nested_continuous_serials(length=256, block=16)
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data = _require_cuda_tensor((256,), torch.float32)
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result1 = kernel1(data)
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torch.testing.assert_close(result1, data + 1.0, atol=1e-5, rtol=1e-5)
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# This is valid
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nested_noncontinuous_serials(length=256, block=16)
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"""
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Mixed serial and Parallel loops:
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(S-P)
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T.serial
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T.Parallel
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(P-S)
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T.Parallel
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T.serial
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Rule:
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- No Parallel - * - Parallel
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"""
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@tilelang.jit(out_idx=[1])
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def nested_continuous_sp(length=256, block=16, dtype="float32"):
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@T.prim_func
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def main(
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A: T.Tensor((length,), dtype),
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B: T.Tensor((length,), dtype),
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):
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with T.Kernel(1, threads=length) as _:
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for i in T.serial(length // block):
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for j in T.Parallel(block):
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B[i * block + j] = A[i * block + j] + 1.0
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return main
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@tilelang.jit(out_idx=[1])
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def nested_continuous_ps(length=256, block=16, dtype="float32"):
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@T.prim_func
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def main(
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A: T.Tensor((length,), dtype),
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B: T.Tensor((length,), dtype),
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):
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with T.Kernel(1, threads=length) as _:
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for i in T.Parallel(length // block):
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for j in T.serial(block):
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B[i * block + j] = A[i * block + j] + 1.0
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return main
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@tilelang.jit(out_idx=[1])
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def nested_continuous_psp(length=256, block1=8, block2=2, dtype="float32"):
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@T.prim_func
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def main(
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A: T.Tensor((length,), dtype),
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B: T.Tensor((length,), dtype),
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):
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with T.Kernel(1, threads=length) as _:
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for i in T.Parallel(length // block1 // block2):
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for j in T.serial(block1):
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for k in T.Parallel(block2):
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B[i * block1 * block2 + j * block2 + k] = A[i * block1 * block2 + j * block2 + k] + 1.0
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return main
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@tilelang.jit(out_idx=[1])
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def nested_continuous_sps(length=256, block1=8, block2=2, dtype="float32"):
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@T.prim_func
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def main(
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A: T.Tensor((length,), dtype),
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B: T.Tensor((length,), dtype),
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):
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with T.Kernel(1, threads=length) as _:
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for i in T.serial(length // block1 // block2):
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for j in T.Parallel(block1):
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for k in T.serial(block2):
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B[i * block1 * block2 + j * block2 + k] = A[i * block1 * block2 + j * block2 + k] + 1.0
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return main
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def test_mixed_sp():
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kernel1 = nested_continuous_sp(length=256, block=16)
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kernel2 = nested_continuous_ps(length=256, block=16)
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data = _require_cuda_tensor((256,), torch.float32)
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result1 = kernel1(data)
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result2 = kernel2(data)
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torch.testing.assert_close(result1, data + 1.0, atol=1e-5, rtol=1e-5)
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torch.testing.assert_close(result2, data + 1.0, atol=1e-5, rtol=1e-5)
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# This should be invalid (Undefined behaviour)
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with pytest.raises(ValueError):
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nested_continuous_psp(length=256, block1=16, block2=8)
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kernel3 = nested_continuous_sps(length=256, block1=8, block2=2)
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result3 = kernel3(data)
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torch.testing.assert_close(result3, data + 1.0, atol=1e-5, rtol=1e-5)
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"""
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Mixed Pipelined and Parallel loops:
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(Pi-Pa)
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T.Pipelined
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T.Parallel
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(Pa-Pi)
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T.Parallel
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T.Pipelined
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Rule:
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- Pi-Pa is ok where Pa-Pi is not allowed.
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- For more nested cases, refer to the rule of T.Parallel.
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"""
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def matmul_nested_pipa(
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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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in_dtype,
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out_dtype,
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accum_dtype,
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threads,
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order,
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stage,
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):
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A_shape = (M, K)
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B_shape = (K, N)
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A_shared_shape = (block_M, block_K)
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B_shared_shape = (block_K, block_N)
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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), order=order, stage=stage):
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for i, j in T.Parallel(block_M, block_K):
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A_shared[i, j] = A[by * block_M + i, k * block_K + j]
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for i, j in T.Parallel(block_K, block_N):
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B_shared[i, j] = B[k * block_K + i, bx * block_N + j]
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# T.copy(A[by * block_M, k * block_K], A_shared)
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# T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(A_shared, B_shared, C_local, False, False)
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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 matmul_nested_papipa(
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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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in_dtype,
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out_dtype,
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accum_dtype,
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threads,
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order,
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stage,
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):
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A_shape = (M, K)
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B_shape = (K, N)
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A_shared_shape = (block_M, block_K)
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B_shared_shape = (block_K, block_N)
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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 _ in T.Parallel(1):
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for k in T.Pipelined(T.ceildiv(K, block_K), order=order, stage=stage):
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for i, j in T.Parallel(block_M, block_K):
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A_shared[i, j] = A[by * block_M + i, k * block_K + j]
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for i, j in T.Parallel(block_K, block_N):
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B_shared[i, j] = B[k * block_K + i, bx * block_N + j]
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# T.copy(A[by * block_M, k * block_K], A_shared)
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# T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(A_shared, B_shared, C_local, False, False)
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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_mixed_pp(
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order,
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stage,
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):
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M = 1024
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N = 1024
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K = 1024
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block_M = 128
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block_N = 128
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block_K = 32
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in_dtype = "float16"
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out_dtype = "float16"
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dtypeAccum = "float32"
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num_threads = 128
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program = matmul_nested_pipa(
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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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in_dtype,
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out_dtype,
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dtypeAccum,
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num_threads,
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order,
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stage,
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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()
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|
|
|
def ref_program(A, B):
|
|
import torch
|
|
|
|
if in_dtype == "float32":
|
|
# Convert float32 to tfloat32 because tfloat32 mma cannot truncate
|
|
# float32 automatically, -0x1000 meas
|
|
A = (A.view(torch.int32) - 0x1000).view(torch.float32)
|
|
B = (B.view(torch.int32) - 0x1000).view(torch.float32)
|
|
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)
|
|
|
|
program1 = matmul_nested_papipa(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
num_threads,
|
|
order,
|
|
stage,
|
|
)
|
|
with pytest.raises(ValueError):
|
|
tilelang.compile(
|
|
program1,
|
|
out_idx=[2],
|
|
pass_configs={
|
|
tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
|
|
tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
|
|
},
|
|
)
|
|
|
|
|
|
def test_mixed_pp():
|
|
run_gemm_mixed_pp(order=[0, 1, 2], stage=[0, 0, 1])
|
|
|
|
|
|
"""
|
|
TiledOp in a T.Parallel is also not permitted.
|
|
"""
|
|
|
|
|
|
def matmul_with_parallel(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
in_dtype,
|
|
out_dtype,
|
|
accum_dtype,
|
|
threads,
|
|
order,
|
|
stage,
|
|
):
|
|
A_shape = (M, K)
|
|
B_shape = (K, N)
|
|
A_shared_shape = (block_M, block_K)
|
|
B_shared_shape = (block_K, block_N)
|
|
|
|
@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)
|
|
C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
|
|
T.clear(C_local)
|
|
for k in T.Pipelined(T.ceildiv(K, block_K), order=order, stage=stage):
|
|
for i, j in T.Parallel(block_M, block_K):
|
|
A_shared[i, j] = A[by * block_M + i, k * block_K + j]
|
|
for i, j in T.Parallel(block_K, block_N):
|
|
B_shared[i, j] = B[k * block_K + i, bx * block_N + j]
|
|
|
|
# T.copy(A[by * block_M, k * block_K], A_shared)
|
|
# T.copy(B[k * block_K, bx * block_N], B_shared)
|
|
|
|
for _ in T.Parallel(1):
|
|
T.gemm(A_shared, B_shared, C_local, False, False)
|
|
T.copy(C_local, C[by * block_M, bx * block_N])
|
|
|
|
return main
|
|
|
|
|
|
def run_gemm_tiled_op_with_parallel(
|
|
order,
|
|
stage,
|
|
):
|
|
M = 1024
|
|
N = 1024
|
|
K = 1024
|
|
block_M = 128
|
|
block_N = 128
|
|
block_K = 32
|
|
in_dtype = "float16"
|
|
out_dtype = "float16"
|
|
dtypeAccum = "float32"
|
|
num_threads = 128
|
|
|
|
program = matmul_nested_pipa(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
num_threads,
|
|
order,
|
|
stage,
|
|
)
|
|
|
|
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()
|
|
|
|
def ref_program(A, B):
|
|
import torch
|
|
|
|
if in_dtype == "float32":
|
|
# Convert float32 to tfloat32 because tfloat32 mma cannot truncate
|
|
# float32 automatically, -0x1000 meas
|
|
A = (A.view(torch.int32) - 0x1000).view(torch.float32)
|
|
B = (B.view(torch.int32) - 0x1000).view(torch.float32)
|
|
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)
|
|
|
|
program1 = matmul_with_parallel(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
in_dtype,
|
|
out_dtype,
|
|
dtypeAccum,
|
|
num_threads,
|
|
order,
|
|
stage,
|
|
)
|
|
with pytest.raises(ValueError):
|
|
tilelang.compile(
|
|
program1,
|
|
out_idx=[2],
|
|
pass_configs={
|
|
tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
|
|
tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
|
|
},
|
|
)
|
|
|
|
|
|
@tilelang.jit(out_idx=[1])
|
|
def tir_op_with_parallel(length=256, block=16, dtype="float32"):
|
|
@T.prim_func
|
|
def main(
|
|
A: T.Tensor((length,), dtype),
|
|
B: T.Tensor((length,), dtype),
|
|
):
|
|
with T.Kernel(1, threads=length) as _:
|
|
for i in T.Parallel(length // block):
|
|
for j in T.Parallel(block):
|
|
B[i * block + j] = T.max(A[i * block + j], 0.0)
|
|
|
|
return main
|
|
|
|
|
|
@tilelang.jit(out_idx=[1])
|
|
def customize_op_with_parallel(length=256, block=16, dtype="float32"):
|
|
@T.prim_func
|
|
def main(
|
|
A: T.Tensor((length,), dtype),
|
|
B: T.Tensor((length,), dtype),
|
|
):
|
|
with T.Kernel(1, threads=length) as _:
|
|
for i in T.Parallel(length // block):
|
|
for j in T.Parallel(block):
|
|
B[i * block + j] = A[i * block + j]
|
|
T.atomic_add(B[i * block + j], 1.0)
|
|
|
|
return main
|
|
|
|
|
|
def test_tiled_op_with_parallel():
|
|
run_gemm_tiled_op_with_parallel(order=[0, 1, 2], stage=[0, 0, 1])
|
|
|
|
kernel1 = tir_op_with_parallel(length=256, block=16)
|
|
data = _require_cuda_tensor((256,), torch.float32)
|
|
result1 = kernel1(data)
|
|
torch.testing.assert_close(result1, torch.relu(data), atol=1e-5, rtol=1e-5)
|
|
kernel2 = customize_op_with_parallel(length=256, block=16)
|
|
result2 = kernel2(data)
|
|
torch.testing.assert_close(result2, data + 1, atol=1e-5, rtol=1e-5)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tilelang.testing.main()
|