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/bot run --add-multi-gpu-test |
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PR_Github #283 [ run ] triggered by Bot |
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/bot run --add-multi-gpu-test |
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PR_Github #387 [ run ] triggered by Bot |
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/bot run --add-multi-gpu-test |
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/bot run --add-multi-gpu-test |
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PR_Github #510 [ run ] triggered by Bot |
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I think I am pinged by mistake, is the review request actually pointed to @litaotju ? |
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@yuxianq Can we split this PR to several small PRs? For example, the first item can be a single PR.
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@QiJune I will have a try. Let me pass the CI first to validate that these features work correctly. |
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/bot run --add-multi-gpu-test |
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PR_Github #550 [ run ] triggered by Bot |
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/bot run --disable-fail-fast --add-multi-gpu-test |
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PR_Github #584 [ run ] triggered by Bot |
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PR_Github #584 [ run ] completed with state |
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Just curious, are someone importing this file?
It should be used a cli command only.
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No. I refactor it when I debug thread/process leak for multiprocessing module. It should be fine for a cli command.
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This looks somewaht ad-hoc to me. Is it possible to not relying on the model type hard code string here in a general interface?
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It is a workaround for DeepSeek V3 Lite since its rope_scaling is null in config.json. DeepSeek V3/R1 should be fine without this code. I have added a comment to explain it.
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Maybe Nit.
Can we say create*positions_for_yarn or something similar w/o to be too specific to DeepSeek easily?
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I have renamed it to create_sinusoidal_positions_yarn. DeepSeek use a extended version of yarn with additional mscale and mscale_all_dim arguments. Since it is compatible with the original version when using default value for mscale and mscale_all_dim, we can merge the Deepseek variant with the original version.
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you should be the custom op will do rope fusion right?
Maybe renaming to something like? self.rope_fused_in_custom_op = True
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I have renamed it to enable_rope_fusion.
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"support" is a vague word.
support means which one?
- configuable, both fused qkv and unfused qkv can run. or
- requires fused qkv?
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I use "support" because a backend can support both fused qkv and unfused qkv, so they are not mutually exclusive. Currently each backend only supports one case.
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when will this be true?
Can we always attach the extra_attrs in _load_model such that this won't be needed?
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Some tests use stub models and override _load_model (see https://github.com/NVIDIA/TensorRT-LLM/blob/7441714d66e179192ef0642a9a150f5486e5b06b/tests/unittest/_torch/test_pytorch_model_engine.py#L50). So we have to add this code to ensure the extra_attrs is attached. I have added comment to explain it.
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shall we wrap the whole forward function be inside this context manager?
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Good point, have wrapped the whole forward function.
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@Superjomn do you remember if we have some reason to make this "wait=False"?
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It can avoid thread leak, which can further avoid memory leak.
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will not this llm object be automatically destoryed when the function return? its just a local var.
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Yes, it is unnecessary. Just for debugging OOM issues. I have removed all unnecessary del llm.
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same here, is there some reason that the object not deleted by python when function returns?
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I have removed all unnecessary del llm.
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/bot run --disable-fail-fast --stage-list "A30-7" |
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/bot run --disable-fail-fast --stage-list "A30-7" |
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PR_Github #1005 [ run ] triggered by Bot |
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/bot run --disable-fail-fast --add-multi-gpu-test |
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/bot run --disable-fail-fast --add-multi-gpu-test |
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PR_Github #1088 [ run ] triggered by Bot |
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Signed-off-by: Yuxian Qiu <142763828+yuxianq@users.noreply.github.com>
Signed-off-by: Yuxian Qiu <142763828+yuxianq@users.noreply.github.com>
Signed-off-by: Yuxian Qiu <142763828+yuxianq@users.noreply.github.com>
Signed-off-by: Yuxian Qiu <142763828+yuxianq@users.noreply.github.com>
Signed-off-by: Yuxian Qiu <142763828+yuxianq@users.noreply.github.com>
Signed-off-by: Yuxian Qiu <142763828+yuxianq@users.noreply.github.com>
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/bot run --disable-fail-fast --add-multi-gpu-test |
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PR_Github #1110 [ run ] triggered by Bot |
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This PR has been split into several PRs. Move the main part of this PR to #3517 and close this outdated PR. |
This MR contains the following updates:
fuse_pos_embd=True/Falseand createRotaryEmbeddinginside attention module, so that the users don't need to handle it in the modeling files.cos_sinfor unfused rope implementation. If flashinfer is available, useapply_rope_with_cos_sin_cache_inplaceinstead ofapply_rope_inplace. Otherwise, we fallback to pure pytorch implementation, which can support any rope now.create_rope_const_paramsto create and cachecos_sin_cachefor all rope types, including Deepseek yarn rope.