mirror of
https://github.com/pytorch/pytorch.git
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Refactored proxytensor to clean up separate branches (#84325)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84325 Approved by: https://github.com/ezyang
This commit is contained in:
Horace He
committed by
PyTorch MergeBot
PyTorch MergeBot
parent
8843f5b986
commit
a27a4a02fe
@ -117,12 +117,12 @@ class TestFunctionalization(TestCase):
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def forward(self, a_1):
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ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False)
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view_copy_default = torch.ops.aten.view_copy.default(a_1, [4, 2])
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add_tensor = torch.ops.aten.add.Tensor(view_copy_default, ones); view_copy_default = ones = None
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view_copy_default_1 = torch.ops.aten.view_copy.default(add_tensor, [4, 2])
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mul_tensor = torch.ops.aten.mul.Tensor(view_copy_default_1, view_copy_default_1)
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copy__default = torch.ops.aten.copy_.default(a_1, view_copy_default_1); a_1 = view_copy_default_1 = None
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return add_tensor
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view_copy = torch.ops.aten.view_copy.default(a_1, [4, 2])
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add = torch.ops.aten.add.Tensor(view_copy, ones); view_copy = ones = None
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view_copy_1 = torch.ops.aten.view_copy.default(add, [4, 2])
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mul = torch.ops.aten.mul.Tensor(view_copy_1, view_copy_1)
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copy_ = torch.ops.aten.copy_.default(a_1, view_copy_1); a_1 = view_copy_1 = None
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return add
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""")
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reinplaced_logs = self.get_logs(f, torch.ones(4, 2), reapply_views=True, run_reinplace=True)
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@ -132,12 +132,12 @@ def forward(self, a_1):
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def forward(self, a_1):
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ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False)
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view_default = torch.ops.aten.view.default(a_1, [4, 2])
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add_tensor = torch.ops.aten.add.Tensor(view_default, ones); view_default = ones = None
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view_default_1 = torch.ops.aten.view.default(add_tensor, [4, 2])
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mul_tensor = torch.ops.aten.mul.Tensor(view_default_1, view_default_1)
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copy__default = torch.ops.aten.copy_.default(a_1, view_default_1); a_1 = view_default_1 = None
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return add_tensor
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view = torch.ops.aten.view.default(a_1, [4, 2])
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add = torch.ops.aten.add.Tensor(view, ones); view = ones = None
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view_1 = torch.ops.aten.view.default(add, [4, 2])
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mul = torch.ops.aten.mul.Tensor(view_1, view_1)
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copy_ = torch.ops.aten.copy_.default(a_1, view_1); a_1 = view_1 = None
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return add
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""")
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def test_simple_out(self):
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@ -157,11 +157,11 @@ def forward(self, a_1):
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def forward(self, a_1):
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ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False)
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view_copy_default = torch.ops.aten.view_copy.default(a_1, [4, 2]); a_1 = None
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view_copy = torch.ops.aten.view_copy.default(a_1, [4, 2]); a_1 = None
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empty = torch.ops.aten.empty.memory_format([], device = device(type='cpu'), pin_memory = False)
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add_tensor = torch.ops.aten.add.Tensor(view_copy_default, ones); view_copy_default = ones = None
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mul_tensor = torch.ops.aten.mul.Tensor(add_tensor, add_tensor); add_tensor = None
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return mul_tensor
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add = torch.ops.aten.add.Tensor(view_copy, ones); view_copy = ones = None
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mul = torch.ops.aten.mul.Tensor(add, add); add = None
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return mul
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""")
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reinplaced_logs = self.get_logs(f, torch.ones(4, 2), reapply_views=True, run_reinplace=True)
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@ -171,11 +171,11 @@ def forward(self, a_1):
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def forward(self, a_1):
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ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False)
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view_default = torch.ops.aten.view.default(a_1, [4, 2]); a_1 = None
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view = torch.ops.aten.view.default(a_1, [4, 2]); a_1 = None
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empty = torch.ops.aten.empty.memory_format([], device = device(type='cpu'), pin_memory = False)
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add_tensor = torch.ops.aten.add.Tensor(view_default, ones); view_default = ones = None
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mul_tensor = torch.ops.aten.mul.Tensor(add_tensor, add_tensor); add_tensor = None
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return mul_tensor
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add = torch.ops.aten.add.Tensor(view, ones); view = ones = None
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mul = torch.ops.aten.mul.Tensor(add, add); add = None
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return mul
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""")
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def test_multi_out(self):
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@ -195,9 +195,9 @@ def forward(self, a_1):
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def forward(self, a_1):
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empty = torch.ops.aten.empty.memory_format([4], device = device(type='cpu'), pin_memory = False)
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empty_1 = torch.ops.aten.empty.memory_format([4], device = device(type='cpu'), pin_memory = False)
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aminmax_default = torch.ops.aten.aminmax.default(a_1, dim = 0); a_1 = None
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getitem = aminmax_default[0]
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getitem_1 = aminmax_default[1]; aminmax_default = None
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aminmax = torch.ops.aten.aminmax.default(a_1, dim = 0); a_1 = None
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getitem = aminmax[0]
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getitem_1 = aminmax[1]; aminmax = None
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return getitem
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""")
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@ -209,9 +209,9 @@ def forward(self, a_1):
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def forward(self, a_1):
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empty = torch.ops.aten.empty.memory_format([4], device = device(type='cpu'), pin_memory = False)
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empty_1 = torch.ops.aten.empty.memory_format([4], device = device(type='cpu'), pin_memory = False)
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aminmax_default = torch.ops.aten.aminmax.default(a_1, dim = 0); a_1 = None
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getitem = aminmax_default[0]
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getitem_1 = aminmax_default[1]; aminmax_default = None
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aminmax = torch.ops.aten.aminmax.default(a_1, dim = 0); a_1 = None
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getitem = aminmax[0]
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getitem_1 = aminmax[1]; aminmax = None
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return getitem
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""")
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@ -232,11 +232,11 @@ def forward(self, a_1):
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def forward(self, a_1):
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_tensor_constant0 = self._tensor_constant0
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lift_fresh = torch.ops.aten.lift_fresh_copy.default(_tensor_constant0); _tensor_constant0 = None
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view_copy_default = torch.ops.aten.view_copy.default(lift_fresh, [-1]); lift_fresh = None
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add_tensor = torch.ops.aten.add.Tensor(view_copy_default, 1); view_copy_default = None
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view_copy_default_1 = torch.ops.aten.view_copy.default(add_tensor, [3]); add_tensor = None
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return view_copy_default_1
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lift_fresh_copy = torch.ops.aten.lift_fresh_copy.default(_tensor_constant0); _tensor_constant0 = None
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view_copy = torch.ops.aten.view_copy.default(lift_fresh_copy, [-1]); lift_fresh_copy = None
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add = torch.ops.aten.add.Tensor(view_copy, 1); view_copy = None
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view_copy_1 = torch.ops.aten.view_copy.default(add, [3]); add = None
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return view_copy_1
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""")
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reinplaced_logs = self.get_logs(f, inpt, reapply_views=True, run_reinplace=True)
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@ -246,11 +246,11 @@ def forward(self, a_1):
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def forward(self, a_1):
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_tensor_constant0 = self._tensor_constant0
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lift_fresh = torch.ops.aten.lift_fresh_copy.default(_tensor_constant0); _tensor_constant0 = None
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view_default = torch.ops.aten.view.default(lift_fresh, [-1]); lift_fresh = None
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add_tensor = torch.ops.aten.add_.Tensor(view_default, 1)
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view_default_1 = torch.ops.aten.view.default(view_default, [3]); view_default = None
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return view_default_1
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lift_fresh_copy = torch.ops.aten.lift_fresh_copy.default(_tensor_constant0); _tensor_constant0 = None
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view = torch.ops.aten.view.default(lift_fresh_copy, [-1]); lift_fresh_copy = None
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add = torch.ops.aten.add_.Tensor(view, 1)
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view_1 = torch.ops.aten.view.default(view, [3]); view = None
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return view_1
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""")
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@ -282,11 +282,11 @@ def forward(self, a_1):
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def forward(self, a_1):
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ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False)
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view_copy_default = torch.ops.aten.view_copy.default(a_1, [4, 2])
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add_tensor = torch.ops.aten.add.Tensor(a_1, ones); ones = None
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copy__default = torch.ops.aten.copy_.default(a_1, add_tensor); a_1 = None
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view_copy_default_1 = torch.ops.aten.view_copy.default(add_tensor, [4, 2]); add_tensor = None
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return view_copy_default_1
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view_copy = torch.ops.aten.view_copy.default(a_1, [4, 2])
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add = torch.ops.aten.add.Tensor(a_1, ones); ones = None
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copy_ = torch.ops.aten.copy_.default(a_1, add); a_1 = None
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view_copy_1 = torch.ops.aten.view_copy.default(add, [4, 2]); add = None
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return view_copy_1
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""")
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reinplaced_logs = self.get_logs(f, torch.ones(4, 2), reapply_views=True, run_reinplace=True)
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@ -296,11 +296,11 @@ def forward(self, a_1):
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def forward(self, a_1):
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ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False)
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view_default = torch.ops.aten.view.default(a_1, [4, 2])
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add_tensor = torch.ops.aten.add.Tensor(a_1, ones); ones = None
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copy__default = torch.ops.aten.copy_.default(a_1, add_tensor); a_1 = None
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view_default_1 = torch.ops.aten.view.default(add_tensor, [4, 2]); add_tensor = None
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return view_default_1
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view = torch.ops.aten.view.default(a_1, [4, 2])
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add = torch.ops.aten.add.Tensor(a_1, ones); ones = None
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copy_ = torch.ops.aten.copy_.default(a_1, add); a_1 = None
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view_1 = torch.ops.aten.view.default(add, [4, 2]); add = None
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return view_1
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""")
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# Some ops that are mutable are neither inplace nor out= ops.
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@ -315,14 +315,14 @@ def forward(self, a_1):
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def forward(self, a_1):
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_fused_moving_avg_obs_fq_helper_functional_default = torch.ops.aten._fused_moving_avg_obs_fq_helper_functional.default(a_1, a_1, a_1, a_1, a_1, a_1, a_1, 1.0, 0, 1, 0)
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getitem = _fused_moving_avg_obs_fq_helper_functional_default[0]
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getitem_1 = _fused_moving_avg_obs_fq_helper_functional_default[1]
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getitem_2 = _fused_moving_avg_obs_fq_helper_functional_default[2]
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getitem_3 = _fused_moving_avg_obs_fq_helper_functional_default[3]
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getitem_4 = _fused_moving_avg_obs_fq_helper_functional_default[4]
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getitem_5 = _fused_moving_avg_obs_fq_helper_functional_default[5]; _fused_moving_avg_obs_fq_helper_functional_default = None
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copy__default = torch.ops.aten.copy_.default(a_1, getitem_5); a_1 = getitem_5 = None
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_fused_moving_avg_obs_fq_helper_functional = torch.ops.aten._fused_moving_avg_obs_fq_helper_functional.default(a_1, a_1, a_1, a_1, a_1, a_1, a_1, 1.0, 0, 1, 0)
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getitem = _fused_moving_avg_obs_fq_helper_functional[0]
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getitem_1 = _fused_moving_avg_obs_fq_helper_functional[1]
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getitem_2 = _fused_moving_avg_obs_fq_helper_functional[2]
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getitem_3 = _fused_moving_avg_obs_fq_helper_functional[3]
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getitem_4 = _fused_moving_avg_obs_fq_helper_functional[4]
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getitem_5 = _fused_moving_avg_obs_fq_helper_functional[5]; _fused_moving_avg_obs_fq_helper_functional = None
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copy_ = torch.ops.aten.copy_.default(a_1, getitem_5); a_1 = getitem_5 = None
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return (getitem, getitem_1)
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""") # noqa: B950
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@ -338,11 +338,11 @@ def forward(self, a_1):
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def forward(self, a_1):
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as_strided_copy_default = torch.ops.aten.as_strided_copy.default(a_1, [2], [2], 1)
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add_tensor = torch.ops.aten.add.Tensor(as_strided_copy_default, 1); as_strided_copy_default = None
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as_strided_scatter_default = torch.ops.aten.as_strided_scatter.default(a_1, add_tensor, [2], [2], 1); add_tensor = None
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copy__default = torch.ops.aten.copy_.default(a_1, as_strided_scatter_default); a_1 = None
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return as_strided_scatter_default
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as_strided_copy = torch.ops.aten.as_strided_copy.default(a_1, [2], [2], 1)
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add = torch.ops.aten.add.Tensor(as_strided_copy, 1); as_strided_copy = None
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as_strided_scatter = torch.ops.aten.as_strided_scatter.default(a_1, add, [2], [2], 1); add = None
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copy_ = torch.ops.aten.copy_.default(a_1, as_strided_scatter); a_1 = None
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return as_strided_scatter
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""")
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def test_tensor_list_composite(self):
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@ -357,8 +357,8 @@ def forward(self, a_1):
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def forward(self, a_1):
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block_diag_default = torch.ops.aten.block_diag.default([a_1, a_1]); a_1 = None
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return block_diag_default
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block_diag = torch.ops.aten.block_diag.default([a_1, a_1]); a_1 = None
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return block_diag
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""")
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def test_cat(self):
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@ -374,8 +374,8 @@ def forward(self, a_1):
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def forward(self, a_1):
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empty = torch.ops.aten.empty.memory_format([0], device = device(type='cpu'), pin_memory = False)
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cat_default = torch.ops.aten.cat.default([a_1]); a_1 = None
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return cat_default
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cat = torch.ops.aten.cat.default([a_1]); a_1 = None
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return cat
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""")
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reinplaced_logs = self.get_logs(f, torch.ones(2, 2), reapply_views=True, run_reinplace=True)
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@ -385,8 +385,8 @@ def forward(self, a_1):
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def forward(self, a_1):
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empty = torch.ops.aten.empty.memory_format([0], device = device(type='cpu'), pin_memory = False)
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cat_default = torch.ops.aten.cat.default([a_1]); a_1 = None
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return cat_default
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cat = torch.ops.aten.cat.default([a_1]); a_1 = None
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return cat
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""")
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@ -406,11 +406,11 @@ def forward(self, a_1):
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def forward(self, a_1):
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ones = torch.ops.aten.ones.default([2], device = device(type='cpu'), pin_memory = False)
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clone_default = torch.ops.aten.clone.default(a_1)
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diagonal_copy_default = torch.ops.aten.diagonal_copy.default(clone_default); clone_default = None
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add_tensor = torch.ops.aten.add.Tensor(diagonal_copy_default, ones); diagonal_copy_default = ones = None
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mul_tensor = torch.ops.aten.mul.Tensor(a_1, a_1); a_1 = None
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return mul_tensor
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clone = torch.ops.aten.clone.default(a_1)
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diagonal_copy = torch.ops.aten.diagonal_copy.default(clone); clone = None
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add = torch.ops.aten.add.Tensor(diagonal_copy, ones); diagonal_copy = ones = None
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mul = torch.ops.aten.mul.Tensor(a_1, a_1); a_1 = None
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return mul
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""")
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reinplaced_logs = self.get_logs(f, torch.ones(2, 2), reapply_views=True, run_reinplace=True)
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@ -420,11 +420,11 @@ def forward(self, a_1):
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def forward(self, a_1):
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ones = torch.ops.aten.ones.default([2], device = device(type='cpu'), pin_memory = False)
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clone_default = torch.ops.aten.clone.default(a_1)
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diagonal_default = torch.ops.aten.diagonal.default(clone_default); clone_default = None
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add_tensor = torch.ops.aten.add_.Tensor(diagonal_default, ones); diagonal_default = ones = None
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mul_tensor = torch.ops.aten.mul.Tensor(a_1, a_1); a_1 = None
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return mul_tensor
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clone = torch.ops.aten.clone.default(a_1)
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diagonal = torch.ops.aten.diagonal.default(clone); clone = None
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add = torch.ops.aten.add_.Tensor(diagonal, ones); diagonal = ones = None
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mul = torch.ops.aten.mul.Tensor(a_1, a_1); a_1 = None
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return mul
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""")
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def test_diagonal_mutated_input(self):
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@ -443,11 +443,11 @@ def forward(self, a_1):
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def forward(self, a_1):
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ones = torch.ops.aten.ones.default([2], device = device(type='cpu'), pin_memory = False)
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diagonal_copy_default = torch.ops.aten.diagonal_copy.default(a_1)
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add_tensor = torch.ops.aten.add.Tensor(diagonal_copy_default, ones); diagonal_copy_default = ones = None
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diagonal_scatter_default = torch.ops.aten.diagonal_scatter.default(a_1, add_tensor); add_tensor = None
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copy__default = torch.ops.aten.copy_.default(a_1, diagonal_scatter_default); a_1 = None
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return diagonal_scatter_default
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diagonal_copy = torch.ops.aten.diagonal_copy.default(a_1)
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add = torch.ops.aten.add.Tensor(diagonal_copy, ones); diagonal_copy = ones = None
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diagonal_scatter = torch.ops.aten.diagonal_scatter.default(a_1, add); add = None
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copy_ = torch.ops.aten.copy_.default(a_1, diagonal_scatter); a_1 = None
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return diagonal_scatter
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""")
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def test_split(self):
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@ -467,19 +467,19 @@ def forward(self, a_1):
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def forward(self, a_1):
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ones = torch.ops.aten.ones.default([2], device = device(type='cpu'), pin_memory = False)
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split_copy_tensor = torch.ops.aten.split_copy.Tensor(a_1, 2)
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getitem = split_copy_tensor[0]
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getitem_1 = split_copy_tensor[1]; split_copy_tensor = None
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diagonal_copy_default = torch.ops.aten.diagonal_copy.default(getitem_1); getitem_1 = None
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add_tensor = torch.ops.aten.add.Tensor(diagonal_copy_default, ones); diagonal_copy_default = ones = None
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split_copy_tensor_1 = torch.ops.aten.split_copy.Tensor(a_1, 2)
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getitem_2 = split_copy_tensor_1[0]
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getitem_3 = split_copy_tensor_1[1]; split_copy_tensor_1 = None
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diagonal_scatter_default = torch.ops.aten.diagonal_scatter.default(getitem_3, add_tensor); getitem_3 = None
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slice_scatter_default = torch.ops.aten.slice_scatter.default(a_1, diagonal_scatter_default, 0, 2, 4); diagonal_scatter_default = None
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mul_tensor = torch.ops.aten.mul.Tensor(slice_scatter_default, slice_scatter_default)
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copy__default = torch.ops.aten.copy_.default(a_1, slice_scatter_default); a_1 = slice_scatter_default = None
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return add_tensor
|
||||
split_copy = torch.ops.aten.split_copy.Tensor(a_1, 2)
|
||||
getitem = split_copy[0]
|
||||
getitem_1 = split_copy[1]; split_copy = None
|
||||
diagonal_copy = torch.ops.aten.diagonal_copy.default(getitem_1); getitem_1 = None
|
||||
add = torch.ops.aten.add.Tensor(diagonal_copy, ones); diagonal_copy = ones = None
|
||||
split_copy_1 = torch.ops.aten.split_copy.Tensor(a_1, 2)
|
||||
getitem_2 = split_copy_1[0]
|
||||
getitem_3 = split_copy_1[1]; split_copy_1 = None
|
||||
diagonal_scatter = torch.ops.aten.diagonal_scatter.default(getitem_3, add); getitem_3 = None
|
||||
slice_scatter = torch.ops.aten.slice_scatter.default(a_1, diagonal_scatter, 0, 2, 4); diagonal_scatter = None
|
||||
mul = torch.ops.aten.mul.Tensor(slice_scatter, slice_scatter)
|
||||
copy_ = torch.ops.aten.copy_.default(a_1, slice_scatter); a_1 = slice_scatter = None
|
||||
return add
|
||||
""") # noqa: B950
|
||||
|
||||
def test_view_inplace(self):
|
||||
@ -498,14 +498,14 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
ones = torch.ops.aten.ones.default([4], device = device(type='cpu'), pin_memory = False)
|
||||
transpose_copy_int = torch.ops.aten.transpose_copy.int(a_1, 1, 0)
|
||||
select_copy_int = torch.ops.aten.select_copy.int(transpose_copy_int, 0, 0); transpose_copy_int = None
|
||||
add_tensor = torch.ops.aten.add.Tensor(select_copy_int, ones); select_copy_int = ones = None
|
||||
transpose_copy_int_1 = torch.ops.aten.transpose_copy.int(a_1, 1, 0); a_1 = None
|
||||
select_scatter_default = torch.ops.aten.select_scatter.default(transpose_copy_int_1, add_tensor, 0, 0); transpose_copy_int_1 = add_tensor = None
|
||||
transpose_copy_int_2 = torch.ops.aten.transpose_copy.int(select_scatter_default, 1, 0); select_scatter_default = None
|
||||
transpose_copy_int_3 = torch.ops.aten.transpose_copy.int(transpose_copy_int_2, 1, 0); transpose_copy_int_2 = None
|
||||
return transpose_copy_int_3
|
||||
transpose_copy = torch.ops.aten.transpose_copy.int(a_1, 1, 0)
|
||||
select_copy = torch.ops.aten.select_copy.int(transpose_copy, 0, 0); transpose_copy = None
|
||||
add = torch.ops.aten.add.Tensor(select_copy, ones); select_copy = ones = None
|
||||
transpose_copy_1 = torch.ops.aten.transpose_copy.int(a_1, 1, 0); a_1 = None
|
||||
select_scatter = torch.ops.aten.select_scatter.default(transpose_copy_1, add, 0, 0); transpose_copy_1 = add = None
|
||||
transpose_copy_2 = torch.ops.aten.transpose_copy.int(select_scatter, 1, 0); select_scatter = None
|
||||
transpose_copy_3 = torch.ops.aten.transpose_copy.int(transpose_copy_2, 1, 0); transpose_copy_2 = None
|
||||
return transpose_copy_3
|
||||
""") # noqa: B950
|
||||
|
||||
def test_optional_tensor_list(self):
|
||||
@ -524,13 +524,13 @@ def forward(self, a_1):
|
||||
|
||||
|
||||
def forward(self, a_1):
|
||||
view_copy_default = torch.ops.aten.view_copy.default(a_1, [8])
|
||||
view_copy = torch.ops.aten.view_copy.default(a_1, [8])
|
||||
arange = torch.ops.aten.arange.default(4, device = device(type='cpu'), pin_memory = False)
|
||||
arange_1 = torch.ops.aten.arange.default(4, dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
|
||||
index_put_default = torch.ops.aten.index_put.default(view_copy_default, [arange], arange_1); view_copy_default = arange = arange_1 = None
|
||||
view_copy_default_1 = torch.ops.aten.view_copy.default(index_put_default, [4, 2])
|
||||
copy__default = torch.ops.aten.copy_.default(a_1, view_copy_default_1); a_1 = view_copy_default_1 = None
|
||||
return index_put_default
|
||||
index_put = torch.ops.aten.index_put.default(view_copy, [arange], arange_1); view_copy = arange = arange_1 = None
|
||||
view_copy_1 = torch.ops.aten.view_copy.default(index_put, [4, 2])
|
||||
copy_ = torch.ops.aten.copy_.default(a_1, view_copy_1); a_1 = view_copy_1 = None
|
||||
return index_put
|
||||
""") # noqa: B950
|
||||
|
||||
def test_scalars(self):
|
||||
@ -550,13 +550,13 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False)
|
||||
view_copy_default = torch.ops.aten.view_copy.default(a_1, [4, 2])
|
||||
add_tensor = torch.ops.aten.add.Tensor(view_copy_default, 1); view_copy_default = None
|
||||
mul_tensor = torch.ops.aten.mul.Tensor(add_tensor, 2)
|
||||
div_tensor = torch.ops.aten.div.Tensor(mul_tensor, 1); mul_tensor = None
|
||||
view_copy_default_1 = torch.ops.aten.view_copy.default(add_tensor, [4, 2]); add_tensor = None
|
||||
copy__default = torch.ops.aten.copy_.default(a_1, view_copy_default_1); a_1 = view_copy_default_1 = None
|
||||
return div_tensor
|
||||
view_copy = torch.ops.aten.view_copy.default(a_1, [4, 2])
|
||||
add = torch.ops.aten.add.Tensor(view_copy, 1); view_copy = None
|
||||
mul = torch.ops.aten.mul.Tensor(add, 2)
|
||||
div = torch.ops.aten.div.Tensor(mul, 1); mul = None
|
||||
view_copy_1 = torch.ops.aten.view_copy.default(add, [4, 2]); add = None
|
||||
copy_ = torch.ops.aten.copy_.default(a_1, view_copy_1); a_1 = view_copy_1 = None
|
||||
return div
|
||||
""")
|
||||
|
||||
@skipIfTorchDynamo("Test does not work with TorchDynamo")
|
||||
@ -574,10 +574,10 @@ def forward(self, a_1):
|
||||
|
||||
|
||||
def forward(self, a_1):
|
||||
clone_default = torch.ops.aten.clone.default(a_1); a_1 = None
|
||||
ge_scalar = torch.ops.aten.ge.Scalar(clone_default, 0); clone_default = None
|
||||
_to_copy_default = torch.ops.aten._to_copy.default(ge_scalar, dtype = torch.float32, layout = torch.strided); ge_scalar = None
|
||||
return _to_copy_default
|
||||
clone = torch.ops.aten.clone.default(a_1); a_1 = None
|
||||
ge = torch.ops.aten.ge.Scalar(clone, 0); clone = None
|
||||
_to_copy = torch.ops.aten._to_copy.default(ge, dtype = torch.float32, layout = torch.strided); ge = None
|
||||
return _to_copy
|
||||
""")
|
||||
|
||||
reinplaced_logs = self.get_logs(f, torch.ones(2, 2), reapply_views=True, run_reinplace=True)
|
||||
@ -586,10 +586,10 @@ def forward(self, a_1):
|
||||
|
||||
|
||||
def forward(self, a_1):
|
||||
clone_default = torch.ops.aten.clone.default(a_1); a_1 = None
|
||||
ge_scalar = torch.ops.aten.ge.Scalar(clone_default, 0); clone_default = None
|
||||
_to_copy_default = torch.ops.aten._to_copy.default(ge_scalar, dtype = torch.float32, layout = torch.strided); ge_scalar = None
|
||||
return _to_copy_default
|
||||
clone = torch.ops.aten.clone.default(a_1); a_1 = None
|
||||
ge = torch.ops.aten.ge.Scalar(clone, 0); clone = None
|
||||
_to_copy = torch.ops.aten._to_copy.default(ge, dtype = torch.float32, layout = torch.strided); ge = None
|
||||
return _to_copy
|
||||
""") # noqa: B950
|
||||
|
||||
@skipIfTorchDynamo("Test does not work with TorchDynamo")
|
||||
@ -622,8 +622,8 @@ def forward(self, a_1):
|
||||
|
||||
|
||||
def forward(self, a_1):
|
||||
view_copy_default = torch.ops.aten.view_copy.default(a_1, [4, 2]); a_1 = None
|
||||
return view_copy_default
|
||||
view_copy = torch.ops.aten.view_copy.default(a_1, [4, 2]); a_1 = None
|
||||
return view_copy
|
||||
""")
|
||||
|
||||
def test_everything(self):
|
||||
@ -648,35 +648,35 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
ones = torch.ops.aten.ones.default([2, 2], device = device(type='cpu'), pin_memory = False)
|
||||
add_tensor = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
|
||||
view_copy_default = torch.ops.aten.view_copy.default(add_tensor, [8])
|
||||
_reshape_alias_copy_default = torch.ops.aten._reshape_alias_copy.default(view_copy_default, [2, 4], [4, 1]); view_copy_default = None
|
||||
transpose_copy_int = torch.ops.aten.transpose_copy.int(_reshape_alias_copy_default, 1, 0)
|
||||
unsqueeze_copy_default = torch.ops.aten.unsqueeze_copy.default(transpose_copy_int, 0); transpose_copy_int = None
|
||||
squeeze_copy_default = torch.ops.aten.squeeze_copy.default(unsqueeze_copy_default); unsqueeze_copy_default = None
|
||||
split_copy_tensor = torch.ops.aten.split_copy.Tensor(squeeze_copy_default, 2); squeeze_copy_default = None
|
||||
getitem = split_copy_tensor[0]
|
||||
getitem_1 = split_copy_tensor[1]; split_copy_tensor = None
|
||||
add_tensor_1 = torch.ops.aten.add.Tensor(getitem, ones); getitem = ones = None
|
||||
select_copy_int = torch.ops.aten.select_copy.int(_reshape_alias_copy_default, 0, 0); _reshape_alias_copy_default = None
|
||||
clone_default = torch.ops.aten.clone.default(add_tensor_1, memory_format = torch.contiguous_format)
|
||||
_unsafe_view_default = torch.ops.aten._unsafe_view.default(clone_default, [4]); clone_default = None
|
||||
view_copy_default_1 = torch.ops.aten.view_copy.default(add_tensor, [8]); add_tensor = None
|
||||
_reshape_alias_copy_default_1 = torch.ops.aten._reshape_alias_copy.default(view_copy_default_1, [2, 4], [4, 1]); view_copy_default_1 = None
|
||||
transpose_copy_int_1 = torch.ops.aten.transpose_copy.int(_reshape_alias_copy_default_1, 1, 0); _reshape_alias_copy_default_1 = None
|
||||
unsqueeze_copy_default_1 = torch.ops.aten.unsqueeze_copy.default(transpose_copy_int_1, 0); transpose_copy_int_1 = None
|
||||
squeeze_copy_default_1 = torch.ops.aten.squeeze_copy.default(unsqueeze_copy_default_1); unsqueeze_copy_default_1 = None
|
||||
slice_scatter_default = torch.ops.aten.slice_scatter.default(squeeze_copy_default_1, add_tensor_1, 0, 0, 2); squeeze_copy_default_1 = None
|
||||
unsqueeze_copy_default_2 = torch.ops.aten.unsqueeze_copy.default(slice_scatter_default, 0); slice_scatter_default = None
|
||||
squeeze_copy_dim = torch.ops.aten.squeeze_copy.dim(unsqueeze_copy_default_2, 0); unsqueeze_copy_default_2 = None
|
||||
transpose_copy_int_2 = torch.ops.aten.transpose_copy.int(squeeze_copy_dim, 1, 0); squeeze_copy_dim = None
|
||||
_reshape_alias_copy_default_2 = torch.ops.aten._reshape_alias_copy.default(transpose_copy_int_2, [8], [1]); transpose_copy_int_2 = None
|
||||
view_copy_default_2 = torch.ops.aten.view_copy.default(_reshape_alias_copy_default_2, [4, 2]); _reshape_alias_copy_default_2 = None
|
||||
view_copy_default_3 = torch.ops.aten.view_copy.default(view_copy_default_2, [8]); view_copy_default_2 = None
|
||||
_reshape_alias_copy_default_3 = torch.ops.aten._reshape_alias_copy.default(view_copy_default_3, [2, 4], [4, 1]); view_copy_default_3 = None
|
||||
select_copy_int_1 = torch.ops.aten.select_copy.int(_reshape_alias_copy_default_3, 0, 0); _reshape_alias_copy_default_3 = None
|
||||
add_tensor_2 = torch.ops.aten.add.Tensor(select_copy_int_1, _unsafe_view_default); select_copy_int_1 = _unsafe_view_default = None
|
||||
return add_tensor_1
|
||||
add = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
|
||||
view_copy = torch.ops.aten.view_copy.default(add, [8])
|
||||
_reshape_alias_copy = torch.ops.aten._reshape_alias_copy.default(view_copy, [2, 4], [4, 1]); view_copy = None
|
||||
transpose_copy = torch.ops.aten.transpose_copy.int(_reshape_alias_copy, 1, 0)
|
||||
unsqueeze_copy = torch.ops.aten.unsqueeze_copy.default(transpose_copy, 0); transpose_copy = None
|
||||
squeeze_copy = torch.ops.aten.squeeze_copy.default(unsqueeze_copy); unsqueeze_copy = None
|
||||
split_copy = torch.ops.aten.split_copy.Tensor(squeeze_copy, 2); squeeze_copy = None
|
||||
getitem = split_copy[0]
|
||||
getitem_1 = split_copy[1]; split_copy = None
|
||||
add_1 = torch.ops.aten.add.Tensor(getitem, ones); getitem = ones = None
|
||||
select_copy = torch.ops.aten.select_copy.int(_reshape_alias_copy, 0, 0); _reshape_alias_copy = None
|
||||
clone = torch.ops.aten.clone.default(add_1, memory_format = torch.contiguous_format)
|
||||
_unsafe_view = torch.ops.aten._unsafe_view.default(clone, [4]); clone = None
|
||||
view_copy_1 = torch.ops.aten.view_copy.default(add, [8]); add = None
|
||||
_reshape_alias_copy_1 = torch.ops.aten._reshape_alias_copy.default(view_copy_1, [2, 4], [4, 1]); view_copy_1 = None
|
||||
transpose_copy_1 = torch.ops.aten.transpose_copy.int(_reshape_alias_copy_1, 1, 0); _reshape_alias_copy_1 = None
|
||||
unsqueeze_copy_1 = torch.ops.aten.unsqueeze_copy.default(transpose_copy_1, 0); transpose_copy_1 = None
|
||||
squeeze_copy_1 = torch.ops.aten.squeeze_copy.default(unsqueeze_copy_1); unsqueeze_copy_1 = None
|
||||
slice_scatter = torch.ops.aten.slice_scatter.default(squeeze_copy_1, add_1, 0, 0, 2); squeeze_copy_1 = None
|
||||
unsqueeze_copy_2 = torch.ops.aten.unsqueeze_copy.default(slice_scatter, 0); slice_scatter = None
|
||||
squeeze_copy_2 = torch.ops.aten.squeeze_copy.dim(unsqueeze_copy_2, 0); unsqueeze_copy_2 = None
|
||||
transpose_copy_2 = torch.ops.aten.transpose_copy.int(squeeze_copy_2, 1, 0); squeeze_copy_2 = None
|
||||
_reshape_alias_copy_2 = torch.ops.aten._reshape_alias_copy.default(transpose_copy_2, [8], [1]); transpose_copy_2 = None
|
||||
view_copy_2 = torch.ops.aten.view_copy.default(_reshape_alias_copy_2, [4, 2]); _reshape_alias_copy_2 = None
|
||||
view_copy_3 = torch.ops.aten.view_copy.default(view_copy_2, [8]); view_copy_2 = None
|
||||
_reshape_alias_copy_3 = torch.ops.aten._reshape_alias_copy.default(view_copy_3, [2, 4], [4, 1]); view_copy_3 = None
|
||||
select_copy_1 = torch.ops.aten.select_copy.int(_reshape_alias_copy_3, 0, 0); _reshape_alias_copy_3 = None
|
||||
add_2 = torch.ops.aten.add.Tensor(select_copy_1, _unsafe_view); select_copy_1 = _unsafe_view = None
|
||||
return add_1
|
||||
""") # noqa: B950
|
||||
|
||||
reinplaced_logs = self.get_logs(f, torch.ones(4, 2), reapply_views=True, run_reinplace=True)
|
||||
@ -686,33 +686,33 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
ones = torch.ops.aten.ones.default([2, 2], device = device(type='cpu'), pin_memory = False)
|
||||
add_tensor = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
|
||||
view_default = torch.ops.aten.view.default(add_tensor, [8])
|
||||
_reshape_alias_default = torch.ops.aten._reshape_alias.default(view_default, [2, 4], [4, 1]); view_default = None
|
||||
transpose_int = torch.ops.aten.transpose.int(_reshape_alias_default, 1, 0)
|
||||
unsqueeze_default = torch.ops.aten.unsqueeze.default(transpose_int, 0); transpose_int = None
|
||||
squeeze_default = torch.ops.aten.squeeze.default(unsqueeze_default); unsqueeze_default = None
|
||||
split_tensor = torch.ops.aten.split.Tensor(squeeze_default, 2); squeeze_default = None
|
||||
getitem = split_tensor[0]
|
||||
getitem_1 = split_tensor[1]; split_tensor = None
|
||||
add_tensor_1 = torch.ops.aten.add_.Tensor(getitem, ones); ones = None
|
||||
select_int = torch.ops.aten.select.int(_reshape_alias_default, 0, 0); _reshape_alias_default = None
|
||||
clone_default = torch.ops.aten.clone.default(getitem, memory_format = torch.contiguous_format)
|
||||
_unsafe_view_default = torch.ops.aten._unsafe_view.default(clone_default, [4]); clone_default = None
|
||||
view_default_1 = torch.ops.aten.view.default(add_tensor, [8]); add_tensor = None
|
||||
_reshape_alias_default_1 = torch.ops.aten._reshape_alias.default(view_default_1, [2, 4], [4, 1]); view_default_1 = None
|
||||
transpose_int_1 = torch.ops.aten.transpose.int(_reshape_alias_default_1, 1, 0); _reshape_alias_default_1 = None
|
||||
unsqueeze_default_1 = torch.ops.aten.unsqueeze.default(transpose_int_1, 0); transpose_int_1 = None
|
||||
squeeze_default_1 = torch.ops.aten.squeeze.default(unsqueeze_default_1); unsqueeze_default_1 = None
|
||||
unsqueeze_default_2 = torch.ops.aten.unsqueeze.default(squeeze_default_1, 0); squeeze_default_1 = None
|
||||
squeeze_dim = torch.ops.aten.squeeze.dim(unsqueeze_default_2, 0); unsqueeze_default_2 = None
|
||||
transpose_int_2 = torch.ops.aten.transpose.int(squeeze_dim, 1, 0); squeeze_dim = None
|
||||
_reshape_alias_default_2 = torch.ops.aten._reshape_alias.default(transpose_int_2, [8], [1]); transpose_int_2 = None
|
||||
view_default_2 = torch.ops.aten.view.default(_reshape_alias_default_2, [4, 2]); _reshape_alias_default_2 = None
|
||||
view_default_3 = torch.ops.aten.view.default(view_default_2, [8]); view_default_2 = None
|
||||
_reshape_alias_default_3 = torch.ops.aten._reshape_alias.default(view_default_3, [2, 4], [4, 1]); view_default_3 = None
|
||||
select_int_1 = torch.ops.aten.select.int(_reshape_alias_default_3, 0, 0); _reshape_alias_default_3 = None
|
||||
add_tensor_2 = torch.ops.aten.add.Tensor(select_int_1, _unsafe_view_default); select_int_1 = _unsafe_view_default = None
|
||||
add = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
|
||||
view = torch.ops.aten.view.default(add, [8])
|
||||
_reshape_alias = torch.ops.aten._reshape_alias.default(view, [2, 4], [4, 1]); view = None
|
||||
transpose = torch.ops.aten.transpose.int(_reshape_alias, 1, 0)
|
||||
unsqueeze = torch.ops.aten.unsqueeze.default(transpose, 0); transpose = None
|
||||
squeeze = torch.ops.aten.squeeze.default(unsqueeze); unsqueeze = None
|
||||
split = torch.ops.aten.split.Tensor(squeeze, 2); squeeze = None
|
||||
getitem = split[0]
|
||||
getitem_1 = split[1]; split = None
|
||||
add_1 = torch.ops.aten.add_.Tensor(getitem, ones); ones = None
|
||||
select = torch.ops.aten.select.int(_reshape_alias, 0, 0); _reshape_alias = None
|
||||
clone = torch.ops.aten.clone.default(getitem, memory_format = torch.contiguous_format)
|
||||
_unsafe_view = torch.ops.aten._unsafe_view.default(clone, [4]); clone = None
|
||||
view_1 = torch.ops.aten.view.default(add, [8]); add = None
|
||||
_reshape_alias_1 = torch.ops.aten._reshape_alias.default(view_1, [2, 4], [4, 1]); view_1 = None
|
||||
transpose_1 = torch.ops.aten.transpose.int(_reshape_alias_1, 1, 0); _reshape_alias_1 = None
|
||||
unsqueeze_1 = torch.ops.aten.unsqueeze.default(transpose_1, 0); transpose_1 = None
|
||||
squeeze_1 = torch.ops.aten.squeeze.default(unsqueeze_1); unsqueeze_1 = None
|
||||
unsqueeze_2 = torch.ops.aten.unsqueeze.default(squeeze_1, 0); squeeze_1 = None
|
||||
squeeze_2 = torch.ops.aten.squeeze.dim(unsqueeze_2, 0); unsqueeze_2 = None
|
||||
transpose_2 = torch.ops.aten.transpose.int(squeeze_2, 1, 0); squeeze_2 = None
|
||||
_reshape_alias_2 = torch.ops.aten._reshape_alias.default(transpose_2, [8], [1]); transpose_2 = None
|
||||
view_2 = torch.ops.aten.view.default(_reshape_alias_2, [4, 2]); _reshape_alias_2 = None
|
||||
view_3 = torch.ops.aten.view.default(view_2, [8]); view_2 = None
|
||||
_reshape_alias_3 = torch.ops.aten._reshape_alias.default(view_3, [2, 4], [4, 1]); view_3 = None
|
||||
select_1 = torch.ops.aten.select.int(_reshape_alias_3, 0, 0); _reshape_alias_3 = None
|
||||
add_2 = torch.ops.aten.add.Tensor(select_1, _unsafe_view); select_1 = _unsafe_view = None
|
||||
return getitem
|
||||
""")
|
||||
|
||||
@ -731,12 +731,12 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False)
|
||||
view_default = torch.ops.aten.view.default(a_1, [4, 2])
|
||||
add_tensor = torch.ops.aten.add.Tensor(view_default, ones); view_default = ones = None
|
||||
view_default_1 = torch.ops.aten.view.default(add_tensor, [4, 2])
|
||||
mul_tensor = torch.ops.aten.mul.Tensor(view_default_1, view_default_1)
|
||||
copy__default = torch.ops.aten.copy_.default(a_1, view_default_1); a_1 = view_default_1 = None
|
||||
return add_tensor
|
||||
view = torch.ops.aten.view.default(a_1, [4, 2])
|
||||
add = torch.ops.aten.add.Tensor(view, ones); view = ones = None
|
||||
view_1 = torch.ops.aten.view.default(add, [4, 2])
|
||||
mul = torch.ops.aten.mul.Tensor(view_1, view_1)
|
||||
copy_ = torch.ops.aten.copy_.default(a_1, view_1); a_1 = view_1 = None
|
||||
return add
|
||||
""")
|
||||
|
||||
def test_aliases_maintained_after_pass_when_reapplying_views(self):
|
||||
@ -781,9 +781,9 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False)
|
||||
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(zeros); zeros = None
|
||||
add_tensor = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
|
||||
return add_tensor
|
||||
diagonal_copy = torch.ops.aten.diagonal_copy.default(zeros); zeros = None
|
||||
add = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
|
||||
return add
|
||||
""")
|
||||
|
||||
reinplaced_logs = self.get_logs(f, torch.ones(2), reapply_views=True, run_reinplace=True)
|
||||
@ -793,9 +793,9 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False)
|
||||
diagonal_default = torch.ops.aten.diagonal.default(zeros); zeros = None
|
||||
add_tensor = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
|
||||
return add_tensor
|
||||
diagonal = torch.ops.aten.diagonal.default(zeros); zeros = None
|
||||
add = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
|
||||
return add
|
||||
""")
|
||||
|
||||
# Test 2: copy_() with same dtype, different shape
|
||||
@ -807,10 +807,10 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False)
|
||||
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(zeros); zeros = None
|
||||
expand_copy_default = torch.ops.aten.expand_copy.default(a_1, [2])
|
||||
add_tensor = torch.ops.aten.add.Tensor(expand_copy_default, a_1); expand_copy_default = a_1 = None
|
||||
return add_tensor
|
||||
diagonal_copy = torch.ops.aten.diagonal_copy.default(zeros); zeros = None
|
||||
expand_copy = torch.ops.aten.expand_copy.default(a_1, [2])
|
||||
add = torch.ops.aten.add.Tensor(expand_copy, a_1); expand_copy = a_1 = None
|
||||
return add
|
||||
""")
|
||||
|
||||
reinplaced_logs = self.get_logs(f, torch.ones(1), reapply_views=True, run_reinplace=True)
|
||||
@ -820,10 +820,10 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False)
|
||||
diagonal_default = torch.ops.aten.diagonal.default(zeros); zeros = None
|
||||
expand_copy_default = torch.ops.aten.expand_copy.default(a_1, [2])
|
||||
add_tensor = torch.ops.aten.add_.Tensor(expand_copy_default, a_1); a_1 = None
|
||||
return expand_copy_default
|
||||
diagonal = torch.ops.aten.diagonal.default(zeros); zeros = None
|
||||
expand_copy = torch.ops.aten.expand_copy.default(a_1, [2])
|
||||
add = torch.ops.aten.add_.Tensor(expand_copy, a_1); a_1 = None
|
||||
return expand_copy
|
||||
""")
|
||||
|
||||
# Test 3: copy_() with different dtype, same shape
|
||||
@ -835,10 +835,10 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False)
|
||||
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(zeros); zeros = None
|
||||
_to_copy_default = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
|
||||
add_tensor = torch.ops.aten.add.Tensor(_to_copy_default, a_1); _to_copy_default = a_1 = None
|
||||
return add_tensor
|
||||
diagonal_copy = torch.ops.aten.diagonal_copy.default(zeros); zeros = None
|
||||
_to_copy = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
|
||||
add = torch.ops.aten.add.Tensor(_to_copy, a_1); _to_copy = a_1 = None
|
||||
return add
|
||||
""") # noqa: B950
|
||||
|
||||
reinplaced_logs = self.get_logs(f, torch.ones(2, dtype=torch.long), reapply_views=True, run_reinplace=True)
|
||||
@ -848,10 +848,10 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False)
|
||||
diagonal_default = torch.ops.aten.diagonal.default(zeros); zeros = None
|
||||
_to_copy_default = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
|
||||
add_tensor = torch.ops.aten.add_.Tensor(_to_copy_default, a_1); a_1 = None
|
||||
return _to_copy_default
|
||||
diagonal = torch.ops.aten.diagonal.default(zeros); zeros = None
|
||||
_to_copy = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
|
||||
add = torch.ops.aten.add_.Tensor(_to_copy, a_1); a_1 = None
|
||||
return _to_copy
|
||||
""") # noqa: B950
|
||||
|
||||
# Test 4: copy_() with different dtype, different shape
|
||||
@ -863,11 +863,11 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False)
|
||||
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(zeros); zeros = None
|
||||
_to_copy_default = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
|
||||
expand_copy_default = torch.ops.aten.expand_copy.default(_to_copy_default, [2]); _to_copy_default = None
|
||||
add_tensor = torch.ops.aten.add.Tensor(expand_copy_default, a_1); expand_copy_default = a_1 = None
|
||||
return add_tensor
|
||||
diagonal_copy = torch.ops.aten.diagonal_copy.default(zeros); zeros = None
|
||||
_to_copy = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
|
||||
expand_copy = torch.ops.aten.expand_copy.default(_to_copy, [2]); _to_copy = None
|
||||
add = torch.ops.aten.add.Tensor(expand_copy, a_1); expand_copy = a_1 = None
|
||||
return add
|
||||
""") # noqa: B950
|
||||
|
||||
reinplaced_logs = self.get_logs(f, torch.ones(1, dtype=torch.long), reapply_views=True, run_reinplace=True)
|
||||
@ -877,11 +877,11 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False)
|
||||
diagonal_default = torch.ops.aten.diagonal.default(zeros); zeros = None
|
||||
_to_copy_default = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
|
||||
expand_copy_default = torch.ops.aten.expand_copy.default(_to_copy_default, [2]); _to_copy_default = None
|
||||
add_tensor = torch.ops.aten.add_.Tensor(expand_copy_default, a_1); a_1 = None
|
||||
return expand_copy_default
|
||||
diagonal = torch.ops.aten.diagonal.default(zeros); zeros = None
|
||||
_to_copy = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
|
||||
expand_copy = torch.ops.aten.expand_copy.default(_to_copy, [2]); _to_copy = None
|
||||
add = torch.ops.aten.add_.Tensor(expand_copy, a_1); a_1 = None
|
||||
return expand_copy
|
||||
""") # noqa: B950
|
||||
|
||||
def test_expand_symint(self):
|
||||
@ -897,8 +897,8 @@ def forward(self, a_1):
|
||||
|
||||
|
||||
def forward(self, a_1):
|
||||
expand_copy_default = torch.ops.aten.expand_copy.default(a_1, [2, 2]); a_1 = None
|
||||
return expand_copy_default
|
||||
expand_copy = torch.ops.aten.expand_copy.default(a_1, [2, 2]); a_1 = None
|
||||
return expand_copy
|
||||
""")
|
||||
|
||||
def test_fill_(self):
|
||||
@ -915,11 +915,11 @@ def forward(self, a_1):
|
||||
|
||||
|
||||
def forward(self, a_1):
|
||||
add_tensor = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
|
||||
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(add_tensor)
|
||||
fill_scalar = torch.ops.aten.fill.Scalar(diagonal_copy_default, 0); diagonal_copy_default = None
|
||||
diagonal_scatter_default = torch.ops.aten.diagonal_scatter.default(add_tensor, fill_scalar); add_tensor = fill_scalar = None
|
||||
return diagonal_scatter_default
|
||||
add = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
|
||||
diagonal_copy = torch.ops.aten.diagonal_copy.default(add)
|
||||
fill = torch.ops.aten.fill.Scalar(diagonal_copy, 0); diagonal_copy = None
|
||||
diagonal_scatter = torch.ops.aten.diagonal_scatter.default(add, fill); add = fill = None
|
||||
return diagonal_scatter
|
||||
""")
|
||||
|
||||
reinplaced_logs = self.get_logs(f, torch.ones(2, 2), reapply_views=True, run_reinplace=True)
|
||||
@ -928,10 +928,10 @@ def forward(self, a_1):
|
||||
|
||||
|
||||
def forward(self, a_1):
|
||||
add_tensor = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
|
||||
diagonal_default = torch.ops.aten.diagonal.default(add_tensor)
|
||||
fill_scalar = torch.ops.aten.fill_.Scalar(diagonal_default, 0); diagonal_default = None
|
||||
return add_tensor
|
||||
add = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
|
||||
diagonal = torch.ops.aten.diagonal.default(add)
|
||||
fill = torch.ops.aten.fill_.Scalar(diagonal, 0); diagonal = None
|
||||
return add
|
||||
""")
|
||||
|
||||
def test_resize_smaller(self):
|
||||
@ -952,21 +952,21 @@ def forward(self, a_1):
|
||||
|
||||
|
||||
def forward(self, a_1):
|
||||
add_tensor = torch.ops.aten.add.Tensor(a_1, 1); a_1 = None
|
||||
view_copy_default = torch.ops.aten.view_copy.default(add_tensor, [4, 4])
|
||||
resize_default = torch.ops.aten.resize.default(view_copy_default, [3, 3])
|
||||
as_strided_copy_default = torch.ops.aten.as_strided_copy.default(view_copy_default, [3, 3], [3, 1]); view_copy_default = None
|
||||
view_copy_default_1 = torch.ops.aten.view_copy.default(as_strided_copy_default, [-1]); as_strided_copy_default = None
|
||||
add_tensor_1 = torch.ops.aten.add.Tensor(view_copy_default_1, 1); view_copy_default_1 = None
|
||||
view_copy_default_2 = torch.ops.aten.view_copy.default(add_tensor, [4, 4]); add_tensor = None
|
||||
as_strided_copy_default_1 = torch.ops.aten.as_strided_copy.default(view_copy_default_2, [3, 3], [3, 1])
|
||||
view_copy_default_3 = torch.ops.aten.view_copy.default(add_tensor_1, [3, 3]); add_tensor_1 = None
|
||||
as_strided_scatter_default = torch.ops.aten.as_strided_scatter.default(view_copy_default_2, view_copy_default_3, [3, 3], [3, 1]); view_copy_default_2 = view_copy_default_3 = None
|
||||
view_copy_default_4 = torch.ops.aten.view_copy.default(as_strided_scatter_default, [8, 2]); as_strided_scatter_default = None
|
||||
view_copy_default_5 = torch.ops.aten.view_copy.default(view_copy_default_4, [4, 4]); view_copy_default_4 = None
|
||||
as_strided_copy_default_2 = torch.ops.aten.as_strided_copy.default(view_copy_default_5, [3, 3], [3, 1]); view_copy_default_5 = None
|
||||
add_tensor_2 = torch.ops.aten.add.Tensor(as_strided_copy_default_2, 1); as_strided_copy_default_2 = None
|
||||
return add_tensor_2
|
||||
add = torch.ops.aten.add.Tensor(a_1, 1); a_1 = None
|
||||
view_copy = torch.ops.aten.view_copy.default(add, [4, 4])
|
||||
resize = torch.ops.aten.resize.default(view_copy, [3, 3])
|
||||
as_strided_copy = torch.ops.aten.as_strided_copy.default(view_copy, [3, 3], [3, 1]); view_copy = None
|
||||
view_copy_1 = torch.ops.aten.view_copy.default(as_strided_copy, [-1]); as_strided_copy = None
|
||||
add_1 = torch.ops.aten.add.Tensor(view_copy_1, 1); view_copy_1 = None
|
||||
view_copy_2 = torch.ops.aten.view_copy.default(add, [4, 4]); add = None
|
||||
as_strided_copy_1 = torch.ops.aten.as_strided_copy.default(view_copy_2, [3, 3], [3, 1])
|
||||
view_copy_3 = torch.ops.aten.view_copy.default(add_1, [3, 3]); add_1 = None
|
||||
as_strided_scatter = torch.ops.aten.as_strided_scatter.default(view_copy_2, view_copy_3, [3, 3], [3, 1]); view_copy_2 = view_copy_3 = None
|
||||
view_copy_4 = torch.ops.aten.view_copy.default(as_strided_scatter, [8, 2]); as_strided_scatter = None
|
||||
view_copy_5 = torch.ops.aten.view_copy.default(view_copy_4, [4, 4]); view_copy_4 = None
|
||||
as_strided_copy_2 = torch.ops.aten.as_strided_copy.default(view_copy_5, [3, 3], [3, 1]); view_copy_5 = None
|
||||
add_2 = torch.ops.aten.add.Tensor(as_strided_copy_2, 1); as_strided_copy_2 = None
|
||||
return add_2
|
||||
""") # noqa: B950
|
||||
|
||||
reinplaced_logs = self.get_logs(f, torch.ones(8, 2), reapply_views=True, run_reinplace=True)
|
||||
@ -975,20 +975,20 @@ def forward(self, a_1):
|
||||
|
||||
|
||||
def forward(self, a_1):
|
||||
add_tensor = torch.ops.aten.add.Tensor(a_1, 1); a_1 = None
|
||||
view_default = torch.ops.aten.view.default(add_tensor, [4, 4])
|
||||
resize_default = torch.ops.aten.resize.default(view_default, [3, 3])
|
||||
as_strided_default = torch.ops.aten.as_strided.default(view_default, [3, 3], [3, 1]); view_default = None
|
||||
view_default_1 = torch.ops.aten.view.default(as_strided_default, [-1]); as_strided_default = None
|
||||
add_tensor_1 = torch.ops.aten.add_.Tensor(view_default_1, 1)
|
||||
view_default_2 = torch.ops.aten.view.default(add_tensor, [4, 4]); add_tensor = None
|
||||
as_strided_default_1 = torch.ops.aten.as_strided.default(view_default_2, [3, 3], [3, 1])
|
||||
view_default_3 = torch.ops.aten.view.default(view_default_1, [3, 3]); view_default_1 = None
|
||||
view_default_4 = torch.ops.aten.view.default(view_default_2, [8, 2]); view_default_2 = None
|
||||
view_default_5 = torch.ops.aten.view.default(view_default_4, [4, 4]); view_default_4 = None
|
||||
as_strided_default_2 = torch.ops.aten.as_strided.default(view_default_5, [3, 3], [3, 1]); view_default_5 = None
|
||||
add_tensor_2 = torch.ops.aten.add_.Tensor(as_strided_default_2, 1)
|
||||
return as_strided_default_2
|
||||
add = torch.ops.aten.add.Tensor(a_1, 1); a_1 = None
|
||||
view = torch.ops.aten.view.default(add, [4, 4])
|
||||
resize = torch.ops.aten.resize.default(view, [3, 3])
|
||||
as_strided = torch.ops.aten.as_strided.default(view, [3, 3], [3, 1]); view = None
|
||||
view_1 = torch.ops.aten.view.default(as_strided, [-1]); as_strided = None
|
||||
add_1 = torch.ops.aten.add_.Tensor(view_1, 1)
|
||||
view_2 = torch.ops.aten.view.default(add, [4, 4]); add = None
|
||||
as_strided_1 = torch.ops.aten.as_strided.default(view_2, [3, 3], [3, 1])
|
||||
view_3 = torch.ops.aten.view.default(view_1, [3, 3]); view_1 = None
|
||||
view_4 = torch.ops.aten.view.default(view_2, [8, 2]); view_2 = None
|
||||
view_5 = torch.ops.aten.view.default(view_4, [4, 4]); view_4 = None
|
||||
as_strided_2 = torch.ops.aten.as_strided.default(view_5, [3, 3], [3, 1]); view_5 = None
|
||||
add_2 = torch.ops.aten.add_.Tensor(as_strided_2, 1)
|
||||
return as_strided_2
|
||||
""")
|
||||
|
||||
def test_resize_larger_valid(self):
|
||||
@ -1015,13 +1015,13 @@ def forward(self, a_1):
|
||||
|
||||
|
||||
def forward(self, a_1):
|
||||
add_tensor = torch.ops.aten.add.Tensor(a_1, 1); a_1 = None
|
||||
resize_default = torch.ops.aten.resize.default(add_tensor, [5, 5]); add_tensor = None
|
||||
view_copy_default = torch.ops.aten.view_copy.default(resize_default, [25]); resize_default = None
|
||||
fill_scalar = torch.ops.aten.fill.Scalar(view_copy_default, 1); view_copy_default = None
|
||||
view_copy_default_1 = torch.ops.aten.view_copy.default(fill_scalar, [5, 5]); fill_scalar = None
|
||||
add_tensor_1 = torch.ops.aten.add.Tensor(view_copy_default_1, 1)
|
||||
return (view_copy_default_1, add_tensor_1)
|
||||
add = torch.ops.aten.add.Tensor(a_1, 1); a_1 = None
|
||||
resize = torch.ops.aten.resize.default(add, [5, 5]); add = None
|
||||
view_copy = torch.ops.aten.view_copy.default(resize, [25]); resize = None
|
||||
fill = torch.ops.aten.fill.Scalar(view_copy, 1); view_copy = None
|
||||
view_copy_1 = torch.ops.aten.view_copy.default(fill, [5, 5]); fill = None
|
||||
add_1 = torch.ops.aten.add.Tensor(view_copy_1, 1)
|
||||
return (view_copy_1, add_1)
|
||||
""")
|
||||
|
||||
reinplaced_logs = self.get_logs(f, torch.ones(8, 2), reapply_views=True, run_reinplace=True)
|
||||
@ -1030,13 +1030,13 @@ def forward(self, a_1):
|
||||
|
||||
|
||||
def forward(self, a_1):
|
||||
add_tensor = torch.ops.aten.add.Tensor(a_1, 1); a_1 = None
|
||||
resize_default = torch.ops.aten.resize_.default(add_tensor, [5, 5])
|
||||
view_default = torch.ops.aten.view.default(add_tensor, [25]); add_tensor = None
|
||||
fill_scalar = torch.ops.aten.fill_.Scalar(view_default, 1)
|
||||
view_default_1 = torch.ops.aten.view.default(view_default, [5, 5]); view_default = None
|
||||
add_tensor_1 = torch.ops.aten.add.Tensor(view_default_1, 1)
|
||||
return (view_default_1, add_tensor_1)
|
||||
add = torch.ops.aten.add.Tensor(a_1, 1); a_1 = None
|
||||
resize = torch.ops.aten.resize_.default(add, [5, 5])
|
||||
view = torch.ops.aten.view.default(add, [25]); add = None
|
||||
fill = torch.ops.aten.fill_.Scalar(view, 1)
|
||||
view_1 = torch.ops.aten.view.default(view, [5, 5]); view = None
|
||||
add_1 = torch.ops.aten.add.Tensor(view_1, 1)
|
||||
return (view_1, add_1)
|
||||
""")
|
||||
|
||||
def test_resize_larger_invalid(self):
|
||||
@ -1115,10 +1115,10 @@ $3 = torch._ops.aten.add.Tensor($2, 1)""")
|
||||
|
||||
def forward(self, a_1):
|
||||
zeros = torch.ops.aten.zeros.default([10], device = device(type='cpu'), pin_memory = False)
|
||||
select_copy_int = torch.ops.aten.select_copy.int(zeros, 0, 5)
|
||||
fill_scalar = torch.ops.aten.fill.Scalar(select_copy_int, 1); select_copy_int = None
|
||||
select_scatter_default = torch.ops.aten.select_scatter.default(zeros, fill_scalar, 0, 5); zeros = fill_scalar = None
|
||||
return select_scatter_default
|
||||
select_copy = torch.ops.aten.select_copy.int(zeros, 0, 5)
|
||||
fill = torch.ops.aten.fill.Scalar(select_copy, 1); select_copy = None
|
||||
select_scatter = torch.ops.aten.select_scatter.default(zeros, fill, 0, 5); zeros = fill = None
|
||||
return select_scatter
|
||||
""") # noqa: B950
|
||||
|
||||
reinplaced_logs = self.get_logs(f, torch.ones(2), reapply_views=True, run_reinplace=True)
|
||||
@ -1128,8 +1128,8 @@ def forward(self, a_1):
|
||||
|
||||
def forward(self, a_1):
|
||||
zeros = torch.ops.aten.zeros.default([10], device = device(type='cpu'), pin_memory = False)
|
||||
select_int = torch.ops.aten.select.int(zeros, 0, 5)
|
||||
fill_scalar = torch.ops.aten.fill_.Scalar(select_int, 1); select_int = None
|
||||
select = torch.ops.aten.select.int(zeros, 0, 5)
|
||||
fill = torch.ops.aten.fill_.Scalar(select, 1); select = None
|
||||
return zeros
|
||||
""")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user