wehub-resource-sync
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import numpy as np
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import pytest
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import tvm
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import tvm.testing
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from tvm.script import tirx as T
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from tvm.testing import env
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@T.prim_func(s_tir=True)
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def ptx_cp_async(A: T.Buffer((32, 128), "float16"), B: T.Buffer((32, 128), "float16")) -> None:
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T.func_attr({"global_symbol": "default_function", "tirx.noalias": True})
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bx = T.env_thread("blockIdx.x")
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tx = T.env_thread("threadIdx.x")
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T.launch_thread(bx, 1)
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T.launch_thread(tx, 32)
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with T.sblock():
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A_shared = T.sblock_alloc_buffer([32, 128], "float16", scope="shared")
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T.reads(A[0:32, 0:128])
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T.writes(B[0:32, 0:128])
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for i in range(16):
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T.evaluate(
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T.ptx.cp_async.legacy(
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A_shared.data, tx * 128 + 8 * i, A.data, tx * 128 + 8 * i, 16, dtype="float16"
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)
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)
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# TODO(masahi): Remove dtype requirement from TVMScript parser
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T.evaluate(T.ptx.cp_async.commit_group(dtype=""))
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T.evaluate(T.ptx.cp_async.wait_group(0, dtype=""))
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for i in range(128):
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B[tx, i] = A_shared[tx, i]
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@pytest.mark.gpu
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@pytest.mark.skipif(not env.has_cuda_compute(8), reason="need cuda compute >= 8.0")
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def test_ptx_cp_async():
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f = ptx_cp_async
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mod = tvm.compile(f, target="cuda")
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A_np = np.random.rand(32, 128).astype("float16")
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B_np = np.zeros((32, 128)).astype("float16")
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def run_and_check():
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dev = tvm.cuda(0)
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A_nd = tvm.runtime.tensor(A_np, device=dev)
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B_nd = tvm.runtime.tensor(B_np, device=dev)
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mod(A_nd, B_nd)
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tvm.testing.assert_allclose(B_nd.numpy(), A_np)
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tvm.testing.run_with_gpu_lock(run_and_check)
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# Note: tests for the indexed barrier API (`create_barriers`,
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# `ptx_init_barrier_thread_count`, `ptx_arrive_barrier`, `ptx_wait_barrier`,
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# `ptx_cp_async_barrier`, `ptx_arrive_barrier_expect_tx`) were removed —
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# fork uses `ptx_mbarrier_*` instead and those intrinsics have no
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# users elsewhere in this codebase.
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if __name__ == "__main__":
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test_ptx_cp_async()
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