one hlo

gistfile1.txt

(base) root@t1v-n-0b41c02e-w-0:/# conda activate torch310
(torch310) root@t1v-n-0b41c02e-w-0:/# cd ~/pytorch/xla/
(torch310) root@t1v-n-0b41c02e-w-0:~/pytorch/xla# git branch
  fori_loop_simple_case_test
  fori_loop_simple_case_test_post_order
  fori_loop_simple_case_test_test
  fori_loop_simple_case_test_test_mnist_clean
* fori_loop_simple_case_testmaster
  master
  newapr23
  newtmpmay1
  newtmpmay7
  newtmpmay8
  openxlapinupdateapr25
  openxlapinupdateapr25again
  tmpmay6
(torch310) root@t1v-n-0b41c02e-w-0:~/pytorch/xla# rm -rf /tmp
(torch310) root@t1v-n-0b41c02e-w-0:~/pytorch/xla# XLA_IR_DEBUG=1 XLA_HLO_DEBUG=1 XLA_SAVE_TENSORS_FMT="hlo" XLA_SAVE_TENSORS_FILE="/tmp/save1.hlo" XLA_FLAGS=--xla_dump_to=/src/repo/hlogeneratedwhengenerateir PJRT_DEVICE=TPU python test/test_test_fori_loop.py
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------------simple linear one------------(original)
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torch_add_res__:  FunctionalTensor(lvl=0, value=\
tensor([53], device='xla:0', dtype=torch.int32))
res:  FunctionalTensor(lvl=0, value=\
tensor([  8.6785, -12.2220, -18.3325, -11.5665,   0.7858, -28.8699,  -6.9433,
        -20.0388,  16.4420, -12.3062,  14.6674,   5.0801, -21.1780, -34.9048,
         12.3117, -11.2889,   1.6557,  -2.8843,  -7.5593,  27.8263],
       device='xla:0'))
expected:  tensor([  8.6785, -12.2220, -18.3325, -11.5665,   0.7858, -28.8699,  -6.9433,
        -20.0388,  16.4420, -12.3062,  14.6674,   5.0801, -21.1780, -34.9048,
         12.3117, -11.2889,   1.6557,  -2.8843,  -7.5593,  27.8263],
       device='xla:0')
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(torch310) root@t1v-n-0b41c02e-w-0:~/pytorch/xla# ls /tmp
ls: cannot access '/tmp': No such file or directory
(torch310) root@t1v-n-0b41c02e-w-0:~/pytorch/xla# cd /tmp
bash: cd: /tmp: No such file or directory
(torch310) root@t1v-n-0b41c02e-w-0:~/pytorch/xla# mkdir /tmp
(torch310) root@t1v-n-0b41c02e-w-0:~/pytorch/xla# XLA_IR_DEBUG=1 XLA_HLO_DEBUG=1 XLA_SAVE_TENSORS_FMT="hlo" XLA_SAVE_TENSORS_FILE="/tmp/save1.hlo" XLA_FLAGS=--xla_dump_to=/src/repo/hlogeneratedwhengenerateir PJRT_DEVICE=TPU python test/test_test_fori_loop.py
------------simple linear one------------(original)
torch_add_res__:  FunctionalTensor(lvl=0, value=\
tensor([53], device='xla:0', dtype=torch.int32))
res:  FunctionalTensor(lvl=0, value=\
tensor([-13.2017,   5.3126,  -2.0887,  42.4963,  -2.8923,  -5.4809,   4.4193,
         27.6952,   4.9128, -11.5728,  12.1975,  26.9884,  -7.1339,  14.2867,
        -15.0189,   7.7009,  -1.0184,   7.9056,  11.0350,  20.2705],
       device='xla:0'))
expected:  tensor([-13.2017,   5.3126,  -2.0887,  42.4963,  -2.8923,  -5.4809,   4.4193,
         27.6952,   4.9128, -11.5728,  12.1975,  26.9884,  -7.1339,  14.2867,
        -15.0189,   7.7009,  -1.0184,   7.9056,  11.0350,  20.2705],
       device='xla:0')
(torch310) root@t1v-n-0b41c02e-w-0:~/pytorch/xla# cat /tmp/save1.hlo.0
[ScheduleSyncTensorsGraph]
TensorsGraphInfo:
  _fake_fori_loop (/root/pytorch/xla/test/test_test_fori_loop.py:12)
  <module> (/root/pytorch/xla/test/test_test_fori_loop.py:59)

Root Hashes: (cdf31edf0e61d230194e802e9ec7a48d)

## BEGIN_GRAPH
HloModule IrToHlo.10, entry_computation_layout={(s32[1]{0}, s32[1]{0})->(s32[])}

ENTRY %IrToHlo.10 (p0.2: s32[1], p1.3: s32[1]) -> (s32[]) {
  %p1.3 = s32[1]{0} parameter(1), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=43}
  %p0.2 = s32[1]{0} parameter(0), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=44}
  %constant.1 = s32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=12}
  %broadcast.4 = s32[1]{0} broadcast(s32[] %constant.1), dimensions={}, metadata={op_type="aten__sub" op_name="aten__sub.10/aten__sub" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=12}
  %multiply.5 = s32[1]{0} multiply(s32[1]{0} %p0.2, s32[1]{0} %broadcast.4), metadata={op_type="aten__sub" op_name="aten__sub.10/aten__sub" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=12}
  %subtract.6 = s32[1]{0} subtract(s32[1]{0} %p1.3, s32[1]{0} %multiply.5), metadata={op_type="aten__sub" op_name="aten__sub.10/aten__sub" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=12}
  %slice.7 = s32[1]{0} slice(s32[1]{0} %subtract.6), slice={[0:1]}, metadata={op_type="xla__generic_slice" op_name="xla__generic_slice" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=12}
  %reshape.8 = s32[] reshape(s32[1]{0} %slice.7), metadata={op_type="aten__view" op_name="aten__view" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=12}
  ROOT %tuple.9 = (s32[]) tuple(s32[] %reshape.8)
}


Graph Hash: 2d459e2f8cb1b1e9fdc7cf2bbd9c7efb

## END_GRAPH

[ScheduleSyncTensorsGraph]
TensorsGraphInfo:
  _str_intern (/root/pytorch/torch/_tensor_str.py:432)
  _str (/root/pytorch/torch/_tensor_str.py:697)
  __repr__ (/root/pytorch/torch/_tensor.py:462)
  _functorch_wrapper_str_intern (/root/pytorch/torch/_tensor_str.py:673)
  _str_intern (/root/pytorch/torch/_tensor_str.py:392)
  _str (/root/pytorch/torch/_tensor_str.py:697)
  __repr__ (/root/pytorch/torch/_tensor.py:462)
  <module> (/root/pytorch/xla/test/test_test_fori_loop.py:61)

Root Hashes: (f282fdfbc5c7637fc62cacde9bade651)

## BEGIN_GRAPH
HloModule IrToHlo.122, entry_computation_layout={(f32[20,10]{0,1}, f32[20]{0}, s64[], s32[1]{0}, s32[1]{0}, /*index=5*/s32[1]{0}, s32[1]{0})->(s32[1]{0})}

%PyLoweringContext.39 (p0.41: s32[1], p1.43: s32[1], p2.44: s32[1], p3.49: s32[1], p4.53: f32[10], p5.55: f32[20], p6.56: f32[20,10], UnusedArgumentsPlaceholder.40: f32[20]) -> (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20]) {
  %UnusedArgumentsPlaceholder.40 = f32[20]{0} parameter(7)
  %p0.41 = s32[1]{0} parameter(0), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %p2.44 = s32[1]{0} parameter(2), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %p1.43 = s32[1]{0} parameter(1), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %constant.42 = s32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="<eval_with_key>.1" source_line=5}
  %broadcast.45 = s32[1]{0} broadcast(s32[] %constant.42), dimensions={}, metadata={op_type="aten__add" op_name="aten__add.7/aten__add" source_file="<eval_with_key>.1" source_line=5}
  %multiply.46 = s32[1]{0} multiply(s32[1]{0} %p1.43, s32[1]{0} %broadcast.45), metadata={op_type="aten__add" op_name="aten__add.7/aten__add" source_file="<eval_with_key>.1" source_line=5}
  %add.47 = s32[1]{0} add(s32[1]{0} %p2.44, s32[1]{0} %multiply.46), metadata={op_type="aten__add" op_name="aten__add.7/aten__add" source_file="<eval_with_key>.1" source_line=5}
  %p3.49 = s32[1]{0} parameter(3), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %constant.48 = s32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="<eval_with_key>.1" source_line=10}
  %broadcast.50 = s32[1]{0} broadcast(s32[] %constant.48), dimensions={}, metadata={op_type="aten__add" op_name="aten__add.9/aten__add" source_file="<eval_with_key>.1" source_line=10}
  %multiply.51 = s32[1]{0} multiply(s32[1]{0} %p3.49, s32[1]{0} %broadcast.50), metadata={op_type="aten__add" op_name="aten__add.9/aten__add" source_file="<eval_with_key>.1" source_line=10}
  %add.52 = s32[1]{0} add(s32[1]{0} %p2.44, s32[1]{0} %multiply.51), metadata={op_type="aten__add" op_name="aten__add.9/aten__add" source_file="<eval_with_key>.1" source_line=10}
  %p4.53 = f32[10]{0} parameter(4), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %reshape.58 = f32[1,10]{1,0} reshape(f32[10]{0} %p4.53), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  %p6.56 = f32[20,10]{0,1} parameter(6), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=82}
  %transpose.57 = f32[10,20]{1,0} transpose(f32[20,10]{0,1} %p6.56), dimensions={1,0}, metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  %dot.59 = f32[1,20]{1,0} dot(f32[1,10]{1,0} %reshape.58, f32[10,20]{1,0} %transpose.57), lhs_contracting_dims={1}, rhs_contracting_dims={0}, metadata={op_type="aten__mm" op_name="aten__mm" source_file="<eval_with_key>.1" source_line=6}
  %reshape.60 = f32[20]{0} reshape(f32[1,20]{1,0} %dot.59), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  %p5.55 = f32[20]{0} parameter(5), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=82}
  %constant.54 = f32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="<eval_with_key>.1" source_line=6}
  %broadcast.61 = f32[20]{0} broadcast(f32[] %constant.54), dimensions={}, metadata={op_type="aten__add" op_name="aten__add.8/aten__add" source_file="<eval_with_key>.1" source_line=6}
  %multiply.62 = f32[20]{0} multiply(f32[20]{0} %p5.55, f32[20]{0} %broadcast.61), metadata={op_type="aten__add" op_name="aten__add.8/aten__add" source_file="<eval_with_key>.1" source_line=6}
  %add.63 = f32[20]{0} add(f32[20]{0} %reshape.60, f32[20]{0} %multiply.62), metadata={op_type="aten__add" op_name="aten__add.8/aten__add" source_file="<eval_with_key>.1" source_line=6}
  %reshape.64 = f32[1,20]{1,0} reshape(f32[20]{0} %add.63), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  %reshape.65 = f32[20]{0} reshape(f32[1,20]{1,0} %reshape.64), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  ROOT %tuple.66 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) tuple(s32[1]{0} %p0.41, s32[1]{0} %add.47, s32[1]{0} %p2.44, s32[1]{0} %add.52, f32[10]{0} %p4.53, /*index=5*/f32[20]{0} %reshape.65, f32[20,10]{0,1} %p6.56, f32[20]{0} %p5.55)
}

%PyLoweringContext.28.67 (in.68: (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20])) -> (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20]) {
  %in.68 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) parameter(0)
  %get-tuple-element.69 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=0
  %get-tuple-element.70 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=1
  %get-tuple-element.71 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=2
  %get-tuple-element.72 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=3
  %get-tuple-element.73 = f32[10]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=4
  %get-tuple-element.74 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=5
  %get-tuple-element.75 = f32[20,10]{0,1} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=6
  %get-tuple-element.76 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=7
  ROOT %call.77 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) call(s32[1]{0} %get-tuple-element.69, s32[1]{0} %get-tuple-element.70, s32[1]{0} %get-tuple-element.71, s32[1]{0} %get-tuple-element.72, f32[10]{0} %get-tuple-element.73, /*index=5*/f32[20]{0} %get-tuple-element.74, f32[20,10]{0,1} %get-tuple-element.75, f32[20]{0} %get-tuple-element.76), to_apply=%PyLoweringContext.39
}

%PyLoweringContext.78 (p0.85: s32[1], p1.87: s32[1], UnusedArgumentsPlaceholder.79: s32[1], UnusedArgumentsPlaceholder.80: s32[1], UnusedArgumentsPlaceholder.81: f32[10], UnusedArgumentsPlaceholder.82: f32[20], UnusedArgumentsPlaceholder.83: f32[20,10], UnusedArgumentsPlaceholder.84: f32[20]) -> pred[] {
  %UnusedArgumentsPlaceholder.79 = s32[1]{0} parameter(2)
  %UnusedArgumentsPlaceholder.80 = s32[1]{0} parameter(3)
  %UnusedArgumentsPlaceholder.81 = f32[10]{0} parameter(4)
  %UnusedArgumentsPlaceholder.82 = f32[20]{0} parameter(5)
  %UnusedArgumentsPlaceholder.83 = f32[20,10]{0,1} parameter(6)
  %UnusedArgumentsPlaceholder.84 = f32[20]{0} parameter(7)
  %p1.87 = s32[1]{0} parameter(1), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %reshape.88 = s32[] reshape(s32[1]{0} %p1.87), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.0" source_line=5}
  %p0.85 = s32[1]{0} parameter(0), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %reshape.86 = s32[] reshape(s32[1]{0} %p0.85), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.0" source_line=6}
  ROOT %compare.89 = pred[] compare(s32[] %reshape.88, s32[] %reshape.86), direction=LT, metadata={op_type="aten__lt" op_name="aten__lt" source_file="<eval_with_key>.0" source_line=7}
}

%PyLoweringContext.12.90 (in.91: (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20])) -> pred[] {
  %in.91 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) parameter(0)
  %get-tuple-element.92 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=0
  %get-tuple-element.93 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=1
  %get-tuple-element.94 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=2
  %get-tuple-element.95 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=3
  %get-tuple-element.96 = f32[10]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=4
  %get-tuple-element.97 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=5
  %get-tuple-element.98 = f32[20,10]{0,1} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=6
  %get-tuple-element.99 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=7
  ROOT %call.100 = pred[] call(s32[1]{0} %get-tuple-element.92, s32[1]{0} %get-tuple-element.93, s32[1]{0} %get-tuple-element.94, s32[1]{0} %get-tuple-element.95, f32[10]{0} %get-tuple-element.96, /*index=5*/f32[20]{0} %get-tuple-element.97, f32[20,10]{0,1} %get-tuple-element.98, f32[20]{0} %get-tuple-element.99), to_apply=%PyLoweringContext.78
}

%test_while.101 (p0.102: s32[1], p1.103: s32[1], p2.104: s32[1], p3.105: s32[1], p4.106: f32[10], p5.107: f32[20], p6.108: f32[20], p7.109: f32[20,10]) -> (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20]) {
  %p0.102 = s32[1]{0} parameter(0)
  %p1.103 = s32[1]{0} parameter(1)
  %p2.104 = s32[1]{0} parameter(2)
  %p3.105 = s32[1]{0} parameter(3)
  %p4.106 = f32[10]{0} parameter(4)
  %p6.108 = f32[20]{0} parameter(6)
  %p7.109 = f32[20,10]{1,0} parameter(7)
  %p5.107 = f32[20]{0} parameter(5)
  %tuple.110 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) tuple(s32[1]{0} %p0.102, s32[1]{0} %p1.103, s32[1]{0} %p2.104, s32[1]{0} %p3.105, f32[10]{0} %p4.106, /*index=5*/f32[20]{0} %p6.108, f32[20,10]{1,0} %p7.109, f32[20]{0} %p5.107)
  ROOT %while.111 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) while((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %tuple.110), condition=%PyLoweringContext.12.90, body=%PyLoweringContext.28.67
}

ENTRY %IrToHlo.122 (p0.1: f32[20,10], p1.2: f32[20], p2.8: s64[], p3.35: s32[1], p4.36: s32[1], p5.37: s32[1], p6.38: s32[1]) -> (s32[1]) {
  %constant.11 = s64[] constant(2531011), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.9 = s64[] constant(214013), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %p2.8 = s64[] parameter(2), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %multiply.10 = s64[] multiply(s64[] %constant.9, s64[] %p2.8), metadata={op_type="aten__mul" op_name="aten__mul" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %add.12 = s64[] add(s64[] %constant.11, s64[] %multiply.10), metadata={op_type="aten__add" op_name="aten__add" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %convert.15 = u64[] convert(s64[] %add.12), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %reshape.17 = u64[1]{0} reshape(u64[] %convert.15), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.16 = u64[] constant(0), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %reshape.18 = u64[1]{0} reshape(u64[] %constant.16), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %concatenate.19 = u64[2]{0} concatenate(u64[1]{0} %reshape.17, u64[1]{0} %reshape.18), dimensions={0}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %rng-bit-generator.20 = (u64[2]{0}, u32[10]{0}) rng-bit-generator(u64[2]{0} %concatenate.19), algorithm=rng_default, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %get-tuple-element.22 = u64[2]{0} get-tuple-element((u64[2]{0}, u32[10]{0}) %rng-bit-generator.20), index=0, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %p6.38 = s32[1]{0} parameter(6), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=43}
  %p5.37 = s32[1]{0} parameter(5), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=44}
  %p4.36 = s32[1]{0} parameter(4), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=45}
  %p3.35 = s32[1]{0} parameter(3), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=46}
  %get-tuple-element.21 = u32[10]{0} get-tuple-element((u64[2]{0}, u32[10]{0}) %rng-bit-generator.20), index=1, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.23 = u32[] constant(9), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %broadcast.24 = u32[10]{0} broadcast(u32[] %constant.23), dimensions={}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %shift-right-logical.25 = u32[10]{0} shift-right-logical(u32[10]{0} %get-tuple-element.21, u32[10]{0} %broadcast.24), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %convert.26 = f32[10]{0} convert(u32[10]{0} %shift-right-logical.25), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.27 = f32[] constant(1.1920929e-07), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %broadcast.28 = f32[10]{0} broadcast(f32[] %constant.27), dimensions={}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %multiply.29 = f32[10]{0} multiply(f32[10]{0} %convert.26, f32[10]{0} %broadcast.28), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.13 = f32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.14 = f32[] constant(0), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %subtract.30 = f32[] subtract(f32[] %constant.13, f32[] %constant.14), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %broadcast.31 = f32[10]{0} broadcast(f32[] %subtract.30), dimensions={}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %multiply.32 = f32[10]{0} multiply(f32[10]{0} %multiply.29, f32[10]{0} %broadcast.31), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %broadcast.33 = f32[10]{0} broadcast(f32[] %constant.14), dimensions={}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %add.34 = f32[10]{0} add(f32[10]{0} %multiply.32, f32[10]{0} %broadcast.33), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.3 = f32[] constant(0), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %reshape.4 = f32[1]{0} reshape(f32[] %constant.3), metadata={op_type="aten__expand" op_name="aten__expand" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %broadcast.5 = f32[1]{0} broadcast(f32[1]{0} %reshape.4), dimensions={0}, metadata={op_type="aten__expand" op_name="aten__expand" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %reshape.6 = f32[] reshape(f32[1]{0} %broadcast.5), metadata={op_type="aten__expand" op_name="aten__expand" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %broadcast.7 = f32[20]{0} broadcast(f32[] %reshape.6), dimensions={}, metadata={op_type="aten__expand" op_name="aten__expand" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %p1.2 = f32[20]{0} parameter(1), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/torch/nn/modules/module.py" source_line=1159}
  %p0.1 = f32[20,10]{0,1} parameter(0), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/torch/nn/modules/module.py" source_line=1159}
  %call.112 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) call(s32[1]{0} %p6.38, s32[1]{0} %p5.37, s32[1]{0} %p4.36, s32[1]{0} %p3.35, f32[10]{0} %add.34, /*index=5*/f32[20]{0} %broadcast.7, f32[20]{0} %p1.2, f32[20,10]{0,1} %p0.1), to_apply=%test_while.101, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.113 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=0, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.114 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=1, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.115 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=2, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.117 = f32[10]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=4, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.118 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=5, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.119 = f32[20,10]{1,0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=6, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.120 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=7, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.116 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=3, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  ROOT %tuple.121 = (s32[1]{0}) tuple(s32[1]{0} %get-tuple-element.116)
}


Graph Hash: a9f5bc548849e4e529f3b0ec296d61bc

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  _str_intern (/root/pytorch/torch/_tensor_str.py:392)
  _str (/root/pytorch/torch/_tensor_str.py:697)
  __repr__ (/root/pytorch/torch/_tensor.py:462)
  <module> (/root/pytorch/xla/test/test_test_fori_loop.py:62)

Root Hashes: (248a503d47853a256a1aa783ac13988d)

## BEGIN_GRAPH
HloModule IrToHlo.122, entry_computation_layout={(f32[20,10]{0,1}, f32[20]{0}, s64[], s32[1]{0}, s32[1]{0}, /*index=5*/s32[1]{0}, s32[1]{0})->(f32[20]{0})}

%PyLoweringContext.39 (p0.41: s32[1], p1.43: s32[1], p2.44: s32[1], p3.49: s32[1], p4.53: f32[10], p5.55: f32[20], p6.56: f32[20,10], UnusedArgumentsPlaceholder.40: f32[20]) -> (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20]) {
  %UnusedArgumentsPlaceholder.40 = f32[20]{0} parameter(7)
  %p0.41 = s32[1]{0} parameter(0), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %p2.44 = s32[1]{0} parameter(2), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %p1.43 = s32[1]{0} parameter(1), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %constant.42 = s32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="<eval_with_key>.1" source_line=5}
  %broadcast.45 = s32[1]{0} broadcast(s32[] %constant.42), dimensions={}, metadata={op_type="aten__add" op_name="aten__add.7/aten__add" source_file="<eval_with_key>.1" source_line=5}
  %multiply.46 = s32[1]{0} multiply(s32[1]{0} %p1.43, s32[1]{0} %broadcast.45), metadata={op_type="aten__add" op_name="aten__add.7/aten__add" source_file="<eval_with_key>.1" source_line=5}
  %add.47 = s32[1]{0} add(s32[1]{0} %p2.44, s32[1]{0} %multiply.46), metadata={op_type="aten__add" op_name="aten__add.7/aten__add" source_file="<eval_with_key>.1" source_line=5}
  %p3.49 = s32[1]{0} parameter(3), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %constant.48 = s32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="<eval_with_key>.1" source_line=10}
  %broadcast.50 = s32[1]{0} broadcast(s32[] %constant.48), dimensions={}, metadata={op_type="aten__add" op_name="aten__add.9/aten__add" source_file="<eval_with_key>.1" source_line=10}
  %multiply.51 = s32[1]{0} multiply(s32[1]{0} %p3.49, s32[1]{0} %broadcast.50), metadata={op_type="aten__add" op_name="aten__add.9/aten__add" source_file="<eval_with_key>.1" source_line=10}
  %add.52 = s32[1]{0} add(s32[1]{0} %p2.44, s32[1]{0} %multiply.51), metadata={op_type="aten__add" op_name="aten__add.9/aten__add" source_file="<eval_with_key>.1" source_line=10}
  %p4.53 = f32[10]{0} parameter(4), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %reshape.58 = f32[1,10]{1,0} reshape(f32[10]{0} %p4.53), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  %p6.56 = f32[20,10]{0,1} parameter(6), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=82}
  %transpose.57 = f32[10,20]{1,0} transpose(f32[20,10]{0,1} %p6.56), dimensions={1,0}, metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  %dot.59 = f32[1,20]{1,0} dot(f32[1,10]{1,0} %reshape.58, f32[10,20]{1,0} %transpose.57), lhs_contracting_dims={1}, rhs_contracting_dims={0}, metadata={op_type="aten__mm" op_name="aten__mm" source_file="<eval_with_key>.1" source_line=6}
  %reshape.60 = f32[20]{0} reshape(f32[1,20]{1,0} %dot.59), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  %p5.55 = f32[20]{0} parameter(5), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=82}
  %constant.54 = f32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="<eval_with_key>.1" source_line=6}
  %broadcast.61 = f32[20]{0} broadcast(f32[] %constant.54), dimensions={}, metadata={op_type="aten__add" op_name="aten__add.8/aten__add" source_file="<eval_with_key>.1" source_line=6}
  %multiply.62 = f32[20]{0} multiply(f32[20]{0} %p5.55, f32[20]{0} %broadcast.61), metadata={op_type="aten__add" op_name="aten__add.8/aten__add" source_file="<eval_with_key>.1" source_line=6}
  %add.63 = f32[20]{0} add(f32[20]{0} %reshape.60, f32[20]{0} %multiply.62), metadata={op_type="aten__add" op_name="aten__add.8/aten__add" source_file="<eval_with_key>.1" source_line=6}
  %reshape.64 = f32[1,20]{1,0} reshape(f32[20]{0} %add.63), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  %reshape.65 = f32[20]{0} reshape(f32[1,20]{1,0} %reshape.64), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  ROOT %tuple.66 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) tuple(s32[1]{0} %p0.41, s32[1]{0} %add.47, s32[1]{0} %p2.44, s32[1]{0} %add.52, f32[10]{0} %p4.53, /*index=5*/f32[20]{0} %reshape.65, f32[20,10]{0,1} %p6.56, f32[20]{0} %p5.55)
}

%PyLoweringContext.28.67 (in.68: (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20])) -> (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20]) {
  %in.68 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) parameter(0)
  %get-tuple-element.69 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=0
  %get-tuple-element.70 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=1
  %get-tuple-element.71 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=2
  %get-tuple-element.72 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=3
  %get-tuple-element.73 = f32[10]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=4
  %get-tuple-element.74 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=5
  %get-tuple-element.75 = f32[20,10]{0,1} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=6
  %get-tuple-element.76 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.68), index=7
  ROOT %call.77 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) call(s32[1]{0} %get-tuple-element.69, s32[1]{0} %get-tuple-element.70, s32[1]{0} %get-tuple-element.71, s32[1]{0} %get-tuple-element.72, f32[10]{0} %get-tuple-element.73, /*index=5*/f32[20]{0} %get-tuple-element.74, f32[20,10]{0,1} %get-tuple-element.75, f32[20]{0} %get-tuple-element.76), to_apply=%PyLoweringContext.39
}

%PyLoweringContext.78 (p0.85: s32[1], p1.87: s32[1], UnusedArgumentsPlaceholder.79: s32[1], UnusedArgumentsPlaceholder.80: s32[1], UnusedArgumentsPlaceholder.81: f32[10], UnusedArgumentsPlaceholder.82: f32[20], UnusedArgumentsPlaceholder.83: f32[20,10], UnusedArgumentsPlaceholder.84: f32[20]) -> pred[] {
  %UnusedArgumentsPlaceholder.79 = s32[1]{0} parameter(2)
  %UnusedArgumentsPlaceholder.80 = s32[1]{0} parameter(3)
  %UnusedArgumentsPlaceholder.81 = f32[10]{0} parameter(4)
  %UnusedArgumentsPlaceholder.82 = f32[20]{0} parameter(5)
  %UnusedArgumentsPlaceholder.83 = f32[20,10]{0,1} parameter(6)
  %UnusedArgumentsPlaceholder.84 = f32[20]{0} parameter(7)
  %p1.87 = s32[1]{0} parameter(1), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %reshape.88 = s32[] reshape(s32[1]{0} %p1.87), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.0" source_line=5}
  %p0.85 = s32[1]{0} parameter(0), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %reshape.86 = s32[] reshape(s32[1]{0} %p0.85), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.0" source_line=6}
  ROOT %compare.89 = pred[] compare(s32[] %reshape.88, s32[] %reshape.86), direction=LT, metadata={op_type="aten__lt" op_name="aten__lt" source_file="<eval_with_key>.0" source_line=7}
}

%PyLoweringContext.12.90 (in.91: (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20])) -> pred[] {
  %in.91 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) parameter(0)
  %get-tuple-element.92 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=0
  %get-tuple-element.93 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=1
  %get-tuple-element.94 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=2
  %get-tuple-element.95 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=3
  %get-tuple-element.96 = f32[10]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=4
  %get-tuple-element.97 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=5
  %get-tuple-element.98 = f32[20,10]{0,1} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=6
  %get-tuple-element.99 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.91), index=7
  ROOT %call.100 = pred[] call(s32[1]{0} %get-tuple-element.92, s32[1]{0} %get-tuple-element.93, s32[1]{0} %get-tuple-element.94, s32[1]{0} %get-tuple-element.95, f32[10]{0} %get-tuple-element.96, /*index=5*/f32[20]{0} %get-tuple-element.97, f32[20,10]{0,1} %get-tuple-element.98, f32[20]{0} %get-tuple-element.99), to_apply=%PyLoweringContext.78
}

%test_while.101 (p0.102: s32[1], p1.103: s32[1], p2.104: s32[1], p3.105: s32[1], p4.106: f32[10], p5.107: f32[20], p6.108: f32[20], p7.109: f32[20,10]) -> (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20]) {
  %p0.102 = s32[1]{0} parameter(0)
  %p1.103 = s32[1]{0} parameter(1)
  %p2.104 = s32[1]{0} parameter(2)
  %p3.105 = s32[1]{0} parameter(3)
  %p4.106 = f32[10]{0} parameter(4)
  %p6.108 = f32[20]{0} parameter(6)
  %p7.109 = f32[20,10]{1,0} parameter(7)
  %p5.107 = f32[20]{0} parameter(5)
  %tuple.110 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) tuple(s32[1]{0} %p0.102, s32[1]{0} %p1.103, s32[1]{0} %p2.104, s32[1]{0} %p3.105, f32[10]{0} %p4.106, /*index=5*/f32[20]{0} %p6.108, f32[20,10]{1,0} %p7.109, f32[20]{0} %p5.107)
  ROOT %while.111 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) while((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %tuple.110), condition=%PyLoweringContext.12.90, body=%PyLoweringContext.28.67
}

ENTRY %IrToHlo.122 (p0.1: f32[20,10], p1.2: f32[20], p2.8: s64[], p3.35: s32[1], p4.36: s32[1], p5.37: s32[1], p6.38: s32[1]) -> (f32[20]) {
  %constant.11 = s64[] constant(2531011), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.9 = s64[] constant(214013), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %p2.8 = s64[] parameter(2), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %multiply.10 = s64[] multiply(s64[] %constant.9, s64[] %p2.8), metadata={op_type="aten__mul" op_name="aten__mul" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %add.12 = s64[] add(s64[] %constant.11, s64[] %multiply.10), metadata={op_type="aten__add" op_name="aten__add" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %convert.15 = u64[] convert(s64[] %add.12), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %reshape.17 = u64[1]{0} reshape(u64[] %convert.15), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.16 = u64[] constant(0), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %reshape.18 = u64[1]{0} reshape(u64[] %constant.16), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %concatenate.19 = u64[2]{0} concatenate(u64[1]{0} %reshape.17, u64[1]{0} %reshape.18), dimensions={0}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %rng-bit-generator.20 = (u64[2]{0}, u32[10]{0}) rng-bit-generator(u64[2]{0} %concatenate.19), algorithm=rng_default, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %get-tuple-element.22 = u64[2]{0} get-tuple-element((u64[2]{0}, u32[10]{0}) %rng-bit-generator.20), index=0, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %p6.38 = s32[1]{0} parameter(6), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=43}
  %p5.37 = s32[1]{0} parameter(5), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=44}
  %p4.36 = s32[1]{0} parameter(4), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=45}
  %p3.35 = s32[1]{0} parameter(3), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=46}
  %get-tuple-element.21 = u32[10]{0} get-tuple-element((u64[2]{0}, u32[10]{0}) %rng-bit-generator.20), index=1, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.23 = u32[] constant(9), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %broadcast.24 = u32[10]{0} broadcast(u32[] %constant.23), dimensions={}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %shift-right-logical.25 = u32[10]{0} shift-right-logical(u32[10]{0} %get-tuple-element.21, u32[10]{0} %broadcast.24), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %convert.26 = f32[10]{0} convert(u32[10]{0} %shift-right-logical.25), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.27 = f32[] constant(1.1920929e-07), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %broadcast.28 = f32[10]{0} broadcast(f32[] %constant.27), dimensions={}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %multiply.29 = f32[10]{0} multiply(f32[10]{0} %convert.26, f32[10]{0} %broadcast.28), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.13 = f32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.14 = f32[] constant(0), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %subtract.30 = f32[] subtract(f32[] %constant.13, f32[] %constant.14), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %broadcast.31 = f32[10]{0} broadcast(f32[] %subtract.30), dimensions={}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %multiply.32 = f32[10]{0} multiply(f32[10]{0} %multiply.29, f32[10]{0} %broadcast.31), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %broadcast.33 = f32[10]{0} broadcast(f32[] %constant.14), dimensions={}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %add.34 = f32[10]{0} add(f32[10]{0} %multiply.32, f32[10]{0} %broadcast.33), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.3 = f32[] constant(0), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %reshape.4 = f32[1]{0} reshape(f32[] %constant.3), metadata={op_type="aten__expand" op_name="aten__expand" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %broadcast.5 = f32[1]{0} broadcast(f32[1]{0} %reshape.4), dimensions={0}, metadata={op_type="aten__expand" op_name="aten__expand" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %reshape.6 = f32[] reshape(f32[1]{0} %broadcast.5), metadata={op_type="aten__expand" op_name="aten__expand" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %broadcast.7 = f32[20]{0} broadcast(f32[] %reshape.6), dimensions={}, metadata={op_type="aten__expand" op_name="aten__expand" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %p1.2 = f32[20]{0} parameter(1), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/torch/nn/modules/module.py" source_line=1159}
  %p0.1 = f32[20,10]{0,1} parameter(0), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/torch/nn/modules/module.py" source_line=1159}
  %call.112 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) call(s32[1]{0} %p6.38, s32[1]{0} %p5.37, s32[1]{0} %p4.36, s32[1]{0} %p3.35, f32[10]{0} %add.34, /*index=5*/f32[20]{0} %broadcast.7, f32[20]{0} %p1.2, f32[20,10]{0,1} %p0.1), to_apply=%test_while.101, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.113 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=0, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.114 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=1, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.115 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=2, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.116 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=3, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.117 = f32[10]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=4, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.119 = f32[20,10]{1,0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=6, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.120 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=7, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.118 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.112), index=5, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  ROOT %tuple.121 = (f32[20]{0}) tuple(f32[20]{0} %get-tuple-element.118)
}


Graph Hash: f4f17d4ce372d561656d4159514906bd

## END_GRAPH

[ScheduleSyncTensorsGraph]
TensorsGraphInfo:
  _str_intern (/root/pytorch/torch/_tensor_str.py:432)
  _str (/root/pytorch/torch/_tensor_str.py:697)
  __repr__ (/root/pytorch/torch/_tensor.py:462)
  <module> (/root/pytorch/xla/test/test_test_fori_loop.py:63)

Root Hashes: (7477ce2004e71cd58d97df1103837cd)

## BEGIN_GRAPH
HloModule IrToHlo.130, entry_computation_layout={(f32[20,10]{0,1}, f32[20]{0}, s64[], s32[1]{0}, s32[1]{0}, /*index=5*/s32[1]{0}, s32[1]{0})->(f32[20]{0})}

%PyLoweringContext.40 (p0.42: s32[1], p1.44: s32[1], p2.45: s32[1], p3.50: s32[1], p4.54: f32[10], p5.56: f32[20], p6.57: f32[20,10], UnusedArgumentsPlaceholder.41: f32[20]) -> (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20]) {
  %UnusedArgumentsPlaceholder.41 = f32[20]{0} parameter(7)
  %p0.42 = s32[1]{0} parameter(0), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %p2.45 = s32[1]{0} parameter(2), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %p1.44 = s32[1]{0} parameter(1), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %constant.43 = s32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="<eval_with_key>.1" source_line=5}
  %broadcast.46 = s32[1]{0} broadcast(s32[] %constant.43), dimensions={}, metadata={op_type="aten__add" op_name="aten__add.7/aten__add" source_file="<eval_with_key>.1" source_line=5}
  %multiply.47 = s32[1]{0} multiply(s32[1]{0} %p1.44, s32[1]{0} %broadcast.46), metadata={op_type="aten__add" op_name="aten__add.7/aten__add" source_file="<eval_with_key>.1" source_line=5}
  %add.48 = s32[1]{0} add(s32[1]{0} %p2.45, s32[1]{0} %multiply.47), metadata={op_type="aten__add" op_name="aten__add.7/aten__add" source_file="<eval_with_key>.1" source_line=5}
  %p3.50 = s32[1]{0} parameter(3), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %constant.49 = s32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="<eval_with_key>.1" source_line=10}
  %broadcast.51 = s32[1]{0} broadcast(s32[] %constant.49), dimensions={}, metadata={op_type="aten__add" op_name="aten__add.9/aten__add" source_file="<eval_with_key>.1" source_line=10}
  %multiply.52 = s32[1]{0} multiply(s32[1]{0} %p3.50, s32[1]{0} %broadcast.51), metadata={op_type="aten__add" op_name="aten__add.9/aten__add" source_file="<eval_with_key>.1" source_line=10}
  %add.53 = s32[1]{0} add(s32[1]{0} %p2.45, s32[1]{0} %multiply.52), metadata={op_type="aten__add" op_name="aten__add.9/aten__add" source_file="<eval_with_key>.1" source_line=10}
  %p4.54 = f32[10]{0} parameter(4), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %reshape.59 = f32[1,10]{1,0} reshape(f32[10]{0} %p4.54), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  %p6.57 = f32[20,10]{0,1} parameter(6), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=82}
  %transpose.58 = f32[10,20]{1,0} transpose(f32[20,10]{0,1} %p6.57), dimensions={1,0}, metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  %dot.60 = f32[1,20]{1,0} dot(f32[1,10]{1,0} %reshape.59, f32[10,20]{1,0} %transpose.58), lhs_contracting_dims={1}, rhs_contracting_dims={0}, metadata={op_type="aten__mm" op_name="aten__mm" source_file="<eval_with_key>.1" source_line=6}
  %reshape.61 = f32[20]{0} reshape(f32[1,20]{1,0} %dot.60), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  %p5.56 = f32[20]{0} parameter(5), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=82}
  %constant.55 = f32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="<eval_with_key>.1" source_line=6}
  %broadcast.62 = f32[20]{0} broadcast(f32[] %constant.55), dimensions={}, metadata={op_type="aten__add" op_name="aten__add.8/aten__add" source_file="<eval_with_key>.1" source_line=6}
  %multiply.63 = f32[20]{0} multiply(f32[20]{0} %p5.56, f32[20]{0} %broadcast.62), metadata={op_type="aten__add" op_name="aten__add.8/aten__add" source_file="<eval_with_key>.1" source_line=6}
  %add.64 = f32[20]{0} add(f32[20]{0} %reshape.61, f32[20]{0} %multiply.63), metadata={op_type="aten__add" op_name="aten__add.8/aten__add" source_file="<eval_with_key>.1" source_line=6}
  %reshape.65 = f32[1,20]{1,0} reshape(f32[20]{0} %add.64), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  %reshape.66 = f32[20]{0} reshape(f32[1,20]{1,0} %reshape.65), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.1" source_line=6}
  ROOT %tuple.67 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) tuple(s32[1]{0} %p0.42, s32[1]{0} %add.48, s32[1]{0} %p2.45, s32[1]{0} %add.53, f32[10]{0} %p4.54, /*index=5*/f32[20]{0} %reshape.66, f32[20,10]{0,1} %p6.57, f32[20]{0} %p5.56)
}

%PyLoweringContext.28.68 (in.69: (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20])) -> (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20]) {
  %in.69 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) parameter(0)
  %get-tuple-element.70 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.69), index=0
  %get-tuple-element.71 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.69), index=1
  %get-tuple-element.72 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.69), index=2
  %get-tuple-element.73 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.69), index=3
  %get-tuple-element.74 = f32[10]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.69), index=4
  %get-tuple-element.75 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.69), index=5
  %get-tuple-element.76 = f32[20,10]{0,1} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.69), index=6
  %get-tuple-element.77 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.69), index=7
  ROOT %call.78 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) call(s32[1]{0} %get-tuple-element.70, s32[1]{0} %get-tuple-element.71, s32[1]{0} %get-tuple-element.72, s32[1]{0} %get-tuple-element.73, f32[10]{0} %get-tuple-element.74, /*index=5*/f32[20]{0} %get-tuple-element.75, f32[20,10]{0,1} %get-tuple-element.76, f32[20]{0} %get-tuple-element.77), to_apply=%PyLoweringContext.40
}

%PyLoweringContext.79 (p0.86: s32[1], p1.88: s32[1], UnusedArgumentsPlaceholder.80: s32[1], UnusedArgumentsPlaceholder.81: s32[1], UnusedArgumentsPlaceholder.82: f32[10], UnusedArgumentsPlaceholder.83: f32[20], UnusedArgumentsPlaceholder.84: f32[20,10], UnusedArgumentsPlaceholder.85: f32[20]) -> pred[] {
  %UnusedArgumentsPlaceholder.80 = s32[1]{0} parameter(2)
  %UnusedArgumentsPlaceholder.81 = s32[1]{0} parameter(3)
  %UnusedArgumentsPlaceholder.82 = f32[10]{0} parameter(4)
  %UnusedArgumentsPlaceholder.83 = f32[20]{0} parameter(5)
  %UnusedArgumentsPlaceholder.84 = f32[20,10]{0,1} parameter(6)
  %UnusedArgumentsPlaceholder.85 = f32[20]{0} parameter(7)
  %p1.88 = s32[1]{0} parameter(1), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %reshape.89 = s32[] reshape(s32[1]{0} %p1.88), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.0" source_line=5}
  %p0.86 = s32[1]{0} parameter(0), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=76}
  %reshape.87 = s32[] reshape(s32[1]{0} %p0.86), metadata={op_type="aten__as_strided" op_name="aten__as_strided" source_file="<eval_with_key>.0" source_line=6}
  ROOT %compare.90 = pred[] compare(s32[] %reshape.89, s32[] %reshape.87), direction=LT, metadata={op_type="aten__lt" op_name="aten__lt" source_file="<eval_with_key>.0" source_line=7}
}

%PyLoweringContext.12.91 (in.92: (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20])) -> pred[] {
  %in.92 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) parameter(0)
  %get-tuple-element.93 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.92), index=0
  %get-tuple-element.94 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.92), index=1
  %get-tuple-element.95 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.92), index=2
  %get-tuple-element.96 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.92), index=3
  %get-tuple-element.97 = f32[10]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.92), index=4
  %get-tuple-element.98 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.92), index=5
  %get-tuple-element.99 = f32[20,10]{0,1} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.92), index=6
  %get-tuple-element.100 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{0,1}, f32[20]{0}) %in.92), index=7
  ROOT %call.101 = pred[] call(s32[1]{0} %get-tuple-element.93, s32[1]{0} %get-tuple-element.94, s32[1]{0} %get-tuple-element.95, s32[1]{0} %get-tuple-element.96, f32[10]{0} %get-tuple-element.97, /*index=5*/f32[20]{0} %get-tuple-element.98, f32[20,10]{0,1} %get-tuple-element.99, f32[20]{0} %get-tuple-element.100), to_apply=%PyLoweringContext.79
}

%test_while.102 (p0.103: s32[1], p1.104: s32[1], p2.105: s32[1], p3.106: s32[1], p4.107: f32[10], p5.108: f32[20], p6.109: f32[20], p7.110: f32[20,10]) -> (s32[1], s32[1], s32[1], s32[1], f32[10], /*index=5*/f32[20], f32[20,10], f32[20]) {
  %p0.103 = s32[1]{0} parameter(0)
  %p1.104 = s32[1]{0} parameter(1)
  %p2.105 = s32[1]{0} parameter(2)
  %p3.106 = s32[1]{0} parameter(3)
  %p4.107 = f32[10]{0} parameter(4)
  %p6.109 = f32[20]{0} parameter(6)
  %p7.110 = f32[20,10]{1,0} parameter(7)
  %p5.108 = f32[20]{0} parameter(5)
  %tuple.111 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) tuple(s32[1]{0} %p0.103, s32[1]{0} %p1.104, s32[1]{0} %p2.105, s32[1]{0} %p3.106, f32[10]{0} %p4.107, /*index=5*/f32[20]{0} %p6.109, f32[20,10]{1,0} %p7.110, f32[20]{0} %p5.108)
  ROOT %while.112 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) while((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %tuple.111), condition=%PyLoweringContext.12.91, body=%PyLoweringContext.28.68
}

ENTRY %IrToHlo.130 (p0.2: f32[20,10], p1.3: f32[20], p2.9: s64[], p3.36: s32[1], p4.37: s32[1], p5.38: s32[1], p6.39: s32[1]) -> (f32[20]) {
  %constant.12 = s64[] constant(2531011), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.10 = s64[] constant(214013), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %p2.9 = s64[] parameter(2), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %multiply.11 = s64[] multiply(s64[] %constant.10, s64[] %p2.9), metadata={op_type="aten__mul" op_name="aten__mul" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %add.13 = s64[] add(s64[] %constant.12, s64[] %multiply.11), metadata={op_type="aten__add" op_name="aten__add" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %convert.16 = u64[] convert(s64[] %add.13), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %reshape.18 = u64[1]{0} reshape(u64[] %convert.16), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.17 = u64[] constant(0), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %reshape.19 = u64[1]{0} reshape(u64[] %constant.17), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %concatenate.20 = u64[2]{0} concatenate(u64[1]{0} %reshape.18, u64[1]{0} %reshape.19), dimensions={0}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %rng-bit-generator.21 = (u64[2]{0}, u32[10]{0}) rng-bit-generator(u64[2]{0} %concatenate.20), algorithm=rng_default, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %get-tuple-element.23 = u64[2]{0} get-tuple-element((u64[2]{0}, u32[10]{0}) %rng-bit-generator.21), index=0, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %p6.39 = s32[1]{0} parameter(6), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=43}
  %p5.38 = s32[1]{0} parameter(5), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=44}
  %p4.37 = s32[1]{0} parameter(4), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=45}
  %p3.36 = s32[1]{0} parameter(3), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=46}
  %get-tuple-element.22 = u32[10]{0} get-tuple-element((u64[2]{0}, u32[10]{0}) %rng-bit-generator.21), index=1, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.24 = u32[] constant(9), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %broadcast.25 = u32[10]{0} broadcast(u32[] %constant.24), dimensions={}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %shift-right-logical.26 = u32[10]{0} shift-right-logical(u32[10]{0} %get-tuple-element.22, u32[10]{0} %broadcast.25), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %convert.27 = f32[10]{0} convert(u32[10]{0} %shift-right-logical.26), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.28 = f32[] constant(1.1920929e-07), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %broadcast.29 = f32[10]{0} broadcast(f32[] %constant.28), dimensions={}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %multiply.30 = f32[10]{0} multiply(f32[10]{0} %convert.27, f32[10]{0} %broadcast.29), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.14 = f32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.15 = f32[] constant(0), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %subtract.31 = f32[] subtract(f32[] %constant.14, f32[] %constant.15), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %broadcast.32 = f32[10]{0} broadcast(f32[] %subtract.31), dimensions={}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %multiply.33 = f32[10]{0} multiply(f32[10]{0} %multiply.30, f32[10]{0} %broadcast.32), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %broadcast.34 = f32[10]{0} broadcast(f32[] %constant.15), dimensions={}, metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %add.35 = f32[10]{0} add(f32[10]{0} %multiply.33, f32[10]{0} %broadcast.34), metadata={op_type="aten__uniform" op_name="aten__uniform" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=47}
  %constant.4 = f32[] constant(0), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %reshape.5 = f32[1]{0} reshape(f32[] %constant.4), metadata={op_type="aten__expand" op_name="aten__expand" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %broadcast.6 = f32[1]{0} broadcast(f32[1]{0} %reshape.5), dimensions={0}, metadata={op_type="aten__expand" op_name="aten__expand" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %reshape.7 = f32[] reshape(f32[1]{0} %broadcast.6), metadata={op_type="aten__expand" op_name="aten__expand" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %broadcast.8 = f32[20]{0} broadcast(f32[] %reshape.7), dimensions={}, metadata={op_type="aten__expand" op_name="aten__expand" source_file="/root/pytorch/xla/test/test_test_fori_loop.py" source_line=48}
  %p1.3 = f32[20]{0} parameter(1), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/torch/nn/modules/module.py" source_line=1159}
  %p0.2 = f32[20,10]{0,1} parameter(0), metadata={op_type="xla__device_data" op_name="xla__device_data" source_file="/root/pytorch/torch/nn/modules/module.py" source_line=1159}
  %call.113 = (s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) call(s32[1]{0} %p6.39, s32[1]{0} %p5.38, s32[1]{0} %p4.37, s32[1]{0} %p3.36, f32[10]{0} %add.35, /*index=5*/f32[20]{0} %broadcast.8, f32[20]{0} %p1.3, f32[20,10]{0,1} %p0.2), to_apply=%test_while.102, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.114 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.113), index=0, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.115 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.113), index=1, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.116 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.113), index=2, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.117 = s32[1]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.113), index=3, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.118 = f32[10]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.113), index=4, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %get-tuple-element.119 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.113), index=5, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %reshape.123 = f32[1,10]{1,0} reshape(f32[10]{0} %add.35), metadata={op_type="aten__view" op_name="aten__view" source_file="/root/pytorch/torch/nn/modules/linear.py" source_line=116}
  %get-tuple-element.120 = f32[20,10]{1,0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.113), index=6, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %transpose.122 = f32[10,20]{0,1} transpose(f32[20,10]{1,0} %get-tuple-element.120), dimensions={1,0}, metadata={op_type="aten__permute" op_name="aten__permute" source_file="/root/pytorch/torch/nn/modules/linear.py" source_line=116}
  %dot.124 = f32[1,20]{1,0} dot(f32[1,10]{1,0} %reshape.123, f32[10,20]{0,1} %transpose.122), lhs_contracting_dims={1}, rhs_contracting_dims={0}, metadata={op_type="aten__mm" op_name="aten__mm" source_file="/root/pytorch/torch/nn/modules/linear.py" source_line=116}
  %reshape.125 = f32[20]{0} reshape(f32[1,20]{1,0} %dot.124), metadata={op_type="aten__view" op_name="aten__view" source_file="/root/pytorch/torch/nn/modules/linear.py" source_line=116}
  %get-tuple-element.121 = f32[20]{0} get-tuple-element((s32[1]{0}, s32[1]{0}, s32[1]{0}, s32[1]{0}, f32[10]{0}, /*index=5*/f32[20]{0}, f32[20,10]{1,0}, f32[20]{0}) %call.113), index=7, metadata={op_type="xla___op_test_while" op_name="xla___op_test_while" source_file="/root/pytorch/xla/torch_xla/experimental/fori_loop.py" source_line=152}
  %constant.1 = f32[] constant(1), metadata={op_type="prim__Constant" op_name="prim__Constant" source_file="/root/pytorch/torch/nn/modules/linear.py" source_line=116}
  %broadcast.126 = f32[20]{0} broadcast(f32[] %constant.1), dimensions={}, metadata={op_type="aten__add" op_name="aten__add.62/aten__add" source_file="/root/pytorch/torch/nn/modules/linear.py" source_line=116}
  %multiply.127 = f32[20]{0} multiply(f32[20]{0} %get-tuple-element.121, f32[20]{0} %broadcast.126), metadata={op_type="aten__add" op_name="aten__add.62/aten__add" source_file="/root/pytorch/torch/nn/modules/linear.py" source_line=116}
  %add.128 = f32[20]{0} add(f32[20]{0} %reshape.125, f32[20]{0} %multiply.127), metadata={op_type="aten__add" op_name="aten__add.62/aten__add" source_file="/root/pytorch/torch/nn/modules/linear.py" source_line=116}
  ROOT %tuple.129 = (f32[20]{0}) tuple(f32[20]{0} %add.128)
}


Graph Hash: ddfe4f6f6877d38fcadd799f1f67cc2c

## END_GRAPH

(torch310) root@t1v-n-0b41c02e-w-0:~/pytorch/xla# rm -rf /tmp/