Changes to TRT-LLM download tool for multigpu distributed case #3830
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There are some changes that do not conform to Python style guidelines:
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/utils.py 2025-09-22 06:35:28.523784+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/utils.py 2025-09-22 06:36:00.657186+00:00
@@ -863,6 +863,6 @@
return False
def is_thor() -> bool:
if torch.cuda.get_device_capability() in [(11, 0)]:
- return True
\ No newline at end of file
+ return True
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There are some changes that do not conform to Python style guidelines:
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/distributed/utils.py 2025-09-25 19:33:28.176615+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/distributed/utils.py 2025-09-25 19:34:02.325958+00:00
@@ -100,11 +100,10 @@
return True
except Exception as e:
logger.warning(f"Failed to detect CUDA version: {e}")
return False
-
return True
def _cache_root() -> Path:
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github-actions
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github-actions
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There are some changes that do not conform to Python style guidelines:
--- /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/distributed/test_nccl_ops.py 2025-10-13 18:42:31.890493+00:00
+++ /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/distributed/test_nccl_ops.py 2025-10-13 18:43:14.026641+00:00
@@ -23,10 +23,11 @@
if not dist.is_initialized():
dist.init_process_group(
backend="nccl",
init_method="env://",
)
+
class DistributedGatherModel(nn.Module):
def __init__(self, input_dim, world_size, group_name):
super().__init__()
self.fc = nn.Linear(input_dim, input_dim)
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narendasan
reviewed
Oct 17, 2025
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| def check_tensor_parallel_device_number(world_size: int) -> None: | ||
| if world_size % 2 != 0: | ||
| raise ValueError( |
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Why would it matter what the examples need? This is supposed to be user facing code.
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| def initialize_logger(rank: int, logger_file_name: str) -> logging.Logger: | ||
| logger = logging.getLogger() |
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Where is this logger used? Typically this is a user's responsibility. we should not be creating the actual logger handlers in our library. We can do this in the examples code tho
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ok was meant for the use case that if we want separate log outputs for the two different GPUs. But I agree these all can be moved to the user facing code
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…T-LLM wheel by using lock file
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TRT-LLM installation tool for distributed