mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-23 07:00:29 +08:00
Lei Wang
556d411e99
* Update test parameters and remove debug print statement - Adjusted test cases in `test_tilelang_dynamic_symbolic_bench.py` to use smaller matrix sizes (1024x1024) for improved performance and quicker execution. - Removed a debug print statement from `phase.py` to clean up the code and enhance clarity. * Refactor loop stack management in warp_specialized_rewriter - Introduced a new `LoopInfo` struct to encapsulate loop variable details, including `loop_var`, `extent`, and `min`, enhancing clarity and maintainability. - Updated the `loop_stack_` to utilize `LoopInfo` instead of a pair, improving type safety and readability. - Adjusted linear index calculations to account for the new structure, ensuring correct behavior in loop transformations. * Remove unused `torch.backends` import and `tilelang.disable_cache()` calls from multiple test files to enhance code clarity and maintainability.
557 lines
15 KiB
Python
557 lines
15 KiB
Python
import torch
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from tilelang import tvm as tvm
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import tilelang.testing
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import tilelang.language as T
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def tl_matmul_block_static(
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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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num_threads,
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):
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A_shape = (K, M) if trans_A else (M, K)
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B_shape = (N, K) if trans_B else (K, N)
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A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
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B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
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@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(
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(M, N), out_dtype)):
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with T.Kernel(
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T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=num_threads) as (bx, by):
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A_shared = T.alloc_shared(A_shared_shape, in_dtype)
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B_shared = T.alloc_shared(B_shared_shape, in_dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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if trans_A:
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T.copy(A[k * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(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 assert_tl_matmul_block_static(
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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=3,
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num_threads=128,
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):
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program = tl_matmul_block_static(
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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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num_threads,
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)
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kernel = tilelang.compile(program)
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if trans_A:
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A = torch.rand(K, M, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
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if trans_B:
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B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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B = torch.rand(K, N, device="cuda", dtype=getattr(torch, in_dtype))
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C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, out_dtype))
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kernel(A, B, C)
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# print(kernel.get_kernel_source())
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def ref_program(A, B):
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import torch
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if trans_A:
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A = A.T
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if trans_B:
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B = B.T
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.__getattribute__(out_dtype))
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return C
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# Get Reference Result
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ref_c = ref_program(A, B)
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torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
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profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
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latency = profiler.do_bench()
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print(f"Static Latency: {latency} ms")
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def tl_matmul_block_dynamic_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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num_threads,
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):
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M = tvm.te.var("m")
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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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@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(
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(M, N), out_dtype)):
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with T.Kernel(
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T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=num_threads) as (bx, by):
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A_shared = T.alloc_shared(A_shared_shape, in_dtype)
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B_shared = T.alloc_shared(B_shared_shape, in_dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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if trans_A:
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T.copy(A[k * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(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 assert_tl_matmul_block_dynamic_m(
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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=3,
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num_threads=128,
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pass_configs=None,
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):
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program = tl_matmul_block_dynamic_m(
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N,
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K,
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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num_stages,
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num_threads,
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)
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kernel = tilelang.compile(program, pass_configs=pass_configs)
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if trans_A:
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A = torch.rand(K, M, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
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if trans_B:
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B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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B = torch.rand(K, N, device="cuda", dtype=getattr(torch, in_dtype))
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C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, out_dtype))
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kernel(A, B, C)
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def ref_program(A, B):
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import torch
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if trans_A:
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A = A.T
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if trans_B:
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B = B.T
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.__getattribute__(out_dtype))
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return C
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# Get Reference Result
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ref_c = ref_program(A, B)
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torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
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profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
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latency = profiler.do_bench(input_tensors=[A, B, C])
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print(f"Dynamic M Latency with pass_configs: {pass_configs} is {latency} ms")
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def tl_matmul_block_dynamic_mn(
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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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num_threads,
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):
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M = tvm.te.var("m")
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N = tvm.te.var("n")
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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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@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(
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(M, N), out_dtype)):
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with T.Kernel(
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T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=num_threads) as (bx, by):
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A_shared = T.alloc_shared(A_shared_shape, in_dtype)
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B_shared = T.alloc_shared(B_shared_shape, in_dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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if trans_A:
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T.copy(A[k * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(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 assert_tl_matmul_block_dynamic_mn(
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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=3,
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num_threads=128,
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pass_configs=None,
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):
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program = tl_matmul_block_dynamic_mn(
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K,
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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num_stages,
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num_threads,
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)
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kernel = tilelang.compile(program, pass_configs=pass_configs)
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if trans_A:
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A = torch.rand(K, M, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
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if trans_B:
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B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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B = torch.rand(K, N, device="cuda", dtype=getattr(torch, in_dtype))
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C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, out_dtype))
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kernel(A, B, C)
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def ref_program(A, B):
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import torch
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if trans_A:
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A = A.T
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if trans_B:
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B = B.T
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.__getattribute__(out_dtype))
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return C
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# Get Reference Result
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ref_c = ref_program(A, B)
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torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
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profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
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latency = profiler.do_bench(input_tensors=[A, B, C])
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print(f"Dynamic MN Latency with pass_configs: {pass_configs} is {latency} ms")
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def tl_matmul_block_dynamic_mnk(
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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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num_threads,
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):
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M = tvm.te.var("m")
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N = tvm.te.var("n")
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K = tvm.te.var("k")
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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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@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(
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(M, N), out_dtype)):
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with T.Kernel(
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T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=num_threads) as (bx, by):
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A_shared = T.alloc_shared(A_shared_shape, in_dtype)
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B_shared = T.alloc_shared(B_shared_shape, in_dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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if trans_A:
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T.copy(A[k * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(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 assert_tl_matmul_block_dynamic_mnk(
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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=3,
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num_threads=128,
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pass_configs=None,
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):
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program = tl_matmul_block_dynamic_mnk(
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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num_stages,
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num_threads,
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)
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kernel = tilelang.compile(program, pass_configs=pass_configs)
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if trans_A:
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A = torch.rand(K, M, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
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if trans_B:
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B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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B = torch.rand(K, N, device="cuda", dtype=getattr(torch, in_dtype))
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C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, out_dtype))
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kernel(A, B, C)
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def ref_program(A, B):
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import torch
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if trans_A:
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A = A.T
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if trans_B:
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B = B.T
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.__getattribute__(out_dtype))
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return C
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# Get Reference Result
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ref_c = ref_program(A, B)
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torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
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profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
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latency = profiler.do_bench(input_tensors=[A, B, C])
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print(f"Dynamic MNK Latency with pass_configs: {pass_configs} is {latency} ms")
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def run_assert_tl_matmul_block_static(M, N, K, block_M, block_N, block_K):
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assert_tl_matmul_block_static(M, N, K, block_M, block_N, block_K, False, False, "float16",
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"float16", "float32")
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def run_assert_tl_matmul_block_dynamic_m(M, N, K, block_M, block_N, block_K):
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assert_tl_matmul_block_dynamic_m(
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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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False,
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False,
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"float16",
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"float16",
|
|
"float32",
|
|
pass_configs={
|
|
"tl.disable_dynamic_tail_split": True,
|
|
"tl.dynamic_alignment": 8
|
|
})
|
|
assert_tl_matmul_block_dynamic_m(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
False,
|
|
False,
|
|
"float16",
|
|
"float16",
|
|
"float32",
|
|
pass_configs={"tl.disable_dynamic_tail_split": False})
|
|
|
|
|
|
def run_assert_tl_matmul_block_dynamic_mn(M, N, K, block_M, block_N, block_K):
|
|
assert_tl_matmul_block_dynamic_mn(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
False,
|
|
False,
|
|
"float16",
|
|
"float16",
|
|
"float32",
|
|
pass_configs={
|
|
"tl.disable_dynamic_tail_split": True,
|
|
"tl.dynamic_alignment": 8
|
|
})
|
|
assert_tl_matmul_block_dynamic_mn(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
False,
|
|
False,
|
|
"float16",
|
|
"float16",
|
|
"float32",
|
|
pass_configs={"tl.disable_dynamic_tail_split": False})
|
|
|
|
|
|
def run_assert_tl_matmul_block_dynamic_mnk(M, N, K, block_M, block_N, block_K):
|
|
assert_tl_matmul_block_dynamic_mnk(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
False,
|
|
False,
|
|
"float16",
|
|
"float16",
|
|
"float32",
|
|
pass_configs={
|
|
"tl.disable_dynamic_tail_split": True,
|
|
"tl.dynamic_alignment": 4
|
|
})
|
|
assert_tl_matmul_block_dynamic_mnk(
|
|
M,
|
|
N,
|
|
K,
|
|
block_M,
|
|
block_N,
|
|
block_K,
|
|
False,
|
|
False,
|
|
"float16",
|
|
"float16",
|
|
"float32",
|
|
pass_configs={"tl.disable_dynamic_tail_split": False})
|
|
|
|
|
|
def test_all():
|
|
run_assert_tl_matmul_block_static(1024, 1024, 1024, 128, 128, 32)
|
|
run_assert_tl_matmul_block_dynamic_m(1024, 1024, 1024, 128, 128, 32)
|
|
run_assert_tl_matmul_block_dynamic_mn(1024, 1024, 1024, 128, 128, 32)
|
|
run_assert_tl_matmul_block_dynamic_mnk(1024, 1024, 1024, 128, 128, 32)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tilelang.testing.main()
|