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wehub-resource-sync
2026-07-13 13:36:25 +08:00
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tests/python/relax/test_transform_allocate_workspace.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import tvm
import tvm.testing
from tvm import relax
from tvm.script import ir as I
from tvm.script import relax as R
@I.ir_module
class Module:
@R.function
def fused_relax_nn_attention_cutlass(
q: R.Tensor((32, 8, 16, 8), dtype="float16"),
k: R.Tensor((32, 8, 16, 8), dtype="float16"),
v: R.Tensor((32, 8, 16, 8), dtype="float16"),
) -> R.Tensor((32, 8, 16, 8), dtype="float16"):
R.func_attr(
{
"Codegen": "cutlass",
"WorkspaceSize": 65536,
"global_symbol": "fused_relax_nn_attention_cutlass",
}
)
@R.function
def gv(
q_1: R.Tensor((32, 8, 16, 8), dtype="float16"),
k_1: R.Tensor((32, 8, 16, 8), dtype="float16"),
v_1: R.Tensor((32, 8, 16, 8), dtype="float16"),
) -> R.Tensor((32, 8, 16, 8), dtype="float16"):
R.func_attr(
{"Composite": "cutlass.attention", "Primitive": True, "WorkspaceSize": 65536}
)
with R.dataflow():
gv_2: R.Tensor((32, 8, 16, 8), dtype="float16") = R.nn.attention(
q_1, k_1, v_1, scale=None
)
R.output(gv_2)
return gv_2
gv1: R.Tensor((32, 8, 16, 8), dtype="float16") = gv(q, k, v)
return gv1
@R.function
def entry_a(
q: R.Tensor((32, 8, 16, 8), dtype="float16"),
k: R.Tensor((32, 8, 16, 8), dtype="float16"),
v: R.Tensor((32, 8, 16, 8), dtype="float16"),
) -> R.Tensor((32, 8, 16, 8), dtype="float16"):
cls = Module
with R.dataflow():
gv: R.Tensor((32, 8, 16, 8), dtype="float16") = cls.fused_relax_nn_attention_cutlass(
q, k, v
)
R.output(gv)
return gv
@R.function
def entry_b(
q: R.Tensor((32, 8, 16, 8), dtype="float16"),
k: R.Tensor((32, 8, 16, 8), dtype="float16"),
v: R.Tensor((32, 8, 16, 8), dtype="float16"),
) -> R.Tensor((32, 8, 16, 8), dtype="float16"):
cls = Module
with R.dataflow():
gv: R.Tensor((32, 8, 16, 8), dtype="float16") = cls.fused_relax_nn_attention_cutlass(
q, k, v
) + R.const(1, dtype="float16")
R.output(gv)
return gv
@I.ir_module
class Expected:
@R.function
def fused_relax_nn_attention_cutlass1(
q: R.Tensor((32, 8, 16, 8), dtype="float16"),
k: R.Tensor((32, 8, 16, 8), dtype="float16"),
v: R.Tensor((32, 8, 16, 8), dtype="float16"),
workspace: R.Tensor((65536,), dtype="uint8"),
) -> R.Tensor((32, 8, 16, 8), dtype="float16"):
R.func_attr(
{
"Codegen": "cutlass",
"global_symbol": "fused_relax_nn_attention_cutlass1",
}
)
@R.function
def gv(
q_1: R.Tensor((32, 8, 16, 8), dtype="float16"),
k_1: R.Tensor((32, 8, 16, 8), dtype="float16"),
v_1: R.Tensor((32, 8, 16, 8), dtype="float16"),
workspace_1: R.Tensor((65536,), dtype="uint8"),
) -> R.Tensor((32, 8, 16, 8), dtype="float16"):
R.func_attr({"Composite": "cutlass.attention", "Primitive": True})
with R.dataflow():
gv_2: R.Tensor((32, 8, 16, 8), dtype="float16") = R.nn.attention(
q_1, k_1, v_1, scale=None
)
R.output(gv_2)
return gv_2
gv1: R.Tensor((32, 8, 16, 8), dtype="float16") = gv(q, k, v, workspace)
return gv1
@R.function
def entry_a(
q: R.Tensor((32, 8, 16, 8), dtype="float16"),
k: R.Tensor((32, 8, 16, 8), dtype="float16"),
v: R.Tensor((32, 8, 16, 8), dtype="float16"),
) -> R.Tensor((32, 8, 16, 8), dtype="float16"):
cls = Expected
with R.dataflow():
workspace_main: R.Tensor((65536,), dtype="uint8") = R.builtin.alloc_tensor(
R.shape([65536]), R.dtype("uint8"), R.prim_value(0)
)
gv: R.Tensor((32, 8, 16, 8), dtype="float16") = cls.fused_relax_nn_attention_cutlass1(
q, k, v, workspace_main
)
R.output(gv)
return gv
@R.function
def entry_b(
q: R.Tensor((32, 8, 16, 8), dtype="float16"),
k: R.Tensor((32, 8, 16, 8), dtype="float16"),
v: R.Tensor((32, 8, 16, 8), dtype="float16"),
) -> R.Tensor((32, 8, 16, 8), dtype="float16"):
cls = Expected
with R.dataflow():
workspace_main: R.Tensor((65536,), dtype="uint8") = R.builtin.alloc_tensor(
R.shape([65536]), R.dtype("uint8"), R.prim_value(0)
)
gv: R.Tensor((32, 8, 16, 8), dtype="float16") = cls.fused_relax_nn_attention_cutlass1(
q, k, v, workspace_main
) + R.const(1, dtype="float16")
R.output(gv)
return gv
def test_single_attention():
rewritten = relax.transform.AllocateWorkspace()(Module)
tvm.ir.assert_structural_equal(rewritten, Expected)
if __name__ == "__main__":
tvm.testing.main()