wehub-resource-sync
60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# 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, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import access_topo_drr
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import ap
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import pir
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@access_topo_drr.register_drr_pass("pd_op_static_relu", tag="umprime")
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class PdOpReluAccessTopoPass(access_topo_drr.DrrPass):
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def __init__(self):
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self.zero = pir.a_f64(ap.DataValue.float64("0"))
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def source_pattern(self, o, t):
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o.full_op = o.ap_native_op("pd_op.full")
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o.full_op([], [t.intermediate])
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o.maximum_op = o.ap_native_op("pd_op.maximum")
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o.maximum_op([t.input, t.intermediate], [t.output])
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def constraint(self, o, t):
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return o.full_op.value == self.zero
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def result_pattern(self, o, t):
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o.result_op = o.ap_native_op("pd_op.relu")
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o.result_op([t.input], [t.output])
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@access_topo_drr.register_drr_pass("pd_op_dynamic_relu", tag="umprime")
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class PdOpDynReluAccessTopoPass(access_topo_drr.DrrPass):
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def __init__(self):
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self.zero = pir.a_f64(ap.DataValue.float64("0"))
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def source_pattern(self, o, t):
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o.full_op = o.ap_native_op("pd_op.full")
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o.full_op([], [t.intermediate0])
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o.generate_shape_op = o.ap_native_op("cinn_op.generate_shape")
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o.generate_shape_op([t.input0], [t.intermediate1])
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o.expand_op = o.ap_native_op("pd_op.expand")
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o.expand_op([t.intermediate0, t.intermediate1], [t.intermediate2])
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o.maximum_op = o.ap_native_op("pd_op.maximum")
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o.maximum_op([t.input1, t.intermediate2], [t.output])
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def constraint(self, o, t):
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return o.full_op.value == self.zero
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def result_pattern(self, o, t):
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o.result_op = o.ap_native_op("pd_op.relu")
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o.result_op([t.input1], [t.output])
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