wehub-resource-sync
271 lines
9.1 KiB
Python
271 lines
9.1 KiB
Python
# Copyright (c) 2023 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 ast
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import os
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import re
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import subprocess
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import sys
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import unittest
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle.base import core
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from paddle.decomposition import decomp
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REGEX_FLAGS = re.compile(
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r"Result\(prim_all=(?P<prim_all>.*), prim_fwd=(?P<prim_fwd>.*), prim_bwd=(?P<prim_bwd>.*)\)"
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)
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class TestPrimFlags(unittest.TestCase):
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def test_prim_flags_default(self):
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self.assertFalse(core._is_bwd_prim_enabled())
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self.assertFalse(core._is_fwd_prim_enabled())
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self.assertFalse(core._is_all_prim_enabled())
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def check_prim_flags_under_subprocess(
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self, instructions, env, expected_flags
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):
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all_instrs = [
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"import paddle",
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*instructions,
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"prim_all = paddle.base.core._is_all_prim_enabled()",
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"prim_fwd = paddle.base.core._is_fwd_prim_enabled()",
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"prim_bwd = paddle.base.core._is_bwd_prim_enabled()",
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"print(f'Result(prim_all={prim_all}, prim_fwd={prim_fwd}, prim_bwd={prim_bwd})', end='')",
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]
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inherited_env = os.environ.copy()
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inherited_env.update(env)
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result = subprocess.run(
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[sys.executable, '-c', '; '.join(all_instrs)],
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capture_output=True,
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env=inherited_env,
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)
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if result.returncode != 0:
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self.fail(f"Failed to run subprocess: {result.stderr}")
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matched_flags = REGEX_FLAGS.search(result.stdout.decode())
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self.assertIsNotNone(
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matched_flags, f"Failed to parse flags: {result.stdout}"
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)
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flags = (
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ast.literal_eval(matched_flags.group("prim_all")),
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ast.literal_eval(matched_flags.group("prim_fwd")),
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ast.literal_eval(matched_flags.group("prim_bwd")),
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)
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self.assertEqual(
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flags, expected_flags, f"Expected: {expected_flags}, got: {flags}"
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)
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def test_prim_flags_under_subprocess(self):
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# Check envs
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self.check_prim_flags_under_subprocess(
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[],
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{},
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(False, False, False), # (prim_all, prim_fwd, prim_bwd)
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)
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self.check_prim_flags_under_subprocess(
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[],
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{"FLAGS_prim_backward": "True"},
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(False, False, True),
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)
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self.check_prim_flags_under_subprocess(
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[],
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{"FLAGS_prim_forward": "True"},
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(False, True, False),
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)
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self.check_prim_flags_under_subprocess(
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[],
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{"FLAGS_prim_all": "True"},
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(True, True, True),
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)
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self.check_prim_flags_under_subprocess(
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[],
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{"FLAGS_prim_all": "True", "FLAGS_prim_forward": "False"},
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(False, False, True),
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)
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self.check_prim_flags_under_subprocess(
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[],
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{"FLAGS_prim_all": "True", "FLAGS_prim_backward": "False"},
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(False, True, False),
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)
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# Check apis
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self.check_prim_flags_under_subprocess(
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["paddle.base.core._set_prim_all_enabled(True)"],
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{},
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(True, True, True),
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)
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self.check_prim_flags_under_subprocess(
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["paddle.base.core._set_prim_forward_enabled(True)"],
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{},
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(False, True, False),
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)
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self.check_prim_flags_under_subprocess(
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["paddle.base.core._set_prim_backward_enabled(True)"],
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{},
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(False, False, True),
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)
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self.check_prim_flags_under_subprocess(
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[
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"paddle.base.core._set_prim_all_enabled(True)",
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"paddle.base.core._set_prim_forward_enabled(False)",
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],
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{},
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(False, False, True),
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)
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self.check_prim_flags_under_subprocess(
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[
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"paddle.base.core._set_prim_all_enabled(True)",
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"paddle.base.core._set_prim_backward_enabled(False)",
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],
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{},
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(False, True, False),
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)
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self.check_prim_flags_under_subprocess(
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[
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"paddle.base.core._set_prim_forward_enabled(True)",
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"paddle.base.core._set_prim_backward_enabled(True)",
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],
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{},
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(True, True, True),
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)
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# Check envs and apis
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self.check_prim_flags_under_subprocess(
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[
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"paddle.base.core._set_prim_all_enabled(False)",
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],
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{
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"FLAGS_prim_all": "True",
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},
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(False, False, False),
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)
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class TestPrimBlacklistFlags(unittest.TestCase):
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def not_in_blacklist(self, op_name):
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inputs = np.random.random([2, 3, 4]).astype("float32")
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paddle.enable_static()
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core._set_prim_forward_enabled(True)
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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'x', shape=inputs.shape, dtype=str(inputs.dtype)
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)
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y = F.gelu(x)
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z = F.silu(y)
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fwd_ops = [op.name() for op in main_program.global_block().ops]
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# Ensure that tanh in original block
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self.assertTrue(op_name in fwd_ops)
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z = decomp.decompose(main_program, [z])
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fwd_ops_new = [op.name() for op in main_program.global_block().ops]
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# Ensure that tanh is split into small ops
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self.assertTrue(op_name not in fwd_ops_new)
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exe = paddle.static.Executor()
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exe.run(startup_program)
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_ = exe.run(main_program, feed={'x': inputs}, fetch_list=[z])
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paddle.disable_static()
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core._set_prim_forward_enabled(False)
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def in_blacklist(self, op_name):
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inputs = np.random.random([2, 3, 4]).astype("float32")
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paddle.enable_static()
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core._set_prim_forward_enabled(True)
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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'x', shape=inputs.shape, dtype=str(inputs.dtype)
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)
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y = F.gelu(x)
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z = F.silu(y)
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fwd_ops = [op.name() for op in main_program.global_block().ops]
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# Ensure that tanh in original block
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self.assertTrue(op_name in fwd_ops)
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z = decomp.decompose(main_program, [z])
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fwd_ops_new = [op.name() for op in main_program.global_block().ops]
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# Ensure that tanh is split into small ops
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self.assertTrue(op_name in fwd_ops_new)
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exe = paddle.static.Executor()
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exe.run(startup_program)
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_ = exe.run(main_program, feed={'x': inputs}, fetch_list=[z])
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paddle.disable_static()
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core._set_prim_forward_enabled(False)
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def test_prim_forward_blacklist(self):
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self.not_in_blacklist("pd_op.gelu")
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core._set_prim_forward_blacklist("pd_op.gelu")
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self.in_blacklist("pd_op.gelu")
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def test_prim_forward_blacklist_flag(self):
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self.not_in_blacklist("pd_op.silu")
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paddle.set_flags({"FLAGS_prim_forward_blacklist": "pd_op.silu"})
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self.in_blacklist("pd_op.silu")
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class PrimeNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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x1 = paddle.tanh(x)
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x2 = paddle.exp(x)
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x3 = x1 + x2
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res = paddle.nn.functional.gelu(x3)
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return res
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class TestPrimBackwardBlacklistFlags(unittest.TestCase):
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def train(self):
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x = paddle.randn([2, 4])
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x.stop_gradient = False
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net = PrimeNet()
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net.forward = paddle.jit.to_static(full_graph=True)(net.forward)
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out = net(x)
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loss = paddle.mean(out)
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loss.backward()
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self.check_prim(net)
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def check_prim(self, net):
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program = net.forward.program_cache.last()[-1][-1].train_program
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if isinstance(
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program, paddle.jit.dy2static.pir_partial_program.RunnableProgram
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):
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program = program.program
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block = program.global_block()
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ops = [op.name() for op in block.ops]
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self.assertTrue('pd_op.tanh_grad' in ops)
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self.assertTrue('pd_op.exp_grad' in ops)
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self.assertTrue('pd_op.gelu_grad' not in ops)
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def test_prim_backward_blacklist(self):
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core._set_prim_all_enabled(True)
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core._set_prim_backward_blacklist("pd_op.tanh_grad", "pd_op.exp_grad")
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self.train()
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core._set_prim_all_enabled(False)
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if __name__ == '__main__':
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unittest.main()
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