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youkaichao merged 5 commits into from
Apr 20, 2025
Merged

update test files #83

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71e3654
update test files
youkaichao Apr 19, 2025
0dc5735
skip export in py 3.11
youkaichao Apr 20, 2025
1215f63
update tests
youkaichao Apr 20, 2025
8b2e427
skip cellvar in py 3.11
youkaichao Apr 20, 2025
149b09d
add tests
youkaichao Apr 20, 2025
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38 changes: 19 additions & 19 deletions ...debug_function_aot_eager_with_dynamic_shape_with_grad/__compiled_fn_1.Backward_graph.0.py
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Original file line number Diff line number Diff line change
@@ -1,20 +1,20 @@
from __future__ import annotations



def forward(self, primals_1: "f32[10]", tangents_1: "f32[10]"):
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
neg: "f32[10]" = torch.ops.aten.neg.default(tangents_1)
abs_1: "f32[10]" = torch.ops.aten.abs.default(primals_1)
add: "f32[10]" = torch.ops.aten.add.Tensor(abs_1, 1); abs_1 = None
div_1: "f32[10]" = torch.ops.aten.div.Tensor(primals_1, add)
div_2: "f32[10]" = torch.ops.aten.div.Tensor(div_1, add); div_1 = None
mul: "f32[10]" = torch.ops.aten.mul.Tensor(neg, div_2); neg = div_2 = None
div_3: "f32[10]" = torch.ops.aten.div.Tensor(tangents_1, add); tangents_1 = add = None
sgn: "f32[10]" = torch.ops.aten.sgn.default(primals_1); primals_1 = None
mul_1: "f32[10]" = torch.ops.aten.mul.Tensor(mul, sgn); mul = sgn = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
add_1: "f32[10]" = torch.ops.aten.add.Tensor(div_3, mul_1); div_3 = mul_1 = None
return (add_1, None)

import torch
from torch import device
class GraphModule(torch.nn.Module):
def forward(self, primals_1: "f32[10]", tangents_1: "f32[10]"):
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
neg: "f32[10]" = torch.ops.aten.neg.default(tangents_1)
abs_1: "f32[10]" = torch.ops.aten.abs.default(primals_1)
add: "f32[10]" = torch.ops.aten.add.Tensor(abs_1, 1); abs_1 = None
div_1: "f32[10]" = torch.ops.aten.div.Tensor(primals_1, add)
div_2: "f32[10]" = torch.ops.aten.div.Tensor(div_1, add); div_1 = None
mul: "f32[10]" = torch.ops.aten.mul.Tensor(neg, div_2); neg = div_2 = None
div_3: "f32[10]" = torch.ops.aten.div.Tensor(tangents_1, add); tangents_1 = add = None
sgn: "f32[10]" = torch.ops.aten.sgn.default(primals_1); primals_1 = None
mul_1: "f32[10]" = torch.ops.aten.mul.Tensor(mul, sgn); mul = sgn = None
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
add_1: "f32[10]" = torch.ops.aten.add.Tensor(div_3, mul_1); div_3 = mul_1 = None
return (add_1, None)
34 changes: 17 additions & 17 deletions ...debug_function_aot_eager_with_dynamic_shape_with_grad/__compiled_fn_1.Captured_Graph.0.py
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Original file line number Diff line number Diff line change
@@ -1,18 +1,18 @@
from __future__ import annotations



def forward(self, L_a_: "f32[10]", L_b_: "f32[10]"):
l_a_ = L_a_
l_b_ = L_b_

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
abs_1: "f32[10]" = torch.abs(l_a_)
add: "f32[10]" = abs_1 + 1; abs_1 = None
x: "f32[10]" = l_a_ / add; l_a_ = add = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:5 in toy_function, code: if b.sum() < 0:
sum_1: "f32[]" = l_b_.sum(); l_b_ = None
lt: "b8[]" = sum_1 < 0; sum_1 = None
return (x, lt)

import torch
from torch import device
class GraphModule(torch.nn.Module):
def forward(self, L_a_: "f32[10]", L_b_: "f32[10]"):
l_a_ = L_a_
l_b_ = L_b_
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
abs_1: "f32[10]" = torch.abs(l_a_)
add: "f32[10]" = abs_1 + 1; abs_1 = None
x: "f32[10]" = l_a_ / add; l_a_ = add = None
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:5 in toy_function, code: if b.sum() < 0:
sum_1: "f32[]" = l_b_.sum(); l_b_ = None
lt: "b8[]" = sum_1 < 0; sum_1 = None
return (lt, x)
28 changes: 14 additions & 14 deletions .../debug_function_aot_eager_with_dynamic_shape_with_grad/__compiled_fn_1.Forward_graph.0.py
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Original file line number Diff line number Diff line change
@@ -1,15 +1,15 @@
from __future__ import annotations



def forward(self, primals_1: "f32[10]", primals_2: "f32[10]"):
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
abs_1: "f32[10]" = torch.ops.aten.abs.default(primals_1)
add: "f32[10]" = torch.ops.aten.add.Tensor(abs_1, 1); abs_1 = None
div: "f32[10]" = torch.ops.aten.div.Tensor(primals_1, add); add = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:5 in toy_function, code: if b.sum() < 0:
sum_1: "f32[]" = torch.ops.aten.sum.default(primals_2); primals_2 = None
lt: "b8[]" = torch.ops.aten.lt.Scalar(sum_1, 0); sum_1 = None
return (div, lt, primals_1)

import torch
from torch import device
class GraphModule(torch.nn.Module):
def forward(self, primals_1: "f32[10]", primals_2: "f32[10]"):
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
abs_1: "f32[10]" = torch.ops.aten.abs.default(primals_1)
add: "f32[10]" = torch.ops.aten.add.Tensor(abs_1, 1); abs_1 = None
div: "f32[10]" = torch.ops.aten.div.Tensor(primals_1, add); add = None
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:5 in toy_function, code: if b.sum() < 0:
sum_1: "f32[]" = torch.ops.aten.sum.default(primals_2); primals_2 = None
lt: "b8[]" = torch.ops.aten.lt.Scalar(sum_1, 0); sum_1 = None
return (lt, div, primals_1)
56 changes: 28 additions & 28 deletions ...ut/debug_function_aot_eager_with_dynamic_shape_with_grad/__compiled_fn_1.Joint_graph.0.py
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@@ -1,30 +1,30 @@
from __future__ import annotations



def forward(self, primals, tangents):
primals_1: "f32[10]"; primals_2: "f32[10]"; tangents_1: "f32[10]";

primals_1, primals_2, tangents_1, = fx_pytree.tree_flatten_spec([primals, tangents], self._in_spec)
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
abs_1: "f32[10]" = torch.ops.aten.abs.default(primals_1)
add: "f32[10]" = torch.ops.aten.add.Tensor(abs_1, 1); abs_1 = None
div: "f32[10]" = torch.ops.aten.div.Tensor(primals_1, add)
import torch
from torch import device
class joint_helper(torch.nn.Module):
def forward(self, primals, tangents):
primals_1: "f32[10]"; primals_2: "f32[10]"; tangents_1: "f32[10]";

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:5 in toy_function, code: if b.sum() < 0:
sum_1: "f32[]" = torch.ops.aten.sum.default(primals_2); primals_2 = None
lt: "b8[]" = torch.ops.aten.lt.Scalar(sum_1, 0); sum_1 = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
neg: "f32[10]" = torch.ops.aten.neg.default(tangents_1)
div_1: "f32[10]" = torch.ops.aten.div.Tensor(primals_1, add)
div_2: "f32[10]" = torch.ops.aten.div.Tensor(div_1, add); div_1 = None
mul: "f32[10]" = torch.ops.aten.mul.Tensor(neg, div_2); neg = div_2 = None
div_3: "f32[10]" = torch.ops.aten.div.Tensor(tangents_1, add); tangents_1 = add = None
sgn: "f32[10]" = torch.ops.aten.sgn.default(primals_1); primals_1 = None
mul_1: "f32[10]" = torch.ops.aten.mul.Tensor(mul, sgn); mul = sgn = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
add_1: "f32[10]" = torch.ops.aten.add.Tensor(div_3, mul_1); div_3 = mul_1 = None
return pytree.tree_unflatten([div, lt, add_1, None], self._out_spec)

primals_1, primals_2, tangents_1, = fx_pytree.tree_flatten_spec([primals, tangents], self._in_spec)
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
abs_1: "f32[10]" = torch.ops.aten.abs.default(primals_1)
add: "f32[10]" = torch.ops.aten.add.Tensor(abs_1, 1); abs_1 = None
div: "f32[10]" = torch.ops.aten.div.Tensor(primals_1, add)

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:5 in toy_function, code: if b.sum() < 0:
sum_1: "f32[]" = torch.ops.aten.sum.default(primals_2); primals_2 = None
lt: "b8[]" = torch.ops.aten.lt.Scalar(sum_1, 0); sum_1 = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
neg: "f32[10]" = torch.ops.aten.neg.default(tangents_1)
div_1: "f32[10]" = torch.ops.aten.div.Tensor(primals_1, add)
div_2: "f32[10]" = torch.ops.aten.div.Tensor(div_1, add); div_1 = None
mul: "f32[10]" = torch.ops.aten.mul.Tensor(neg, div_2); neg = div_2 = None
div_3: "f32[10]" = torch.ops.aten.div.Tensor(tangents_1, add); tangents_1 = add = None
sgn: "f32[10]" = torch.ops.aten.sgn.default(primals_1); primals_1 = None
mul_1: "f32[10]" = torch.ops.aten.mul.Tensor(mul, sgn); mul = sgn = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
add_1: "f32[10]" = torch.ops.aten.add.Tensor(div_3, mul_1); div_3 = mul_1 = None
return pytree.tree_unflatten([lt, div, add_1, None], self._out_spec)

34 changes: 17 additions & 17 deletions ..._shape_with_grad/__compiled_fn_1.pre_insert_deferred_runtime_asserts___compiled_fn_1.0.py
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Original file line number Diff line number Diff line change
@@ -1,18 +1,18 @@
from __future__ import annotations



def forward(self, L_a_: "f32[10]", L_b_: "f32[10]"):
l_a_ = L_a_
l_b_ = L_b_

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
abs_1: "f32[10]" = torch.abs(l_a_)
add: "f32[10]" = abs_1 + 1; abs_1 = None
x: "f32[10]" = l_a_ / add; l_a_ = add = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:5 in toy_function, code: if b.sum() < 0:
sum_1: "f32[]" = l_b_.sum(); l_b_ = None
lt: "b8[]" = sum_1 < 0; sum_1 = None
return (x, lt)

import torch
from torch import device
class GraphModule(torch.nn.Module):
def forward(self, L_a_: "f32[10]", L_b_: "f32[10]"):
l_a_ = L_a_
l_b_ = L_b_
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
abs_1: "f32[10]" = torch.abs(l_a_)
add: "f32[10]" = abs_1 + 1; abs_1 = None
x: "f32[10]" = l_a_ / add; l_a_ = add = None
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:5 in toy_function, code: if b.sum() < 0:
sum_1: "f32[]" = l_b_.sum(); l_b_ = None
lt: "b8[]" = sum_1 < 0; sum_1 = None
return (lt, x)
56 changes: 28 additions & 28 deletions ...tion_aot_eager_with_dynamic_shape_with_grad/__compiled_fn_1.tensorify_python_scalars.0.py
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@@ -1,30 +1,30 @@
from __future__ import annotations



def forward(self, primals, tangents):
primals_1: "f32[10]"; primals_2: "f32[10]"; tangents_1: "f32[10]";

primals_1, primals_2, tangents_1, = fx_pytree.tree_flatten_spec([primals, tangents], self._in_spec)
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
abs_1: "f32[10]" = torch.ops.aten.abs.default(primals_1)
add: "f32[10]" = torch.ops.aten.add.Tensor(abs_1, 1); abs_1 = None
div: "f32[10]" = torch.ops.aten.div.Tensor(primals_1, add)
import torch
from torch import device
class joint_helper(torch.nn.Module):
def forward(self, primals, tangents):
primals_1: "f32[10]"; primals_2: "f32[10]"; tangents_1: "f32[10]";

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:5 in toy_function, code: if b.sum() < 0:
sum_1: "f32[]" = torch.ops.aten.sum.default(primals_2); primals_2 = None
lt: "b8[]" = torch.ops.aten.lt.Scalar(sum_1, 0); sum_1 = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
neg: "f32[10]" = torch.ops.aten.neg.default(tangents_1)
div_1: "f32[10]" = torch.ops.aten.div.Tensor(primals_1, add)
div_2: "f32[10]" = torch.ops.aten.div.Tensor(div_1, add); div_1 = None
mul: "f32[10]" = torch.ops.aten.mul.Tensor(neg, div_2); neg = div_2 = None
div_3: "f32[10]" = torch.ops.aten.div.Tensor(tangents_1, add); tangents_1 = add = None
sgn: "f32[10]" = torch.ops.aten.sgn.default(primals_1); primals_1 = None
mul_1: "f32[10]" = torch.ops.aten.mul.Tensor(mul, sgn); mul = sgn = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
add_1: "f32[10]" = torch.ops.aten.add.Tensor(div_3, mul_1); div_3 = mul_1 = None
return pytree.tree_unflatten([div, lt, add_1, None], self._out_spec)

primals_1, primals_2, tangents_1, = fx_pytree.tree_flatten_spec([primals, tangents], self._in_spec)
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
abs_1: "f32[10]" = torch.ops.aten.abs.default(primals_1)
add: "f32[10]" = torch.ops.aten.add.Tensor(abs_1, 1); abs_1 = None
div: "f32[10]" = torch.ops.aten.div.Tensor(primals_1, add)

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:5 in toy_function, code: if b.sum() < 0:
sum_1: "f32[]" = torch.ops.aten.sum.default(primals_2); primals_2 = None
lt: "b8[]" = torch.ops.aten.lt.Scalar(sum_1, 0); sum_1 = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
neg: "f32[10]" = torch.ops.aten.neg.default(tangents_1)
div_1: "f32[10]" = torch.ops.aten.div.Tensor(primals_1, add)
div_2: "f32[10]" = torch.ops.aten.div.Tensor(div_1, add); div_1 = None
mul: "f32[10]" = torch.ops.aten.mul.Tensor(neg, div_2); neg = div_2 = None
div_3: "f32[10]" = torch.ops.aten.div.Tensor(tangents_1, add); tangents_1 = add = None
sgn: "f32[10]" = torch.ops.aten.sgn.default(primals_1); primals_1 = None
mul_1: "f32[10]" = torch.ops.aten.mul.Tensor(mul, sgn); mul = sgn = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:4 in toy_function, code: x = a / (torch.abs(a) + 1)
add_1: "f32[10]" = torch.ops.aten.add.Tensor(div_3, mul_1); div_3 = mul_1 = None
return pytree.tree_unflatten([lt, div, add_1, None], self._out_spec)

34 changes: 17 additions & 17 deletions ...ebug_function_aot_eager_with_dynamic_shape_with_grad/__compiled_fn_11.Backward_graph.0.py
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@@ -1,18 +1,18 @@
from __future__ import annotations



def forward(self, primals_1: "Sym(s0)", primals_2: "f32[s0]", primals_3: "f32[s0]", tangents_1: "f32[s0]"):
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:7 in torch_dynamo_resume_in_toy_function_at_5, code: return x * b
mul_4: "f32[s0]" = torch.ops.aten.mul.Tensor(tangents_1, primals_3); primals_3 = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:6 in torch_dynamo_resume_in_toy_function_at_5, code: b = b * -1
mul: "f32[s0]" = torch.ops.aten.mul.Tensor(primals_2, -1); primals_2 = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:7 in torch_dynamo_resume_in_toy_function_at_5, code: return x * b
mul_5: "f32[s0]" = torch.ops.aten.mul.Tensor(tangents_1, mul); tangents_1 = mul = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:6 in torch_dynamo_resume_in_toy_function_at_5, code: b = b * -1
mul_6: "f32[s0]" = torch.ops.aten.mul.Tensor(mul_4, -1); mul_4 = None
return (None, mul_6, mul_5)

import torch
from torch import device
class GraphModule(torch.nn.Module):
def forward(self, primals_1: "Sym(s52)", primals_2: "f32[s52]", primals_3: "f32[s52]", tangents_1: "f32[s52]"):
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:7 in torch_dynamo_resume_in_toy_function_at_5, code: return x * b
mul_4: "f32[s52]" = torch.ops.aten.mul.Tensor(tangents_1, primals_3); primals_3 = None
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:6 in torch_dynamo_resume_in_toy_function_at_5, code: b = b * -1
mul: "f32[s52]" = torch.ops.aten.mul.Tensor(primals_2, -1); primals_2 = None
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:7 in torch_dynamo_resume_in_toy_function_at_5, code: return x * b
mul_5: "f32[s52]" = torch.ops.aten.mul.Tensor(tangents_1, mul); tangents_1 = mul = None
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:6 in torch_dynamo_resume_in_toy_function_at_5, code: b = b * -1
mul_6: "f32[s52]" = torch.ops.aten.mul.Tensor(mul_4, -1); mul_4 = None
return (None, mul_6, mul_5)
28 changes: 14 additions & 14 deletions ...ebug_function_aot_eager_with_dynamic_shape_with_grad/__compiled_fn_11.Captured_Graph.0.py
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@@ -1,15 +1,15 @@
from __future__ import annotations



def forward(self, s0: "Sym(s0)", L_b_: "f32[s0]", L_x_: "f32[s0]"):
l_b_ = L_b_
l_x_ = L_x_

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:6 in torch_dynamo_resume_in_toy_function_at_5, code: b = b * -1
b: "f32[s0]" = l_b_ * -1; l_b_ = None

# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:7 in torch_dynamo_resume_in_toy_function_at_5, code: return x * b
mul_1: "f32[s0]" = l_x_ * b; l_x_ = b = None
return (mul_1,)

import torch
from torch import device
class GraphModule(torch.nn.Module):
def forward(self, s52: "Sym(s52)", L_b_: "f32[s52]", L_x_: "f32[s52]"):
l_b_ = L_b_
l_x_ = L_x_
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:6 in torch_dynamo_resume_in_toy_function_at_5, code: b = b * -1
b: "f32[s52]" = l_b_ * -1; l_b_ = None
# File: /Users/youkaichao/data/DeepLearning/depyf/tests/test_pytorch/test_pytorch.py:7 in torch_dynamo_resume_in_toy_function_at_5, code: return x * b
mul_1: "f32[s52]" = l_x_ * b; l_x_ = b = None
return (mul_1,)
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