Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

fix issue#3269: unwrap tensor shape without opt val #3279

Merged
merged 2 commits into from
Nov 5, 2024
Merged

fix issue#3269: unwrap tensor shape without opt val #3279

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
584ca4a
add the optional opt val flag
lanluo-nvidia Nov 4, 2024
c283ad4
resolve comments
lanluo-nvidia Nov 5, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 2 additions & 1 deletion py/torch_tensorrt/dynamo/partitioning/common.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,8 @@ def construct_dynamic_input(
if isinstance(dim, torch.SymInt):
min_max_opt = extract_var_range_info(dim)
min_shape.append(min_max_opt["min"])
opt_shape.append(min_max_opt["opt"])
# opt might not exist
opt_shape.append(min_max_opt.get("opt"))
max_shape.append(min_max_opt["max"])
else:
min_shape.append(dim)
Expand Down
7 changes: 4 additions & 3 deletions py/torch_tensorrt/dynamo/utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union

import numpy as np
import sympy
import tensorrt as trt
import torch
from torch._subclasses.fake_tensor import FakeTensor
Expand Down Expand Up @@ -342,14 +343,14 @@ def extract_var_range_info(symbolic_integer: torch.SymInt) -> Dict[str, int]:
shape_env.var_to_val
)
assert var_range, var_val
min_val, max_val, opt_val = int(var_range.lower), int(var_range.upper), int(var_val)
min_val, max_val = int(var_range.lower), int(var_range.upper)
# Torchdynamo 0/1 specialization outlier
min_val = 1 if min_val == 2 else min_val
min_max_opt = {}
min_max_opt["min"] = min_val
min_max_opt["max"] = max_val
min_max_opt["opt"] = opt_val

if isinstance(var_val, sympy.core.numbers.Integer):
min_max_opt["opt"] = int(var_val)
return min_max_opt


Expand Down
Loading