OSSForks/pytorch
1
0
Fork 0
mirror of https://github.com/zebrajr/pytorch.git synced 2025-12-06 12:20:52 +01:00
Code Issues Actions 38 Packages Projects Releases Wiki Activity
69,153 Commits 8,304 Branches 150 Tags 1.5 GiB
b816760a2f
Commit Graph

48 Commits

Author SHA1 Message Date
Edward Z. Yang
b816760a2f More progress on type checking ValueRanges (#118870) 
Type checking Python is a pain. Here are my learnings:

* The types for heavily polymorphic code is going to be verbose, no way around it. I originally was hoping I could lean on polymorphism with a bounded TypeVar to compactly write signatures for many of the ValueRanges methods, but I ran into some unworkaroundable mypy bugs. Writing out all the types explicitly and using `@overload` liberally works pretty well, so I think I recommend people do that instead of trying to do fancy things.
* Sympy is missing annotations for assumptions, because they are all metaprogrammed. I don't really relish maintaining a typeshed for sympy, so I wrote a small mypy plugin to add them in.
* GADT style refinement is... just not a good idea in practice. Mypy easily gets confused whether or not a return value from a refined section is allowed for the outer return type. So many of these have been replaced with less informative implementation types and more informative external types via overloads. Hopefully this is good for use sites.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118870
Approved by: https://github.com/Skylion007, https://github.com/albanD
 2024-02-05 20:29:25 +00:00
Edward Z. Yang
abc09b27b9 Some minor type stub improvements (#118529) 
I was just playing around with improving the typing of symbolic_shapes. The PR is not "complete" but I in particular wanted to get feedback on whether or not people liked making ValueRanges Generic; it seems that distinguishing if you have an Expr ValueRange or a SympyBoolean ValueRange is a lot of trouble for downstream. Using TypeGuard, we can perform refinements on the generic parameter inside methods, although we still have to cast back to ValueRange[T] due to https://github.com/python/mypy/issues/14425#issuecomment-1914852707

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/118529
Approved by: https://github.com/Skylion007
 2024-02-04 00:19:00 +00:00
PyTorch MergeBot
dbba1d4bf5 Revert "Some minor type stub improvements (#118529)" 
This reverts commit c978f38bd4.

Reverted https://github.com/pytorch/pytorch/pull/118529 on behalf of https://github.com/facebook-github-bot due to Diff reverted internally ([comment](https://github.com/pytorch/pytorch/pull/118529#issuecomment-1922362331))
 2024-02-01 22:18:36 +00:00
Edward Z. Yang
c978f38bd4 Some minor type stub improvements (#118529) 
I was just playing around with improving the typing of symbolic_shapes. The PR is not "complete" but I in particular wanted to get feedback on whether or not people liked making ValueRanges Generic; it seems that distinguishing if you have an Expr ValueRange or a SympyBoolean ValueRange is a lot of trouble for downstream. Using TypeGuard, we can perform refinements on the generic parameter inside methods, although we still have to cast back to ValueRange[T] due to https://github.com/python/mypy/issues/14425#issuecomment-1914852707

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/118529
Approved by: https://github.com/Skylion007
 2024-01-31 20:56:56 +00:00
Edward Z. Yang
d03173e88c Unify MYPYINDUCTOR and MYPY (#118432) 
The original motivation for MYPYINDUCTOR was a faster type checking configuration that only checked a subset of files. With the removal of `follow_imports = ignore`, we are now able to use dmypy to do fast incremental typechecking, eliminating the need for this.

Perhaps erroneously, when I tee'ed up this PR I elected to delete the `follow_imports = skip` designations in the mypy-inductor.ini. This lead to a number of extra type error suppressions that I manually edited. You will need to review.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/118432
Approved by: https://github.com/Skylion007
ghstack dependencies: #118414, #118418
 2024-01-27 17:23:20 +00:00
vfdev-5
7005a4bcb6 [dynamo] Added dyn shapes support for math trigo ops: sin(h), cos(h), tan(h) ... (#114866) 
Description:
- Added dynamic shapes support for math trigo ops: sin(h), cos(h), tan(h) ...

```python
import math
import torch

def func(x, a, b):
    c = 0
    c = c + math.sqrt(a)
    c = c + math.cos(a)
    c = c + math.cosh(a)
    c = c + math.sin(a)
    c = c + math.sinh(a)
    c = c + math.tan(a)
    c = c + math.tanh(a)
    c = c + math.asin(b)
    c = c + math.acos(b)
    c = c + math.atan(a)
    y = x + c
    return y

cfunc = torch.compile(func, dynamic=True, fullgraph=True)

device = "cpu"  # or "cuda"
x = torch.tensor([0, 1, 2, 3], dtype=torch.float32, device=device)
a = 12
b = 1

out = cfunc(x, a, b)
expected = func(x, a, b)
torch.testing.assert_close(out, expected)
```

and the graph `TORCH_LOGS=+graph_code python check_math_ops.py`:

<details>
<summary>
graph code
</summary>

```
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG] TRACED GRAPH
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]  ===== __compiled_fn_0 =====
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]  <eval_with_key>.0 class GraphModule(torch.nn.Module):
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]     def forward(self, L_a_ : torch.SymInt, s1 : torch.SymInt, L_x_ : torch.Tensor):
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         l_a_ = L_a_
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         l_x_ = L_x_
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         # File: check_math_ops.py:57, code: c = c + math.sqrt(a)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         sym_sqrt = torch.sym_sqrt(l_a_)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         add = 0 + sym_sqrt;  sym_sqrt = None
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         # File: check_math_ops.py:58, code: c = c + math.cos(a)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         sym_cos = torch.sym_cos(l_a_)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         add_1 = add + sym_cos;  add = sym_cos = None
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         # File: check_math_ops.py:59, code: c = c + math.cosh(a)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         sym_cosh = torch.sym_cosh(l_a_)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         add_2 = add_1 + sym_cosh;  add_1 = sym_cosh = None
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         # File: check_math_ops.py:60, code: c = c + math.sin(a)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         sym_sin = torch.sym_sin(l_a_)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         add_3 = add_2 + sym_sin;  add_2 = sym_sin = None
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         # File: check_math_ops.py:61, code: c = c + math.sinh(a)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         sym_sinh = torch.sym_sinh(l_a_)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         add_4 = add_3 + sym_sinh;  add_3 = sym_sinh = None
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         # File: check_math_ops.py:62, code: c = c + math.tan(a)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         sym_tan = torch.sym_tan(l_a_)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         add_5 = add_4 + sym_tan;  add_4 = sym_tan = None
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         # File: check_math_ops.py:63, code: c = c + math.tanh(a)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         sym_tanh = torch.sym_tanh(l_a_)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         add_6 = add_5 + sym_tanh;  add_5 = sym_tanh = None
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         # File: check_math_ops.py:64, code: c = c + math.asin(b)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         add_7 = add_6 + 1.5707963267948966;  add_6 = None
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         # File: check_math_ops.py:65, code: c = c + math.acos(b)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         add_8 = add_7 + 0.0;  add_7 = None
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         # File: check_math_ops.py:66, code: c = c + math.atan(a)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         sym_atan = torch.sym_atan(l_a_);  l_a_ = None
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         add_9 = add_8 + sym_atan;  add_8 = sym_atan = None
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         # File: check_math_ops.py:67, code: y = x + c
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         y = l_x_ + add_9;  l_x_ = add_9 = None
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]         return (y,)
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
[2023-11-30 22:16:10,654] [0/0] torch._dynamo.output_graph.__graph_code: [DEBUG]
```
</details>

Generated code with `TORCH_LOGS=+output_code python check_math_ops.py`:
<details>
<summary>
C++ code
</summary>

```
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG] cpp_fused_add_0 = async_compile.cpp('''
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG] #include "/tmp/torchinductor_root/2l/c2ljzlm4sosod7u6lyrroqdba6hmfcyijrric6p4t3fhbcmw6osp.h"
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG] extern "C" void kernel(const float* in_ptr0,
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]                        float* out_ptr0,
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]                        const long ks0,
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]                        const long ks1)
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG] {
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]     {
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]         #pragma GCC ivdep
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]         for(long x0=static_cast<long>(0L); x0<static_cast<long>(ks0); x0+=static_cast<long>(1L))
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]         {
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]             auto tmp0 = in_ptr0[static_cast<long>(x0)];
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]             auto tmp1 = c10::convert<float>(1.57079632679490 + (std::sqrt(ks1)) + (std::atan(ks1)) + (std::cos(ks1)) + (std::cosh(ks1)) + (std::sin(ks1)) + (std::sinh(ks1)) + (std::tan(ks1)) + (std::tanh(ks1)));
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]             auto tmp2 = decltype(tmp0)(tmp0 + tmp1);
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]             out_ptr0[static_cast<long>(x0)] = tmp2;
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]         }
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG]     }
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG] }
[2023-11-30 22:19:09,709] [0/0] torch._inductor.graph.__output_code: [DEBUG] ''')
```

</details>

<details>
<summary>
Triton code
</summary>

```
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG] @pointwise(
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     size_hints=[4],
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     filename=__file__,
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [instance_descriptor(divisible_by_16=(0, 1), equal_to_1=(), i
ds_of_folded_args=(), divisible_by_8=())]},
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': []},
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     min_elem_per_thread=0
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG] )
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG] @triton.jit
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG] def triton_(in_ptr0, out_ptr0, ks0, xnumel, XBLOCK : tl.constexpr):
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     xoffset = tl.program_id(0) * XBLOCK
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     xindex = xoffset + tl.arange(0, XBLOCK)[:]
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     xmask = xindex < xnumel
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     x0 = xindex
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     tmp0 = tl.load(in_ptr0 + (x0), xmask)
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     tmp1 = 1.57079632679490 + (tl.math.sqrt(ks0.to(tl.float32))) + (tl.math.atan((ks0).to(tl.float32))) + (tl.math.cos((ks0).to(tl.float32))) + (tl.math.cosh((ks0).to(tl.float32))) + (tl.math.sin((ks0)
.to(tl.float32))) + (tl.math.sinh((ks0).to(tl.float32))) + (tl.math.tan((ks0).to(tl.float32))) + (tl.math.tanh((ks0).to(tl.float32)))
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     tmp2 = tmp1.to(tl.float32)
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     tmp3 = tmp0 + tmp2
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG]     tl.store(out_ptr0 + (x0), tmp3, xmask)
[2023-11-30 22:20:00,383] [0/0] torch._inductor.graph.__output_code: [DEBUG] ''')
```

</details>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/114866
Approved by: https://github.com/peterbell10
 2024-01-11 11:52:28 +00:00
Edward Z. Yang
33d90cfd16 Allow for [-oo, oo] ranges for bools (#114362) 
This fixes a problem in Seamless M4T in fairseq2 repro
instructions at https://docs.google.com/document/d/1PVy4KibfljirQDoijOwyHCV97B67r_iElWqFh7h1Acc/edit

I tried extracting a minimal repro but I couldn't actually manage it!

Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114362
Approved by: https://github.com/Skylion007
 2024-01-09 01:08:34 +00:00
Philip Meier
505a9e4854 add support for dynamic shapes in round (#115259) 
Fixes #114310 and supersedes #114748.

There are two reasons why we have quite a few special cases for `round`:

1. `round` is actually two ops. With `ndigits=None` (default), `round` always returns an integer. When `ndigits` is an integer, the returned type is a float.
2. Although `round` takes two arguments, it is a unary function with a parameter rather than a binary one.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/115259
Approved by: https://github.com/peterbell10, https://github.com/lezcano
 2023-12-19 15:45:50 +00:00
lezcano
0a9819e3e1 Prefer is_number over is_constant() (#114513) 
`is_constant` tries really hard to check whether an expression is
constant. `is_number` is often enough. Note that `sympy.nan.is_number`
is true. Same for infinities

Pull Request resolved: https://github.com/pytorch/pytorch/pull/114513
Approved by: https://github.com/peterbell10
 2023-12-05 16:56:15 +00:00
Jez Ng
5b95715bc0 Make {Tracing,Compile}Context.get() return non-optional type (#113535) 
They are used in many contexts that don't actually check if the returned
type is `None`. I have also created `try_get()` for the cases where we
do actually want an Optional type returned.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/113535
Approved by: https://github.com/ezyang
ghstack dependencies: #113412
 2023-11-14 04:31:12 +00:00
ooooo
a8097ed479 Fix docstring errors in _composable_state.py, remote_device.py, value_ranges.py, utils.py, run.py, rendezvous.py, launch.py, argparse_util.py, __init__.py, _cycles.py (#112953) 
Fixes #112639

```txt
 torch/utils/_sympy/value_ranges.py
 torch/utils/_sympy/value_ranges.py:60 in public class `ValueRanges`:
        D101: Missing docstring in public class
torch/utils/_sympy/value_ranges.py:68 in public method `__init__`:
        D107: Missing docstring in __init__
torch/utils/_sympy/value_ranges.py:81 in public method `__contains__`:
        D105: Missing docstring in magic method
torch/utils/_sympy/value_ranges.py:86 in public method `tighten`:
        D400: First line should end with a period (not 'n')
torch/utils/_sympy/value_ranges.py:90 in public method `__and__`:
        D105: Missing docstring in magic method
torch/utils/_sympy/value_ranges.py:103 in public method `__or__`:
        D105: Missing docstring in magic method
torch/utils/_sympy/value_ranges.py:113 in public method `is_singleton`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:118 in public method `unknown`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:122 in public method `wrap`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:129 in public method `increasing_map`:
        D400: First line should end with a period (not ')')
torch/utils/_sympy/value_ranges.py:135 in public method `decreasing_map`:
        D400: First line should end with a period (not ')')
torch/utils/_sympy/value_ranges.py:141 in public method `monotone_map`:
        D400: First line should end with a period (not 'g')
torch/utils/_sympy/value_ranges.py:149 in public method `convex_min_zero_map`:
        D400: First line should end with a period (not '0')
torch/utils/_sympy/value_ranges.py:149 in public method `convex_min_zero_map`:
        D403: First word of the first line should be properly capitalized ('Fn', not 'fn')
torch/utils/_sympy/value_ranges.py:158 in public method `coordinatewise_increasing_map`:
        D205: 1 blank line required between summary line and description (found 0)
torch/utils/_sympy/value_ranges.py:158 in public method `coordinatewise_increasing_map`:
        D400: First line should end with a period (not ':')
torch/utils/_sympy/value_ranges.py:171 in public method `coordinatewise_monotone_map`:
        D400: First line should end with a period (not 'e')
torch/utils/_sympy/value_ranges.py:180 in private class `SymPyValueRangeAnalysis`:
        D205: 1 blank line required between summary line and description (found 0)
torch/utils/_sympy/value_ranges.py:180 in private class `SymPyValueRangeAnalysis`:
        D400: First line should end with a period (not 's')
torch/utils/_sympy/value_ranges.py:386 in private method `reciprocal`:
        D210: No whitespaces allowed surrounding docstring text
torch/utils/_sympy/value_ranges.py:386 in private method `reciprocal`:
        D400: First line should end with a period (not 'n')
torch/utils/_sympy/value_ranges.py:488 in public class `ValueRangeAnalysis`:
        D101: Missing docstring in public class
torch/utils/_sympy/value_ranges.py:489 in public method `__init__`:
        D107: Missing docstring in __init__
torch/utils/_sympy/value_ranges.py:501 in public method `bool_handler`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:506 in public method `default_handler`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:511 in public method `load`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:514 in public method `store`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:517 in public method `reduction`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:520 in public method `index_expr`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:525 in public method `to_dtype`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:558 in public method `square`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:562 in public method `neg`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:566 in public method `truncdiv`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:577 in public method `sub`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:580 in public method `__getattr__`:
        D105: Missing docstring in magic method
torch/utils/_sympy/value_ranges.py:585 in public function `bound_sympy`:
        D103: Missing docstring in public function
36
torch/utils/_sympy/value_ranges.py:60 in public class `ValueRanges`:
        D101: Missing docstring in public class
torch/utils/_sympy/value_ranges.py:68 in public method `__init__`:
        D107: Missing docstring in __init__
torch/utils/_sympy/value_ranges.py:81 in public method `__contains__`:
        D105: Missing docstring in magic method
torch/utils/_sympy/value_ranges.py:86 in public method `tighten`:
        D400: First line should end with a period (not 'n')
torch/utils/_sympy/value_ranges.py:90 in public method `__and__`:
        D105: Missing docstring in magic method
torch/utils/_sympy/value_ranges.py:103 in public method `__or__`:
        D105: Missing docstring in magic method
torch/utils/_sympy/value_ranges.py:113 in public method `is_singleton`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:118 in public method `unknown`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:122 in public method `wrap`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:182 in private class `SymPyValueRangeAnalysis`:
        D205: 1 blank line required between summary line and description (found 0)
torch/utils/_sympy/value_ranges.py:182 in private class `SymPyValueRangeAnalysis`:
        D400: First line should end with a period (not 's')
torch/utils/_sympy/value_ranges.py:388 in private method `reciprocal`:
        D210: No whitespaces allowed surrounding docstring text
torch/utils/_sympy/value_ranges.py:388 in private method `reciprocal`:
        D400: First line should end with a period (not 'n')
torch/utils/_sympy/value_ranges.py:490 in public class `ValueRangeAnalysis`:
        D101: Missing docstring in public class
torch/utils/_sympy/value_ranges.py:491 in public method `__init__`:
        D107: Missing docstring in __init__
torch/utils/_sympy/value_ranges.py:503 in public method `bool_handler`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:508 in public method `default_handler`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:513 in public method `load`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:516 in public method `store`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:519 in public method `reduction`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:522 in public method `index_expr`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:527 in public method `to_dtype`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:560 in public method `square`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:564 in public method `neg`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:568 in public method `truncdiv`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:579 in public method `sub`:
        D102: Missing docstring in public method
torch/utils/_sympy/value_ranges.py:582 in public method `__getattr__`:
        D105: Missing docstring in magic method
torch/utils/_sympy/value_ranges.py:587 in public function `bound_sympy`:
        D103: Missing docstring in public function
28

torch/utils/viz/_cycles.py
torch/utils/viz/_cycles.py:14 in public function `observe_garbage`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:207 in public function `object_annotation`:
        D205: 1 blank line required between summary line and description (found 0)
torch/utils/viz/_cycles.py:207 in public function `object_annotation`:
        D400: First line should end with a period (not 'g')
torch/utils/viz/_cycles.py:256 in public class `Node`:
        D101: Missing docstring in public class
torch/utils/viz/_cycles.py:262 in public function `create_graph`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:308 in public function `escape`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:312 in public function `is_cuda_tensor`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:315 in public function `cuda_allocation_context`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:335 in public function `to_dot`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:406 in public function `to_html`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:416 in public function `observe_tensor_cycles`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:429 in public function `warn_tensor_cycles`:
        D205: 1 blank line required between summary line and description (found 0)
torch/utils/viz/_cycles.py:429 in public function `warn_tensor_cycles`:
        D400: First line should end with a period (not 'p')
torch/utils/viz/_cycles.py:429 in public function `warn_tensor_cycles`:
        D401: First line should be in imperative mood; try rephrasing (found 'Reference')
14
torch/utils/viz/_cycles.py:14 in public function `observe_garbage`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:256 in public class `Node`:
        D101: Missing docstring in public class
torch/utils/viz/_cycles.py:262 in public function `create_graph`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:308 in public function `escape`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:312 in public function `is_cuda_tensor`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:315 in public function `cuda_allocation_context`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:335 in public function `to_dot`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:406 in public function `to_html`:
        D103: Missing docstring in public function
torch/utils/viz/_cycles.py:416 in public function `observe_tensor_cycles`:
        D103: Missing docstring in public function
9

torch/distributed/argparse_util.py
torch/distributed/argparse_util.py:1 at module level:
        D100: Missing docstring in public module
torch/distributed/argparse_util.py:13 in public class `env`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/argparse_util.py:13 in public class `env`:
        D400: First line should end with a period (not 'g')
torch/distributed/argparse_util.py:13 in public class `env`:
        D412: No blank lines allowed between a section header and its content ('Example')
torch/distributed/argparse_util.py:43 in public method `__init__`:
        D107: Missing docstring in __init__
torch/distributed/argparse_util.py:56 in public method `__call__`:
        D102: Missing docstring in public method
torch/distributed/argparse_util.py:61 in public class `check_env`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/argparse_util.py:61 in public class `check_env`:
        D400: First line should end with a period (not 's')
torch/distributed/argparse_util.py:61 in public class `check_env`:
        D412: No blank lines allowed between a section header and its content ('Example')
torch/distributed/argparse_util.py:97 in public method `__init__`:
        D107: Missing docstring in __init__
torch/distributed/argparse_util.py:102 in public method `__call__`:
        D102: Missing docstring in public method
11
torch/distributed/argparse_util.py:1 at module level:
        D100: Missing docstring in public module
torch/distributed/argparse_util.py:43 in public method `__init__`:
        D107: Missing docstring in __init__
torch/distributed/argparse_util.py:56 in public method `__call__`:
        D102: Missing docstring in public method
torch/distributed/argparse_util.py:97 in public method `__init__`:
        D107: Missing docstring in __init__
torch/distributed/argparse_util.py:102 in public method `__call__`:
        D102: Missing docstring in public method
5

torch/distributed/_composable_state.py
torch/distributed/_composable_state.py:20 in private function `_get_module_state`:
        D202: No blank lines allowed after function docstring (found 1)
torch/distributed/_composable_state.py:20 in private function `_get_module_state`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/_composable_state.py:20 in private function `_get_module_state`:
        D400: First line should end with a period (not '`')
3
0

torch/distributed/launch.py
torch/distributed/launch.py:1 at module level:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/launch.py:1 at module level:
        D400: First line should end with a period (not 'd')
torch/distributed/launch.py:156 in public function `parse_args`:
        D103: Missing docstring in public function
torch/distributed/launch.py:171 in public function `launch`:
        D103: Missing docstring in public function
torch/distributed/launch.py:180 in public function `main`:
        D103: Missing docstring in public function
5
torch/distributed/launch.py:157 in public function `parse_args`:
        D103: Missing docstring in public function
torch/distributed/launch.py:172 in public function `launch`:
        D103: Missing docstring in public function
torch/distributed/launch.py:181 in public function `main`:
        D103: Missing docstring in public function
3

torch/distributed/remote_device.py
torch/distributed/remote_device.py:1 at module level:
        D100: Missing docstring in public module
torch/distributed/remote_device.py:81 in private method `worker_name`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/remote_device.py:81 in private method `worker_name`:
        D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/distributed/remote_device.py:88 in private method `rank`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/remote_device.py:88 in private method `rank`:
        D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/distributed/remote_device.py:95 in private method `device`:
        D200: One-line docstring should fit on one line with quotes (found 3)
torch/distributed/remote_device.py:95 in private method `device`:
        D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
7
torch/distributed/remote_device.py:1 at module level:
        D100: Missing docstring in public module
torch/distributed/remote_device.py:85 in private method `rank`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/remote_device.py:85 in private method `rank`:
        D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
3

torch/distributed/rendezvous.py
torch/distributed/rendezvous.py:1 at module level:
        D100: Missing docstring in public module
torch/distributed/rendezvous.py:23 in public function `register_rendezvous_handler`:
        D401: First line should be in imperative mood (perhaps 'Register', not 'Registers')
torch/distributed/rendezvous.py:88 in public function `rendezvous`:
        D103: Missing docstring in public function
torch/distributed/rendezvous.py:147 in private function `_create_c10d_store`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/rendezvous.py:147 in private function `_create_c10d_store`:
        D400: First line should end with a period (not 'r')
5
torch/distributed/rendezvous.py:1 at module level:
        D100: Missing docstring in public module
torch/distributed/rendezvous.py:89 in public function `rendezvous`:
        D103: Missing docstring in public function
2

torch/distributed/run.py
torch/distributed/run.py:9 at module level:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/run.py:9 at module level:
        D400: First line should end with a period (not '`')
torch/distributed/run.py:393 in public function `get_args_parser`:
        D202: No blank lines allowed after function docstring (found 1)
torch/distributed/run.py:393 in public function `get_args_parser`:
        D401: First line should be in imperative mood; try rephrasing (found 'Helper')
torch/distributed/run.py:610 in public function `parse_args`:
        D103: Missing docstring in public function
torch/distributed/run.py:615 in public function `parse_min_max_nnodes`:
        D103: Missing docstring in public function
torch/distributed/run.py:629 in public function `determine_local_world_size`:
        D103: Missing docstring in public function
torch/distributed/run.py:670 in public function `get_rdzv_endpoint`:
        D103: Missing docstring in public function
torch/distributed/run.py:677 in public function `get_use_env`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/run.py:677 in public function `get_use_env`:
        D401: First line should be in imperative mood (perhaps 'Retrieve', not 'Retrieves')
torch/distributed/run.py:689 in public function `config_from_args`:
        D103: Missing docstring in public function
torch/distributed/run.py:770 in public function `run_script_path`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/run.py:770 in public function `run_script_path`:
        D401: First line should be in imperative mood (perhaps 'Run', not 'Runs')
torch/distributed/run.py:781 in public function `run`:
        D103: Missing docstring in public function
torch/distributed/run.py:804 in public function `main`:
        D103: Missing docstring in public function
15
torch/distributed/run.py:611 in public function `parse_args`:
        D103: Missing docstring in public function
torch/distributed/run.py:616 in public function `parse_min_max_nnodes`:
        D103: Missing docstring in public function
torch/distributed/run.py:630 in public function `determine_local_world_size`:
        D103: Missing docstring in public function
torch/distributed/run.py:671 in public function `get_rdzv_endpoint`:
        D103: Missing docstring in public function
torch/distributed/run.py:691 in public function `config_from_args`:
        D103: Missing docstring in public function
torch/distributed/run.py:784 in public function `run`:
        D103: Missing docstring in public function
torch/distributed/run.py:807 in public function `main`:
        D103: Missing docstring in public function
7

torch/distributed/__init__.py
torch/distributed/__init__.py:1 at module level:
        D104: Missing docstring in public package
torch/distributed/__init__.py:8 in public function `is_available`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/__init__.py:8 in public function `is_available`:
        D400: First line should end with a period (not ',')
torch/distributed/__init__.py:8 in public function `is_available`:
        D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
4
torch/distributed/__init__.py:1 at module level:
        D104: Missing docstring in public package
1

torch/distributed/utils.py:1 at module level:
        D100: Missing docstring in public module
torch/distributed/utils.py:16 in private function `_pack_kwargs`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/utils.py:16 in private function `_pack_kwargs`:
        D400: First line should end with a period (not ')')
torch/distributed/utils.py:47 in private function `_cast_forward_inputs`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/utils.py:88 in private function `_recursive_to`:
        D200: One-line docstring should fit on one line with quotes (found 3)
torch/distributed/utils.py:141 in private function `_p_assert`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/utils.py:141 in private function `_p_assert`:
        D209: Multi-line docstring closing quotes should be on a separate line
torch/distributed/utils.py:141 in private function `_p_assert`:
        D400: First line should end with a period (not 't')
torch/distributed/utils.py:141 in private function `_p_assert`:
        D401: First line should be in imperative mood; try rephrasing (found 'This')
torch/distributed/utils.py:275 in private function `_sync_module_states`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/utils.py:275 in private function `_sync_module_states`:
        D400: First line should end with a period (not 'n')
torch/distributed/utils.py:275 in private function `_sync_module_states`:
        D401: First line should be in imperative mood (perhaps 'Sync', not 'Syncs')
torch/distributed/utils.py:300 in private function `_sync_params_and_buffers`:
        D205: 1 blank line required between summary line and description (found 0)
torch/distributed/utils.py:300 in private function `_sync_params_and_buffers`:
        D400: First line should end with a period (not 'y')
torch/distributed/utils.py:300 in private function `_sync_params_and_buffers`:
        D401: First line should be in imperative mood (perhaps 'Synchronize', not 'Synchronizes')
15
torch/distributed/utils.py:1 at module level:
        D100: Missing docstring in public module
1
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/112953
Approved by: https://github.com/weifengpy
 2023-11-08 01:13:09 +00:00
Edward Z. Yang
793c62b79c Allow binary pointwise operations to cause refinement on unbacked SymInts (#112155) 
To do this, there is a little detour to remove hint caching for unbacked
SymInts; now, we just always attempt to update the hint (using
maybe_evaluate_static; this is much better than the replace we were
doing before) if we don't think we know it.

With this change, we now can generally infer that i0 == 1 is false for
a size-like unbacked SymInt.  So if we write the size match /
broadcasting test very carefully (see comment), we will eventually
end up expect_true(sizeA == sizeB), which is good enough to cause
refinement.  Phew!

I think I still want to setup a replacement if you do i0 == s0, but I'm
going to do that in a follow up.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112155
Approved by: https://github.com/aakhundov, https://github.com/voznesenskym
 2023-11-01 23:02:17 +00:00
Ying Zhang
bbdce93571 Basic fp8 support in Inductor (#109168) 
Add basic fp8 support in Inductor, including:
* Fix fp8 Triton codegen issues;
* Add min_elements_per_thread requirement for fp8 related dtype conversions. More details on Triton implementation can be found from 10f59d8ce0/lib/Conversion/TritonGPUToLLVM/ElementwiseOpToLLVM.cpp (L10).

Note that the current implementation only works for Pointwise. Will create follow-up PRs for Reduction.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/109168
Approved by: https://github.com/drisspg
 2023-09-23 04:41:41 +00:00
ydwu4
6140facf00 Support SymBool input to torch.compile (#107850) 
We could have SymBool inputs for torch.compile, e.g. in the following situation:
```
def f(x:torch.Tensor):
  pred = x.size(0) == 3
  torch.compile(f)(pred, x)

make_fx(f, tracing_mode="symbolic")(x)
```

The idea of this PR (credit to @ezyang) is to support SymBool by re-using the infra we've already had for SymInt so that we don't need to replicate a lot of stuff.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/107850
Approved by: https://github.com/ezyang
ghstack dependencies: #107662
 2023-09-14 21:34:31 +00:00
PyTorch MergeBot
47f79e9a2b Revert "Support SymBool input to torch.compile (#107850)" 
This reverts commit 9f6d70b2fd.

Reverted https://github.com/pytorch/pytorch/pull/107850 on behalf of https://github.com/huydhn due to Sorry for reverting this, but test_export_with_symbool_inputs is failing in trunk a08e1370ef ([comment](https://github.com/pytorch/pytorch/pull/107850#issuecomment-1718675877))
 2023-09-14 02:53:36 +00:00
ydwu4
9f6d70b2fd Support SymBool input to torch.compile (#107850) 
We could have SymBool inputs for torch.compile, e.g. in the following situation:
```
def f(x:torch.Tensor):
  pred = x.size(0) == 3
  torch.compile(f)(pred, x)

make_fx(f, tracing_mode="symbolic")(x)
```

The idea of this PR (credit to @ezyang) is to support SymBool by re-using the infra we've already had for SymInt so that we don't need to replicate a lot of stuff.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/107850
Approved by: https://github.com/ezyang
ghstack dependencies: #107662
 2023-09-14 01:16:29 +00:00
lezcano
2b6249e209 Wrap indirect indexing on CUDA (#105055) 
Lifting this to CPU should be rather easy. @jgong5
Partially fixes https://github.com/pytorch/pytorch/issues/97365. I'd wait to close that issue once this works on CPU as well.

This fix works with dynamic shapes as well.

@voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @ngimel @yf225 @chenyang78 @kadeng @muchulee8

Pull Request resolved: https://github.com/pytorch/pytorch/pull/105055
Approved by: https://github.com/peterbell10, https://github.com/jansel
 2023-08-23 11:59:20 +00:00
PyTorch MergeBot
b282787409 Revert "Wrap indirect indexing on CUDA (#105055)" 
This reverts commit 85c673e6b2.

Reverted https://github.com/pytorch/pytorch/pull/105055 on behalf of https://github.com/peterbell10 due to Causes failure in inductor_torchbench ([comment](https://github.com/pytorch/pytorch/pull/105055#issuecomment-1688871947))
 2023-08-22 20:24:41 +00:00
lezcano
f13101640f Quick return when there's nothing to bound in bound_sympy (#107549) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107549
Approved by: https://github.com/ezyang, https://github.com/eellison
ghstack dependencies: #105055
 2023-08-22 01:06:35 +00:00
lezcano
85c673e6b2 Wrap indirect indexing on CUDA (#105055) 
Lifting this to CPU should be rather easy. @jgong5
Partially fixes https://github.com/pytorch/pytorch/issues/97365. I'd wait to close that issue once this works on CPU as well.

This fix works with dynamic shapes as well.

@voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @ngimel @yf225 @chenyang78 @kadeng @muchulee8

Pull Request resolved: https://github.com/pytorch/pytorch/pull/105055
Approved by: https://github.com/peterbell10, https://github.com/jansel
 2023-08-22 01:06:35 +00:00
Sam Larsen
8f774330af [inductor] Use shape env bounds in inductor bounds.py (#106175) (#106568) 
Summary: If constrained ranges are available, use them in bounds.py before value range analysis (to enable Int64 -> Int32 optimization).

Test Plan: New unit test in test_torchinductor.py to mark a tensor as dynamic, then constrain with constrain_as_size (as outlined in https://github.com/pytorch/pytorch/issues/106175)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/106568
Approved by: https://github.com/eellison, https://github.com/lezcano
 2023-08-11 00:16:09 +00:00
David Berard
f160a972aa [inductor][easy] "unhandled ValueRange op" - log at debug level (#106215) 
Set this log line to debug level - it appears frequently for many ops that don't have implementations following https://github.com/pytorch/pytorch/pull/102611.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106215
Approved by: https://github.com/lezcano
 2023-07-29 03:40:40 +00:00
lezcano
34c91a7051 Prefer bound_sympy over sympy_interp (#105138) 
This is the first PR towards simplifying sympy_interp, and more
generally, simplifying the implementation of ValueRangeAnalysis for
SymPy expressions.

In general, it would be conteptually good to have a minimal subset of
operations that conform our SymPy expressions, let that be guards or
indexing expressions. This would allow us to reason better about SymPy
guards and potentially have invariants like knowing that guards are
continuous piecewise rational functions. If this were the case,
we could operate on them using exact arithmetic and completely avoid
precision errors like the one found in https://github.com/pytorch/pytorch/issues/105097
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105138
Approved by: https://github.com/ezyang
 2023-07-17 11:34:05 +00:00
lezcano
eae99b0f51 Bound just size variables in bound_sympy (#105155) 
We also bound them as starting on 2, because of 0,1 specialisation.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/105155
Approved by: https://github.com/ezyang
 2023-07-17 11:34:05 +00:00
lezcano
c099b7e07a ValueRange analysis for indirect indexing (#102611) 
We do so by forwarding ValueRange analysis from IR buffers to CSEvars

Pull Request resolved: https://github.com/pytorch/pytorch/pull/102611
Approved by: https://github.com/eellison, https://github.com/peterbell10
 2023-07-14 13:43:05 +00:00
lezcano
d1fedad080 Perform value range analysis with rationals when possible (#105137) 
This is particularly useful for guards to avoid rounding errors, as most
guards (all?) are rational functions.

Fixes https://github.com/pytorch/pytorch/issues/105097

Pull Request resolved: https://github.com/pytorch/pytorch/pull/105137
Approved by: https://github.com/ezyang
 2023-07-13 16:45:47 +00:00
lezcano
3d07184930 Move optimize indexing to use the class Bounds (#104558) 
This PR removes plenty of duplicated code. In particular, it removes the two repeated implementations of `get_expr_range`, which are superseded by the more correct `bound_sympy`.

The two duplicated `get_expr_range`s were a result of an oversight in https://github.com/pytorch/pytorch/pull/100549.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/104558
Approved by: https://github.com/eellison
 2023-07-07 23:52:14 +00:00
lezcano
710abc41cc Implement bound_sympy (#104559) 
The analysis for SymPy expressions was incorrect as, even though it said
that the assumption was "smoothness" the assumption was, in fact, that he
formula was monotone in every variable. In other words, it was
assuming that the derivative does not change signs in any variable (!!).

We implement a function that, given bounds on the values of the free
symbols of a sympy expression, it gives a bound on a the expression
itself.

We reshuffle a few things in value_ranges.py to create a
`SymPyValueRangeAnalysis` class, but we do not change any code really.
The only relevant change in that file is the addition of the
`sympy_bound`s function. We do this because we don't want to inadvertently
use any fallbacks in this case.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/104559
Approved by: https://github.com/eellison
 2023-07-07 23:52:14 +00:00
lezcano
ff05f81e1d Simplify and extend ValueRanges (#104557) 
This PR:
- It adds a few boolean variants of some methods that were missing
- It simplifies the implementation of plenty of the operations
- Adds ModularIndexing to the SymPy interpreter

Pull Request resolved: https://github.com/pytorch/pytorch/pull/104557
Approved by: https://github.com/eellison
 2023-07-07 23:52:13 +00:00
PyTorch MergeBot
4de1ee6ba4 Revert "Value range refinement using multi-variate expressions. (#97964)" 
This reverts commit 2642412207.

Reverted https://github.com/pytorch/pytorch/pull/97964 on behalf of https://github.com/huydhn due to Sorry for reverting your PR, but it is breaking an internal test ([comment](https://github.com/pytorch/pytorch/pull/97964#issuecomment-1615194524))
 2023-06-30 21:08:05 +00:00
Yukio Siraichi
2642412207 Value range refinement using multi-variate expressions. (#97964) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97964
Approved by: https://github.com/ezyang
 2023-06-30 01:32:22 +00:00
lezcano
19a57870a3 Fix a number of issues with divs in ValueRangeAnalysis (#100547) 
This PR:
- Adds `floordiv` and `truncdiv` as they were missing
- Maps `div` to its correct definition (it was being mapped to `floordiv`)
- Simplifies the bounds of `floordiv`
- Fixes some issues with the returned types of `floor` `ceil`
- Adds tests for the previous point

Pull Request resolved: https://github.com/pytorch/pytorch/pull/100547
Approved by: https://github.com/ezyang
 2023-05-04 12:31:55 +00:00
Angela Yi
1d077f28ed [export] Constraints API (#98433) 
Wrapper for users to insert constraints into model code.

The constraints will not be maintained in the graph after tracing through make_fx so retracing with dynamo/make_fx will not work. This will be supported after torch._assert supported is implemented. Then we can convert the constrain_range calls to torch._asserts.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/98433
Approved by: https://github.com/avikchaudhuri, https://github.com/tugsbayasgalan
 2023-04-13 21:20:10 +00:00
PyTorch MergeBot
ab761605ae Revert "[export] Constraints API (#98433)" 
This reverts commit 1510eb4072.

Reverted https://github.com/pytorch/pytorch/pull/98433 on behalf of https://github.com/izaitsevfb due to Breaks internal tests, asked by author to revert
 2023-04-12 23:37:19 +00:00
Angela Yi
1510eb4072 [export] Constraints API (#98433) 
Wrapper for users to insert constraints into model code.

The constraints will not be maintained in the graph after tracing through make_fx so retracing with dynamo/make_fx will not work. This will be supported after torch._assert supported is implemented. Then we can convert the constrain_range calls to torch._asserts.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/98433
Approved by: https://github.com/avikchaudhuri, https://github.com/tugsbayasgalan
 2023-04-12 01:32:44 +00:00
Edward Z. Yang
16ec7efa49 Don't use f-strings in logging calls (1/X) (#98591) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98591
Approved by: https://github.com/albanD
 2023-04-07 15:52:50 +00:00
Edward Z. Yang
8372c5dc68 Refactor dynamic dims api, stateless internals, higher level export API (#96699) 
The purpose of this API is to execute a few large components of work:

1) Refactor all the internals of plumbing dynamic dimension information after dynamo to be stateless
2) Decouple allocation controls around dynamic dimensions from verification
3) For (2), for allocation, create an enum that dictates whether we are in DUCK (default today), STATIC (aka assume_static_default in the past), or DYNAMIC (aka user constrained, do not duck shape)
4) For (2), for verification, we separate out the list of dynamic ranges entirely from allocation. This means shape_env does not tracking for what we verify on, and instead, it is the callers job to invoke produce_guards() with the various things they want verified, specifically, with the valid ranges. We do use constrain ranges to refine value ranges when doing analysis.
5) We have decided, therefore, as an extension of (4) to double down on "late" checks versus "eager" checks, primarily because the mechanisms for gathering what actually matters happens during guards, and should be a purview of the caller seeking guards, not the shape env. However, for dynamo, these structures are essentially one and the same.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96699
Approved by: https://github.com/avikchaudhuri, https://github.com/ezyang
 2023-03-29 16:55:49 +00:00
Edward Z. Yang
c86d23a1ef Allow point-ranges on floating point inf (#95799) 
Fixes https://github.com/pytorch/pytorch/issues/95797

Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95799
Approved by: https://github.com/eellison
 2023-03-02 08:14:11 +00:00
Edward Z. Yang
7ca623c2e1 Fix convit_base (#95174) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95174
Approved by: https://github.com/ngimel, https://github.com/jansel, https://github.com/atalman
 2023-02-21 14:07:59 +00:00
Nicolas Macchioni
83b5eb4e16 [sympy] fix ValueRanges.pow error when b.lower is float (#95151) 
Summary:
fix `TypeError: 'Float' object cannot be interpreted as an integer` for `ValueRanges.pow(a, b)` when `not a.is_singleton() and b.is_singleton() and not isinstance(b.lower, int)`

this is breaking  `cuda11.7-py3.10-gcc7-sm86 / test (inductor_timm, 1, 2, linux.g5.4xlarge.nvidia.gpu)`
{F878635541}

Test Plan: sandcastle + CI

Differential Revision: D43430385

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95151
Approved by: https://github.com/Skylion007
 2023-02-20 22:55:24 +00:00
Nicolas Macchioni
50ec4ddb70 fix 'sympy.core.logic' has no attribute 'boolalg' (#95130) 
Summary: fix module error by directly importing `sympy.logic.boolalg.Boolean`

Test Plan: CI

Differential Revision: D43423823

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95130
Approved by: https://github.com/Skylion007
 2023-02-20 00:09:57 +00:00
Edward Z. Yang
2f9ffe7b0a Add torch.utils._sympy.interp (#94985) 
This utility allows us to conveniently abstract interpret Sympy expressions with respect to some alternative domain. I am particularly interested in using ValueRanges to do range analysis on expressions (not this PR).

Some minor house-keeping:
* ReferenceAnalysis got moved to its own file, sprouted a constant() implementation, and some uses of math.* got converted to sympy.*
* ValueRangeAnalysis now understands mod
* Test file gets moved from `test_value_ranges.py` to `test_sympy_utils.py`

Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94985
Approved by: https://github.com/eellison
 2023-02-17 14:28:18 +00:00
Edward Z. Yang
ccef485221 Add boolean/comparison operator support to ValueRanges (#94944) 
Pretty straightforward.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94944
Approved by: https://github.com/lezcano
 2023-02-17 14:28:18 +00:00
Edward Z. Yang
08ef83f07c Add exhaustive testing to ValueRanges, fix bugs (#94939) 
Since I didn't want to deal with nondeterministic tests, I went the exhaustive testing route for a fixed list of constants to look at. The tests generate random ranges, propagate the range through the function, and then pick elements in the range and check that the result on the operation is in the resulting range. This caught bugs in log, sqrt and pow.

My resolution for pow was a little special, because I had trouble figuring out the correct semantics under all inputs domains. Instead, I picked two input domains (pow on two point ranges, and pow where exponent is known) and only implemented those. Everything else we give up. I think this is unlikely to affect perf.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/94939
Approved by: https://github.com/lezcano, https://github.com/eellison, https://github.com/nunoplopes
 2023-02-17 14:28:15 +00:00
Edward Z. Yang
12c9a932ca Assert more invariants on ValueRanges (#94906) 
The main new invariant is lower/upper must be a Sympy expression of some sort (filtered through `simple_sympify`). There are some simpler sanity checks (mostly making sure the range is well formed). There is a type confusion problem (it's not immediately obvious if a range is for float/int/bool) but we aren't going to solve this for now as it is more complicated.

Billing of changes:

* ValueRanges.wrap() now accepts sympy expressions
* ValueRanges now accepts non-sympy expressions and will sympyify them appropriately. Rewrite calls to ValueRanges to not sympify manually as it is unnecessary
* Don't attempt to test sqrt(-1)
* Add ValuesRanges.unknown() which gives -oo, oo bounds, and rewrite direct calls to -math.inf, math.inf to use it
* Make multiply work between ValueRanges.unknown() and ValueRanges.wrap(0)
* Consistently use sympy.oo instead of math.inf

Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94906
Approved by: https://github.com/eellison
 2023-02-17 14:28:11 +00:00
Edward Z. Yang
89e16c4f18 Assume sympy is always installed (#94903) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94903
Approved by: https://github.com/Skylion007, https://github.com/malfet
 2023-02-16 14:09:58 +00:00
Huy Do
371f587c92 Dockerize lint jobs (#94255) 
This is to minimize network flakiness when running lint jobs.  I create a new Docker image for linter and install all linter dependencies there.  After that, all linter jobs are converted to use Nova generic Linux job https://github.com/pytorch/test-infra/blob/main/.github/workflows/linux_job.yml with the new image.

For the future task: I encounter this issue with the current mypy version we are using and Python 3.11 https://github.com/python/mypy/issues/13627.  Fixing this requires upgrading mypy to a newer version, but that can be done separately (require formatting/fixing `*.py` files with the newer mypy version)

`collect_env` linter job is currently not included here as it needs older Python versions (3.5).  It could also be converted to use the same mechanism (with another Docker image, probably).  This one rarely fails though.

### Testing

BEFORE
https://github.com/pytorch/pytorch/actions/runs/4130366955 took a total of ~14m

AFTER
https://github.com/pytorch/pytorch/actions/runs/4130712385 also takes a total of ~14m
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94255
Approved by: https://github.com/ZainRizvi
 2023-02-11 21:56:19 +00:00
Edward Z. Yang
50bc25baa0 Move ValueRanges into its own module (#94528) 
I am going to use it in ShapeEnv shortly.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/94528
Approved by: https://github.com/eellison
 2023-02-11 02:54:49 +00:00