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Merge branch 'main' into user/fanrongl/multi_stream_for_dsa_norm

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Fanrong Li 2026-01-07 11:33:18 +08:00 committed by GitHub
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6
security_scanning/examples/apps/poetry.lock generated
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6
security_scanning/examples/auto_deploy/poetry.lock generated
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22
security_scanning/examples/draft_target_model/poetry.lock generated
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22
security_scanning/examples/eagle/poetry.lock generated
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6
security_scanning/examples/llm-eval/lm-eval-harness/poetry.lock generated
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22
security_scanning/examples/lookahead/poetry.lock generated
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22
security_scanning/examples/medusa/poetry.lock generated
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22
security_scanning/examples/models/contrib/baichuan/poetry.lock generated
View File

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22
security_scanning/examples/models/contrib/bloom/poetry.lock generated
View File

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22
security_scanning/examples/models/contrib/chatglm-6b/poetry.lock generated
View File

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22
security_scanning/examples/models/contrib/chatglm2-6b/poetry.lock generated
View File

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22
security_scanning/examples/models/contrib/chatglm3-6b-32k/poetry.lock generated
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22
security_scanning/examples/models/contrib/dbrx/poetry.lock generated
View File

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22
security_scanning/examples/models/contrib/deepseek_v1/poetry.lock generated
View File

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22
security_scanning/examples/models/contrib/deepseek_v2/poetry.lock generated
View File

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22
security_scanning/examples/models/contrib/gptj/poetry.lock generated
View File

@ -181,13 +181,13 @@ typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
[[package]]
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@ -1713,13 +1713,13 @@ telegram = ["requests"]
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22
security_scanning/examples/models/contrib/gptneox/poetry.lock generated
View File

@ -181,13 +181,13 @@ typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
[[package]]
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@ -771,13 +771,13 @@ zstd = ["zstandard (>=0.18.0)"]
[[package]]
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@ -1713,13 +1713,13 @@ telegram = ["requests"]
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[package.dependencies]

28
security_scanning/examples/models/contrib/grok/poetry.lock generated
View File

@ -181,13 +181,13 @@ typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
[[package]]
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[[package]]
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@ -1630,13 +1630,13 @@ nvidia-nvjitlink-cu12 = "*"
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[package.dependencies]

30
security_scanning/examples/models/contrib/hyperclovax/poetry.lock generated
View File

@ -2,13 +2,13 @@
[[package]]
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[package.dependencies]
@ -290,13 +290,13 @@ zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "huggingface-hub"
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version = "1.2.4"
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[package.dependencies]
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[[package]]
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2
security_scanning/examples/models/contrib/hyperclovax/pyproject.toml
View File

@ -8,7 +8,7 @@ readme = "README.md"
[tool.poetry.dependencies]
python = ">=3.10,<3.13"
decord = "^0.6.0"
timm = "^1.0.23"
timm = "^1.0.24"
av = "^16.0.1"

22
security_scanning/examples/models/contrib/internlm/poetry.lock generated
View File

@ -181,13 +181,13 @@ typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
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22
security_scanning/examples/models/contrib/jais/poetry.lock generated
View File

@ -181,13 +181,13 @@ typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
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6
security_scanning/examples/models/contrib/mmdit/poetry.lock generated
View File

@ -2,13 +2,13 @@
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22
security_scanning/examples/models/contrib/mpt/poetry.lock generated
View File

@ -181,13 +181,13 @@ typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
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@ -1713,13 +1713,13 @@ telegram = ["requests"]
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22
security_scanning/examples/models/contrib/opt/poetry.lock generated
View File

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22
security_scanning/examples/models/contrib/skywork/poetry.lock generated
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22
security_scanning/examples/models/contrib/smaug/poetry.lock generated
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22
security_scanning/examples/models/core/commandr/poetry.lock generated
View File

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22
security_scanning/examples/models/core/gemma/poetry.lock generated
View File

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22
security_scanning/examples/models/core/glm-4-9b/poetry.lock generated
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22
security_scanning/examples/models/core/gpt/poetry.lock generated
View File

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22
security_scanning/examples/models/core/nemotron/poetry.lock generated
View File

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22
security_scanning/examples/models/core/phi/poetry.lock generated
View File

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[package.dependencies]

12
security_scanning/examples/models/core/qwen/poetry.lock generated
View File

@ -231,13 +231,13 @@ files = [
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[package.dependencies]

22
security_scanning/examples/models/core/qwenvl/poetry.lock generated
View File

@ -181,13 +181,13 @@ typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
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[package.dependencies]

22
security_scanning/examples/ngram/poetry.lock generated
View File

@ -181,13 +181,13 @@ typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
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@ -1781,13 +1781,13 @@ telegram = ["requests"]
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22
security_scanning/examples/quantization/poetry.lock generated
View File

@ -181,13 +181,13 @@ typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
[[package]]
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[package.dependencies]

28
security_scanning/examples/ray_orchestrator/poetry.lock generated
View File

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files = [
{file = "google_auth-2.46.0-py3-none-any.whl", hash = "sha256:fa51659c3745cb7024dd073f4ab766222767ea5f7dee2472110eaa03c9dbd2cb"},
{file = "google_auth-2.46.0.tar.gz", hash = "sha256:cb04c071a73394a6e3b9e48c1a7f48506001175b33e9679587a0f5320a21a34d"},
{file = "google_auth-2.47.0-py3-none-any.whl", hash = "sha256:c516d68336bfde7cf0da26aab674a36fedcf04b37ac4edd59c597178760c3498"},
{file = "google_auth-2.47.0.tar.gz", hash = "sha256:833229070a9dfee1a353ae9877dcd2dec069a8281a4e72e72f77d4a70ff945da"},
]
[package.dependencies]
cachetools = ">=2.0.0,<7.0"
pyasn1-modules = ">=0.2.1"
rsa = ">=3.1.4,<5"
[package.extras]
aiohttp = ["aiohttp (>=3.6.2,<4.0.0)", "requests (>=2.20.0,<3.0.0)"]
cryptography = ["cryptography (<39.0.0)", "cryptography (>=38.0.3)"]
cryptography = ["cryptography (>=38.0.3)"]
enterprise-cert = ["cryptography", "pyopenssl"]
pyjwt = ["cryptography (<39.0.0)", "cryptography (>=38.0.3)", "pyjwt (>=2.0)"]
pyopenssl = ["cryptography (<39.0.0)", "cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
pyjwt = ["cryptography (>=38.0.3)", "pyjwt (>=2.0)"]
pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
reauth = ["pyu2f (>=0.1.5)"]
requests = ["requests (>=2.20.0,<3.0.0)"]
testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "cryptography (<39.0.0)", "cryptography (<39.0.0)", "cryptography (>=38.0.3)", "cryptography (>=38.0.3)", "flask", "freezegun", "grpcio", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"]
testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "cryptography (>=38.0.3)", "cryptography (>=38.0.3)", "flask", "freezegun", "grpcio", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"]
urllib3 = ["packaging", "urllib3"]
[[package]]

22
security_scanning/examples/redrafter/poetry.lock generated
View File

@ -181,13 +181,13 @@ typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
[[package]]
name = "anyio"
version = "4.12.0"
version = "4.12.1"
description = "High-level concurrency and networking framework on top of asyncio or Trio"
optional = false
python-versions = ">=3.9"
files = [
{file = "anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb"},
{file = "anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0"},
{file = "anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c"},
{file = "anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703"},
]
[package.dependencies]
@ -771,13 +771,13 @@ zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "huggingface-hub"
version = "1.2.3"
version = "1.2.4"
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
optional = false
python-versions = ">=3.9.0"
files = [
{file = "huggingface_hub-1.2.3-py3-none-any.whl", hash = "sha256:c9b7a91a9eedaa2149cdc12bdd8f5a11780e10de1f1024718becf9e41e5a4642"},
{file = "huggingface_hub-1.2.3.tar.gz", hash = "sha256:4ba57f17004fd27bb176a6b7107df579865d4cde015112db59184c51f5602ba7"},
{file = "huggingface_hub-1.2.4-py3-none-any.whl", hash = "sha256:2db69b91877d9d34825f5cd2a63b94f259011a77dcf761b437bf510fbe9522e9"},
{file = "huggingface_hub-1.2.4.tar.gz", hash = "sha256:7a1d9ec4802e64372d1d152d69fb8e26d943f15a2289096fbc8e09e7b90c21a5"},
]
[package.dependencies]
@ -790,13 +790,13 @@ pyyaml = ">=5.1"
shellingham = "*"
tqdm = ">=4.42.1"
typer-slim = "*"
typing-extensions = ">=3.7.4.3"
typing-extensions = ">=4.1.0"
[package.extras]
all = ["Jinja2", "Pillow", "authlib (>=1.3.2)", "fastapi", "fastapi", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0)", "numpy", "pytest (>=8.4.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures (<16.0)", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "ty", "types-PyYAML", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
dev = ["Jinja2", "Pillow", "authlib (>=1.3.2)", "fastapi", "fastapi", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0)", "numpy", "pytest (>=8.4.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures (<16.0)", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "ty", "types-PyYAML", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
hf-xet = ["hf-xet (>=1.1.3,<2.0.0)"]
hf-xet = ["hf-xet (>=1.2.0,<2.0.0)"]
mcp = ["mcp (>=1.8.0)"]
oauth = ["authlib (>=1.3.2)", "fastapi", "httpx", "itsdangerous"]
quality = ["libcst (>=1.4.0)", "mypy (==1.15.0)", "ruff (>=0.9.0)", "ty"]
@ -1791,13 +1791,13 @@ telegram = ["requests"]
[[package]]
name = "typer-slim"
version = "0.21.0"
version = "0.21.1"
description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
optional = false
python-versions = ">=3.9"
files = [
{file = "typer_slim-0.21.0-py3-none-any.whl", hash = "sha256:92aee2188ac6fc2b2924bd75bb61a340b78bd8cd51fd9735533ce5a856812c8e"},
{file = "typer_slim-0.21.0.tar.gz", hash = "sha256:f2dbd150cfa0fead2242e21fa9f654dfc64773763ddf07c6be9a49ad34f79557"},
{file = "typer_slim-0.21.1-py3-none-any.whl", hash = "sha256:6e6c31047f171ac93cc5a973c9e617dbc5ab2bddc4d0a3135dc161b4e2020e0d"},
{file = "typer_slim-0.21.1.tar.gz", hash = "sha256:73495dd08c2d0940d611c5a8c04e91c2a0a98600cbd4ee19192255a233b6dbfd"},
]
[package.dependencies]

6
security_scanning/examples/trtllm-eval/poetry.lock generated
View File

@ -213,13 +213,13 @@ typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
[[package]]
name = "anyio"
version = "4.12.0"
version = "4.12.1"
description = "High-level concurrency and networking framework on top of asyncio or Trio"
optional = false
python-versions = ">=3.9"
files = [
{file = "anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb"},
{file = "anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0"},
{file = "anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c"},
{file = "anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703"},
]
[package.dependencies]

4
security_scanning/metadata.json
View File

@ -1,4 +1,4 @@
{
"commit_hash": "9cae7277ea9cc41242ede456ea1955902962f971",
"timestamp": "2026-01-06T02:39:48Z"
"commit_hash": "6095c80e560c612b8a8f4ff70e8930edf7798d21",
"timestamp": "2026-01-07T02:39:54Z"
}

12
security_scanning/poetry.lock generated
View File

@ -245,13 +245,13 @@ files = [
[[package]]
name = "anyio"
version = "4.12.0"
version = "4.12.1"
description = "High-level concurrency and networking framework on top of asyncio or Trio"
optional = false
python-versions = ">=3.9"
files = [
{file = "anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb"},
{file = "anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0"},
{file = "anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c"},
{file = "anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703"},
]
[package.dependencies]
@ -2834,13 +2834,13 @@ dill = ">=0.3.8"
[[package]]
name = "narwhals"
version = "2.14.0"
version = "2.15.0"
description = "Extremely lightweight compatibility layer between dataframe libraries"
optional = false
python-versions = ">=3.9"
files = [
{file = "narwhals-2.14.0-py3-none-any.whl", hash = "sha256:b56796c9a00179bd757d15282c540024e1d5c910b19b8c9944d836566c030acf"},
{file = "narwhals-2.14.0.tar.gz", hash = "sha256:98be155c3599db4d5c211e565c3190c398c87e7bf5b3cdb157dece67641946e0"},
{file = "narwhals-2.15.0-py3-none-any.whl", hash = "sha256:cbfe21ca19d260d9fd67f995ec75c44592d1f106933b03ddd375df7ac841f9d6"},
{file = "narwhals-2.15.0.tar.gz", hash = "sha256:a9585975b99d95084268445a1fdd881311fa26ef1caa18020d959d5b2ff9a965"},
]
[package.extras]

23
setup.py
View File

@ -173,7 +173,6 @@ def extract_from_precompiled(precompiled_location: str, package_data: List[str],
- Remote URL: Downloads and extracts from URL (wheel or tar.gz)
"""
import fnmatch
import glob
import shutil
import tarfile
import zipfile
@ -183,6 +182,7 @@ def extract_from_precompiled(precompiled_location: str, package_data: List[str],
# Handle local directory (assuming repo structure)
if os.path.isdir(precompiled_location):
precompiled_location = os.path.abspath(precompiled_location)
print(
f"Using local directory as precompiled source: {precompiled_location}"
)
@ -192,14 +192,13 @@ def extract_from_precompiled(precompiled_location: str, package_data: List[str],
f"Directory {precompiled_location} does not contain a tensorrt_llm folder."
)
for pattern in package_data:
full_pattern = os.path.join(source_tensorrt_llm, pattern)
for src_file in glob.glob(full_pattern, recursive=True):
if not os.path.isfile(src_file):
continue
# Walk through all files and match using fnmatch (consistent with wheel extraction)
for root, dirs, files in os.walk(source_tensorrt_llm):
for filename in files:
src_file = os.path.join(root, filename)
# Get path relative to precompiled_location (e.g., "tensorrt_llm/libs/libtensorrt_llm.so")
rel_path = os.path.relpath(src_file, precompiled_location)
dst_file = rel_path # e.g., "tensorrt_llm/libs/libtensorrt_llm.so"
dst_file = rel_path
# Skip yaml files
if dst_file.endswith(".yaml"):
@ -214,6 +213,14 @@ def extract_from_precompiled(precompiled_location: str, package_data: List[str],
if not any(dst_file.startswith(d) for d in allowed_dirs):
continue
# Check if file matches any pattern using fnmatch (same as wheel extraction)
for filename_pattern in package_data:
if fnmatch.fnmatchcase(rel_path,
f"tensorrt_llm/{filename_pattern}"):
break
else:
continue
dst_dir = os.path.dirname(dst_file)
if dst_dir:
os.makedirs(dst_dir, exist_ok=True)

37
tensorrt_llm/_torch/auto_deploy/export/library/meta_nonzero.py Normal file
View File

@ -0,0 +1,37 @@
"""Patch to enable torch.nonzero() on meta tensors during export.
This patch addresses an issue where torch.nonzero() raises NotImplementedError
when tracing models that use nonzero on meta device. The fix sets the config
flag to assume all elements are non-zero, which enables export to proceed.
"""
import torch.fx.experimental._config as fx_config
from ..interface import BaseExportPatch, ExportPatchRegistry
@ExportPatchRegistry.register("meta_nonzero")
class MetaNonzeroPatch(BaseExportPatch):
"""Patch to enable torch.nonzero() meta registration during export.
This patch sets torch.fx.experimental._config.meta_nonzero_assume_all_nonzero
to True, allowing torch.nonzero() to work on meta tensors during tracing.
The implementation assumes all elements are non-zero, which is acceptable
for tracing purposes where only shapes matter.
"""
def _apply_patch(self):
"""Apply the meta nonzero patch."""
# Store original config value
self.original_values["meta_nonzero_assume_all_nonzero"] = getattr(
fx_config, "meta_nonzero_assume_all_nonzero", False
)
# Enable nonzero on meta tensors
fx_config.meta_nonzero_assume_all_nonzero = True
def _revert_patch(self):
"""Revert the meta nonzero patch."""
fx_config.meta_nonzero_assume_all_nonzero = self.original_values[
"meta_nonzero_assume_all_nonzero"
]

4
tensorrt_llm/_torch/auto_deploy/models/patches/llama4.py
View File

@ -72,11 +72,11 @@ def _forward_with_cond(
inputs_embeds = self.get_input_embeddings()(input_ids)
def _vision_branch(inputs_embeds, pixel_values, input_ids):
# Updated to match transformers 4.57.1+ signature
# get_image_features now takes: (self, pixel_values, vision_feature_select_strategy, **kwargs)
image_features = self.get_image_features(
pixel_values=pixel_values,
vision_feature_layer=vision_feature_layer,
vision_feature_select_strategy=vision_feature_select_strategy,
image_sizes=None,
)
vision_flat = image_features.view(-1, image_features.size(-1))

33
tensorrt_llm/_torch/auto_deploy/models/patches/mixtral.py
View File

@ -7,19 +7,38 @@ from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
from ...export.interface import BaseExportPatch, ExportPatchRegistry
# Import SiLUActivation for compatibility check
try:
from transformers.activations import SiLUActivation
_SILU_TYPES = (nn.SiLU, SiLUActivation)
except ImportError:
_SILU_TYPES = (nn.SiLU,)
def _is_silu_activation(act_fn) -> bool:
"""Check if activation function is SiLU or equivalent."""
return isinstance(act_fn, _SILU_TYPES)
def _forward_moe(self: MixtralSparseMoeBlock, hidden_states: torch.Tensor):
# check if we can apply the patch
use_original_forward = False
if not all(isinstance(expert.act_fn, nn.SiLU) for expert in self.experts):
use_original_forward = True
unsupported_reasons = []
if not all(_is_silu_activation(expert.act_fn) for expert in self.experts):
unsupported_reasons.append("expert activation is not SiLU")
if any(getattr(mod, "bias", None) is not None for mod in self.experts.modules()):
use_original_forward = True
unsupported_reasons.append("expert modules have bias")
# rely on original forward instead
if use_original_forward:
return self._original_forward(hidden_states)
# Raise informative error for unsupported configurations
# (fallback to original forward is not export-compatible with transformers >= 4.57.1)
if unsupported_reasons:
raise NotImplementedError(
f"MixtralSparseMoeBlock forward patch does not support this model configuration: "
f"{', '.join(unsupported_reasons)}. "
f"The original transformers forward uses torch.nonzero() and tensor indexing "
f"which are not compatible with torch.export on meta tensors."
)
batch_size, sequence_length, hidden_dim = hidden_states.shape
if self.training and self.jitter_noise > 0:

33
tensorrt_llm/_torch/auto_deploy/models/patches/qwen3.py
View File

@ -7,19 +7,38 @@ from transformers.models.qwen3_moe.modeling_qwen3_moe import Qwen3MoeSparseMoeBl
from ...export.interface import BaseExportPatch, ExportPatchRegistry
# Import SiLUActivation for compatibility check
try:
from transformers.activations import SiLUActivation
_SILU_TYPES = (nn.SiLU, SiLUActivation)
except ImportError:
_SILU_TYPES = (nn.SiLU,)
def _is_silu_activation(act_fn) -> bool:
"""Check if activation function is SiLU or equivalent."""
return isinstance(act_fn, _SILU_TYPES)
def _forward_moe(self: Qwen3MoeSparseMoeBlock, hidden_states: torch.Tensor):
# check if we can apply the patch
use_original_forward = False
if not all(isinstance(expert.act_fn, nn.SiLU) for expert in self.experts):
use_original_forward = True
unsupported_reasons = []
if not all(_is_silu_activation(expert.act_fn) for expert in self.experts):
unsupported_reasons.append("expert activation is not SiLU")
if any(getattr(mod, "bias", None) is not None for mod in self.experts.modules()):
use_original_forward = True
unsupported_reasons.append("expert modules have bias")
# rely on original forward instead
if use_original_forward:
return self._original_forward(hidden_states)
# Raise informative error for unsupported configurations
# (fallback to original forward is not export-compatible with transformers >= 4.57.1)
if unsupported_reasons:
raise NotImplementedError(
f"Qwen3MoeSparseMoeBlock forward patch does not support this model configuration: "
f"{', '.join(unsupported_reasons)}. "
f"The original transformers forward uses torch.nonzero() and tensor indexing "
f"which are not compatible with torch.export on meta tensors."
)
batch_size, sequence_length, hidden_dim = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_dim)

16
tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py
View File

@ -1864,10 +1864,13 @@ if IS_CUTLASS_DSL_AVAILABLE:
mma_tiler_mn_candidates = [(self.tile_size, 128),
(self.tile_size, 256)]
cluster_shape_mn_candidates = [(self.tile_size // 128, 1)]
# TODO: Add raster_along_m=True if we find it more performant in some cases.
raster_along_m_candidates = [False]
valid_tactics = []
for mma_tiler_mn, cluster_shape_mn in itertools.product(
mma_tiler_mn_candidates, cluster_shape_mn_candidates):
for mma_tiler_mn, cluster_shape_mn, raster_along_m in itertools.product(
mma_tiler_mn_candidates, cluster_shape_mn_candidates,
raster_along_m_candidates):
if self.__class__.kernel_class.can_implement(
ab_dtype=cutlass.Float4E2M1FN,
sf_dtype=cutlass.Float8E4M3FN,
@ -1883,7 +1886,8 @@ if IS_CUTLASS_DSL_AVAILABLE:
b_major="k",
c_major="n",
):
valid_tactics.append((mma_tiler_mn, cluster_shape_mn))
valid_tactics.append(
(mma_tiler_mn, cluster_shape_mn, raster_along_m))
return valid_tactics
@ -2013,15 +2017,16 @@ if IS_CUTLASS_DSL_AVAILABLE:
stream = cuda.CUstream(torch_stream.cuda_stream)
if isinstance(tactic, tuple):
mma_tiler_mn, cluster_shape_mn = tactic
mma_tiler_mn, cluster_shape_mn, raster_along_m = tactic
else:
mma_tiler_mn = (self.tile_size, 128)
cluster_shape_mn = (self.tile_size // 128, 1)
raster_along_m = False
assert mma_tiler_mn[
0] == self.tile_size, f"Tactic ({tactic}) is incompatible with tile size ({self.tile_size})"
cache_key = (self.scaling_vector_size, self.tile_size, self.top_k,
mma_tiler_mn, cluster_shape_mn)
mma_tiler_mn, cluster_shape_mn, raster_along_m)
if cache_key not in self.__class__.kernel_cache:
gemm = self.__class__.kernel_class(
sf_vec_size=self.scaling_vector_size,
@ -2029,6 +2034,7 @@ if IS_CUTLASS_DSL_AVAILABLE:
cluster_shape_mn=cluster_shape_mn,
vectorized_f32=True,
topk=self.top_k,
raster_along_m=raster_along_m,
)
# Compute max active clusters on current device
hardware_info = cutlass.utils.HardwareInfo()

265
tensorrt_llm/_torch/cute_dsl_kernels/blackwell/blockscaled_contiguous_gather_grouped_gemm_swiglu_fusion.py
View File

@ -37,6 +37,7 @@ import cutlass.utils.blackwell_helpers as sm100_utils
import cutlass.utils.blockscaled_layout as blockscaled_utils
from cutlass._mlir.dialects import math
from cutlass.cute.nvgpu import cpasync, tcgen05
from cutlass.cutlass_dsl import Int32
from .custom_pipeline import PipelineCpAsyncUmma
from .utils import (
@ -154,6 +155,144 @@ CUDA Graph Support:
"""
# TODO: Remove this hook helper function after nvidia-cutlass-dsl 4.4 is released.
def hooked_PersistentTileSchedulerParams_init(
self,
problem_shape_ntile_mnl: cute.Shape,
cluster_shape_mnk: cute.Shape,
swizzle_size: int = 1,
raster_along_m: bool = True,
*,
loc=None,
ip=None,
):
if cluster_shape_mnk[2] != 1:
raise ValueError(f"unsupported cluster_shape_k {cluster_shape_mnk[2]}")
if swizzle_size < 1:
raise ValueError(f"expect swizzle_size >= 1, but get {swizzle_size}")
self.problem_shape_ntile_mnl = problem_shape_ntile_mnl
# cluster_shape_mnk is kept for reconstruction
self._cluster_shape_mnk = cluster_shape_mnk
self.cluster_shape_mn = cluster_shape_mnk[:2]
self.swizzle_size = swizzle_size
self._raster_along_m = raster_along_m
self._loc = loc
# Apply swizzle if swizzle_size > 1
if swizzle_size > 1:
problem_shape_ncluster_mnl = cute.round_up(
self.problem_layout_ncluster_mnl.shape,
(1, swizzle_size, 1) if raster_along_m else (swizzle_size, 1, 1),
)
if raster_along_m:
self.problem_layout_ncluster_mnl = cute.make_layout(
(
problem_shape_ncluster_mnl[0],
(swizzle_size, problem_shape_ncluster_mnl[1] // swizzle_size),
problem_shape_ncluster_mnl[2],
),
stride=(
swizzle_size,
(1, swizzle_size * problem_shape_ncluster_mnl[0]),
problem_shape_ncluster_mnl[0] * problem_shape_ncluster_mnl[1],
),
loc=loc,
ip=ip,
)
else:
self.problem_layout_ncluster_mnl = cute.make_layout(
(
(swizzle_size, problem_shape_ncluster_mnl[0] // swizzle_size),
problem_shape_ncluster_mnl[1],
problem_shape_ncluster_mnl[2],
),
stride=(
(1, swizzle_size * problem_shape_ncluster_mnl[1]),
swizzle_size,
problem_shape_ncluster_mnl[0] * problem_shape_ncluster_mnl[1],
),
loc=loc,
ip=ip,
)
# Create FastDivmod divisors (only when swizzle_size == 1 for correctness)
# FastDivmod assumes simple col-major/row-major layout, incompatible with swizzled layouts
if swizzle_size == 1:
problem_shape_ncluster_mnl = cute.ceil_div(
self.problem_shape_ntile_mnl, cluster_shape_mnk[:2], loc=loc, ip=ip
)
if raster_along_m:
self.problem_layout_ncluster_mnl = cute.make_layout(
problem_shape_ncluster_mnl,
stride=(
1,
problem_shape_ncluster_mnl[0],
problem_shape_ncluster_mnl[0] * problem_shape_ncluster_mnl[1],
),
loc=loc,
ip=ip,
)
else:
self.problem_layout_ncluster_mnl = cute.make_layout(
problem_shape_ncluster_mnl,
stride=(
problem_shape_ncluster_mnl[1],
1,
problem_shape_ncluster_mnl[0] * problem_shape_ncluster_mnl[1],
),
loc=loc,
ip=ip,
)
problem_layout_size = cute.size(self.problem_layout_ncluster_mnl, loc=loc, ip=ip)
cluster_count_m = self.problem_layout_ncluster_mnl.shape[0]
cluster_count_n = self.problem_layout_ncluster_mnl.shape[1]
# batch_fdd: Used to map linear_idx to work_unit_id (handles persistent scheduling)
self.batch_fdd = cute.fast_divmod_create_divisor(problem_layout_size, loc=loc, ip=ip)
# cluster_shape_m_fdd: Used to decode work_unit_id to cluster coordinates
self.cluster_shape_m_fdd = cute.fast_divmod_create_divisor(cluster_count_m, loc=loc, ip=ip)
# cluster_shape_n_fdd: Used for the second level decomposition
self.cluster_shape_n_fdd = cute.fast_divmod_create_divisor(cluster_count_n, loc=loc, ip=ip)
else:
# FastDivmod not applicable with swizzling, set to None
self.batch_fdd = None
self.cluster_shape_m_fdd = None
self.cluster_shape_n_fdd = None
def hooked_get_cluster_work_idx_with_fastdivmod(
self, current_work_linear_idx: Int32, *, loc=None, ip=None
) -> Tuple[Int32, Int32, Int32]:
work_iteration, work_unit_id = divmod(current_work_linear_idx, self.params.batch_fdd)
if self.params._raster_along_m:
# raster_along_m=True means column major (m is fastest)
# First, get cluster_m using cluster_shape_m_fdd
cluster_n_batch, cluster_m = divmod(work_unit_id, self.params.cluster_shape_m_fdd)
# Then decode cluster_n_batch to get cluster_n and batch_l using FastDivmod
batch_l, cluster_n = divmod(cluster_n_batch, self.params.cluster_shape_n_fdd)
else:
# raster_along_m=False means row major (n is fastest)
# First, get cluster_n using cluster_shape_n_fdd
cluster_m_batch, cluster_n = divmod(work_unit_id, self.params.cluster_shape_n_fdd)
# Then decode cluster_m_batch to get cluster_m and batch_l using FastDivmod
batch_l, cluster_m = divmod(cluster_m_batch, self.params.cluster_shape_m_fdd)
return (cluster_m, cluster_n, batch_l)
cutlass.utils.PersistentTileSchedulerParams.__init__ = hooked_PersistentTileSchedulerParams_init
cutlass.utils.StaticPersistentTileScheduler._get_cluster_work_idx_with_fastdivmod = (
hooked_get_cluster_work_idx_with_fastdivmod
)
class BlockScaledContiguousGatherGroupedGemmKernel:
"""This class implements contiguous grouped matrix multiplication with gather operation and SwiGLU fusion
for FC1 layer computation (C = up * silu(gate), where up/gate come from interleaved GEMM result).
@ -245,6 +384,7 @@ class BlockScaledContiguousGatherGroupedGemmKernel:
cluster_shape_mn: Tuple[int, int],
vectorized_f32: bool,
topk: cutlass.Int64,
raster_along_m: bool = False,
):
"""Initializes the configuration for a Blackwell blockscaled dense GEMM kernel with
gather operation and SwiGLU fusion.
@ -289,6 +429,7 @@ class BlockScaledContiguousGatherGroupedGemmKernel:
self.cluster_shape_mn = cluster_shape_mn
# K dimension is deferred in _setup_attributes
self.mma_tiler = (*mma_tiler_mn, 1)
self.raster_along_m = raster_along_m
self.cta_group = tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE
@ -743,7 +884,11 @@ class BlockScaledContiguousGatherGroupedGemmKernel:
# Compute grid size
self.tile_sched_params, grid = self._compute_grid(
c, self.cta_tile_shape_mnk_c, self.cluster_shape_mn, max_active_clusters
c,
self.cta_tile_shape_mnk_c,
self.cluster_shape_mn,
max_active_clusters,
self.raster_along_m,
)
self.buffer_align_bytes = 1024
@ -1254,34 +1399,69 @@ class BlockScaledContiguousGatherGroupedGemmKernel:
pipeline.PipelineUserType.Producer, self.num_tile_stage
)
while work_tile.is_valid_tile:
cur_tile_coord = work_tile.tile_idx
mma_tile_coord_m = cur_tile_coord[0] // cute.size(tiled_mma.thr_id.shape)
if mma_tile_coord_m < num_non_exiting_tiles[0]:
tile_info_pipeline.producer_acquire(tile_info_producer_state)
num_non_exiting_tiles_value = num_non_exiting_tiles[0]
if cutlass.const_expr(self.raster_along_m):
while work_tile.is_valid_tile:
cur_tile_coord = work_tile.tile_idx
expert_idx = tile_idx_to_expert_idx[mma_tile_coord_m]
mn_limit = tile_idx_to_mn_limit[mma_tile_coord_m]
with cute.arch.elect_one():
sInfo[(0, tile_info_producer_state.index)] = cur_tile_coord[0]
sInfo[(1, tile_info_producer_state.index)] = cur_tile_coord[1]
sInfo[(2, tile_info_producer_state.index)] = expert_idx
sInfo[(3, tile_info_producer_state.index)] = cutlass.Int32(
work_tile.is_valid_tile
mma_tile_coord_m = cur_tile_coord[0] // cute.size(tiled_mma.thr_id.shape)
if mma_tile_coord_m < num_non_exiting_tiles_value:
tile_info_pipeline.producer_acquire(tile_info_producer_state)
cur_tile_coord = work_tile.tile_idx
expert_idx = tile_idx_to_expert_idx[mma_tile_coord_m]
mn_limit = tile_idx_to_mn_limit[mma_tile_coord_m]
with cute.arch.elect_one():
sInfo[(0, tile_info_producer_state.index)] = cur_tile_coord[0]
sInfo[(1, tile_info_producer_state.index)] = cur_tile_coord[1]
sInfo[(2, tile_info_producer_state.index)] = expert_idx
sInfo[(3, tile_info_producer_state.index)] = cutlass.Int32(
work_tile.is_valid_tile
)
sInfo[(4, tile_info_producer_state.index)] = mn_limit
# fence view async shared
cute.arch.fence_proxy(
cute.arch.ProxyKind.async_shared,
space=cute.arch.SharedSpace.shared_cta,
)
sInfo[(4, tile_info_producer_state.index)] = mn_limit
# fence view async shared
cute.arch.fence_proxy(
cute.arch.ProxyKind.async_shared,
space=cute.arch.SharedSpace.shared_cta,
)
self.sched_sync_barrier.arrive_and_wait()
tile_info_pipeline.producer_commit(tile_info_producer_state)
tile_info_producer_state.advance()
self.sched_sync_barrier.arrive_and_wait()
tile_info_pipeline.producer_commit(tile_info_producer_state)
tile_info_producer_state.advance()
tile_sched.advance_to_next_work()
work_tile = tile_sched.get_current_work()
tile_sched.advance_to_next_work()
work_tile = tile_sched.get_current_work()
else:
is_continue = cutlass.Boolean(1)
while work_tile.is_valid_tile and is_continue:
cur_tile_coord = work_tile.tile_idx
mma_tile_coord_m = cur_tile_coord[0] // cute.size(tiled_mma.thr_id.shape)
if mma_tile_coord_m < num_non_exiting_tiles_value:
tile_info_pipeline.producer_acquire(tile_info_producer_state)
cur_tile_coord = work_tile.tile_idx
expert_idx = tile_idx_to_expert_idx[mma_tile_coord_m]
mn_limit = tile_idx_to_mn_limit[mma_tile_coord_m]
with cute.arch.elect_one():
sInfo[(0, tile_info_producer_state.index)] = cur_tile_coord[0]
sInfo[(1, tile_info_producer_state.index)] = cur_tile_coord[1]
sInfo[(2, tile_info_producer_state.index)] = expert_idx
sInfo[(3, tile_info_producer_state.index)] = cutlass.Int32(
work_tile.is_valid_tile
)
sInfo[(4, tile_info_producer_state.index)] = mn_limit
# fence view async shared
cute.arch.fence_proxy(
cute.arch.ProxyKind.async_shared,
space=cute.arch.SharedSpace.shared_cta,
)
self.sched_sync_barrier.arrive_and_wait()
tile_info_pipeline.producer_commit(tile_info_producer_state)
tile_info_producer_state.advance()
else:
is_continue = cutlass.Boolean(0)
tile_sched.advance_to_next_work()
work_tile = tile_sched.get_current_work()
tile_info_pipeline.producer_acquire(tile_info_producer_state)
with cute.arch.elect_one():
@ -2781,6 +2961,7 @@ class BlockScaledContiguousGatherGroupedGemmKernel:
cta_tile_shape_mnk: Tuple[int, int, int],
cluster_shape_mn: Tuple[int, int],
max_active_clusters: cutlass.Constexpr,
raster_along_m: bool = False,
) -> Tuple[utils.PersistentTileSchedulerParams, Tuple[int, int, int]]:
"""Use persistent tile scheduler to compute the grid size for the output tensor C.
@ -2803,7 +2984,9 @@ class BlockScaledContiguousGatherGroupedGemmKernel:
num_ctas_mnl = gc[(0, (None, None, None))].shape
cluster_shape_mnl = (*cluster_shape_mn, 1)
tile_sched_params = utils.PersistentTileSchedulerParams(num_ctas_mnl, cluster_shape_mnl)
tile_sched_params = utils.PersistentTileSchedulerParams(
num_ctas_mnl, cluster_shape_mnl, raster_along_m=raster_along_m
)
grid = utils.StaticPersistentTileScheduler.get_grid_shape(
tile_sched_params, max_active_clusters
)
@ -3209,3 +3392,33 @@ class BlockScaledContiguousGatherGroupedGemmKernel:
stream=stream,
epilogue_op=epilogue_op,
)
@cute.jit
def cvt_sf_MKL_to_M32x4xrm_K4xrk_L(
sf_ref_tensor: cute.Tensor,
sf_mma_tensor: cute.Tensor,
):
"""Convert scale factor tensor from MKL layout to mma specification M(32x4xrest_m)xK(4xrest_k)xL layout"""
# sf_mma_tensor has flatten shape (32, 4, rest_m, 4, rest_k, l)
# group to ((32, 4, rest_m), (4, rest_k), l)
sf_mma_tensor = cute.group_modes(sf_mma_tensor, 0, 3)
sf_mma_tensor = cute.group_modes(sf_mma_tensor, 1, 3)
for i in cutlass.range(cute.size(sf_ref_tensor)):
mkl_coord = sf_ref_tensor.layout.get_hier_coord(i)
sf_mma_tensor[mkl_coord] = sf_ref_tensor[mkl_coord]
@cute.jit
def cvt_sf_M32x4xrm_K4xrk_L_to_MKL(
sf_swizzled_tensor: cute.Tensor,
sf_unswizzled_tensor: cute.Tensor,
):
"""Convert scale factor tensor from mma specification M(32x4xrest_m)xK(4xrest_k)xL layout to MKL layout"""
# sf_swizzled_tensor has flatten shape (32, 4, rest_m, 4, rest_k, l)
# group to ((32, 4, rest_m), (4, rest_k), l)
sf_swizzled_tensor = cute.group_modes(sf_swizzled_tensor, 0, 3)
sf_swizzled_tensor = cute.group_modes(sf_swizzled_tensor, 1, 3)
for i in cutlass.range(cute.size(sf_unswizzled_tensor)):
mkl_coord = sf_unswizzled_tensor.layout.get_hier_coord(i)
sf_unswizzled_tensor[mkl_coord] = sf_swizzled_tensor[mkl_coord]

27
tensorrt_llm/commands/serve.py
View File

@ -18,7 +18,7 @@ from torch.cuda import device_count
from tensorrt_llm import LLM as PyTorchLLM
from tensorrt_llm import MultimodalEncoder
from tensorrt_llm._tensorrt_engine import LLM
from tensorrt_llm._utils import get_free_port, mpi_rank
from tensorrt_llm._utils import mpi_rank
from tensorrt_llm.executor.utils import LlmLauncherEnvs
from tensorrt_llm.inputs.multimodal import MultimodalServerConfig
from tensorrt_llm.llmapi import (BuildConfig, CapacitySchedulerPolicy,
@ -189,25 +189,12 @@ def launch_server(
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
# If disagg cluster config is provided and port is not specified, try to find a free port, otherwise try to bind to the specified port
assert port > 0 or disagg_cluster_config is not None, "Port must be specified if disagg cluster config is not provided"
if port > 0:
port_retries = 1
else:
port_retries = 100
port = get_free_port()
while port_retries > 0:
try:
s.bind((host, port))
break
except OSError as e:
port_retries -= 1
if port_retries == 0:
raise RuntimeError(
f"Failed to bind socket to {host}:{port}: {e}")
else:
logger.warning(
f"Failed to bind socket to {host}:{port}: {e}, retrying {port_retries}..."
)
port = get_free_port()
try:
s.bind((host, port))
if port == 0:
port = s.getsockname()[1]
except OSError as e:
raise RuntimeError(f"Failed to bind socket to {host}:{port}: {e}")
if backend == 'pytorch':
llm_args.pop("build_config", None)

3
tests/integration/defs/accuracy/references/gsm8k.yaml
View File

@ -106,6 +106,9 @@ deepseek-ai/DeepSeek-V3.2-Exp:
- quant_algo: NVFP4
spec_dec_algo: MTP
accuracy: 95.6
Qwen3/Qwen3-4B:
- spec_dec_algo: Eagle
accuracy: 85.823
Qwen3/Qwen3-8B:
- accuracy: 87.1114
- spec_dec_algo: Eagle

29
tests/integration/defs/accuracy/test_llm_api_pytorch.py
View File

@ -3327,6 +3327,35 @@ class TestQwen2_7BInstruct(LlmapiAccuracyTestHarness):
extra_evaluator_kwargs=self.EXTRA_EVALUATOR_KWARGS)
class TestQwen3_4B(LlmapiAccuracyTestHarness):
MODEL_NAME = "Qwen3/Qwen3-4B"
def test_eagle3(self):
"RCCA: https://nvbugspro.nvidia.com/bug/5698434"
pytorch_config = dict(
disable_overlap_scheduler=True,
cuda_graph_config=CudaGraphConfig(),
)
kv_cache_config = KvCacheConfig(
enable_block_reuse=False,
free_gpu_memory_fraction=0.6,
)
eagle_model_dir = f"{llm_models_root()}/Qwen3/Qwen3-4B_eagle3/"
target_model_dir = f"{llm_models_root()}/Qwen3/Qwen3-4B"
draft_len = 3
spec_config = EagleDecodingConfig(max_draft_len=draft_len,
speculative_model_dir=eagle_model_dir)
with LLM(model=target_model_dir,
**pytorch_config,
kv_cache_config=kv_cache_config,
speculative_config=spec_config) as llm:
task = GSM8K(self.MODEL_NAME)
task.evaluate(llm)
class TestQwen3_8B(LlmapiAccuracyTestHarness):
MODEL_NAME = "Qwen3/Qwen3-8B"

22
tests/integration/defs/common.py
View File

@ -15,6 +15,7 @@
import copy
import os
import platform
import random
import re
import socket
import tempfile
@ -1162,28 +1163,33 @@ def get_free_port_in_ci(max_attempts=100):
Get a free port in the range [CONTAINER_PORT_START, CONTAINER_PORT_START + CONTAINER_PORT_NUM - 1]
If CONTAINER_PORT_START and CONTAINER_PORT_NUM are not set or all ports are already in use, fallback to get_free_port
"""
global PORTS_IN_USE
container_port_start = int(os.environ.get("CONTAINER_PORT_START", -1))
container_port_num = int(os.environ.get("CONTAINER_PORT_NUM", -1))
if container_port_start != -1 and container_port_num != -1:
for i in range(container_port_num):
port = container_port_start + i
if port in PORTS_IN_USE:
continue
available_ports = [
port for port in range(container_port_start, container_port_start +
container_port_num)
if port not in PORTS_IN_USE
]
for _ in range(len(available_ports)):
# Get a random port from the available ports
port = random.choice(available_ports)
# Check if the port is free
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
try:
s.bind(("localhost", port))
# Port is free, add it to the set of used ports
PORTS_IN_USE.add(port)
return port
except OSError:
# Port is not free, try the next port
available_ports.remove(port)
continue
# No port found in the range, try to get a random free port from the system
for i in range(max_attempts):
for _ in range(max_attempts):
port = get_free_port()
if port not in PORTS_IN_USE:
PORTS_IN_USE.add(port)

1
tests/integration/test_lists/qa/llm_function_core.txt
View File

@ -525,6 +525,7 @@ accuracy/test_llm_api_pytorch.py::TestGLM4_6::test_nvfp4_multi_gpus[throughput]
accuracy/test_llm_api_pytorch.py::TestGLM4_6::test_nvfp4_multi_gpus[throughput_trtllm]
accuracy/test_llm_api_pytorch.py::TestGLM4_6::test_nvfp4_2_model_mtp[2model]
accuracy/test_llm_api_pytorch.py::TestGLM4_6::test_nvfp4_2_model_mtp[2model_trtllm]
accuracy/test_llm_api_pytorch.py::TestQwen3_4B::test_eagle3
accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_fp8_block_scales[latency]
accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_bf16[multi_gpus_no_cache]
accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_fp8_block_scales[latency-torch_compile=False]

1
tests/integration/test_lists/qa/llm_function_core_sanity.txt
View File

@ -229,6 +229,7 @@ accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_w4a8_mxfp4[mxfp8-laten
accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_fp8_block_scales[latency]
accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_w4a8_mxfp4[fp8-latency]
accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_w4a8_mxfp4[mxfp8-latency]
accuracy/test_llm_api_pytorch.py::TestQwen3_4B::test_eagle3
accuracy/test_llm_api_pytorch.py::TestSeedOss_36B::test_auto_dtype
accuracy/test_llm_api_pytorch_multimodal.py::TestQwen2_VL_7B::test_auto_dtype

6
tests/integration/test_lists/waives.txt
View File

@ -346,7 +346,6 @@ accuracy/test_llm_api_pytorch.py::TestNemotronNas::test_auto_dtype_tp8 SKIP (htt
unittest/_torch/auto_deploy/unit/multigpu/test_ad_build_small_multi.py::test_build_ad[meta-llama/Meta-Llama-3.1-8B-Instruct-llm_extra_args0-2] SKIP (https://nvbugs/5680755)
full:H100_PCIe/unittest/llmapi/test_llm_pytorch.py::test_llama_7b_multi_lora_evict_and_reload_lora_gpu_cache SKIP (https://nvbugs/5682551)
unittest/_torch/speculative/test_draft_len_schedule.py::test_correctness_across_batch_sizes[model_drafter-schedule1] SKIP (https://nvbugs/5680911)
unittest/_torch/speculative/test_eagle3.py::test_qwen3_eagle3[True-True-True-True] SKIP (https://nvbugspro.nvidia.com/bug/5749988)
accuracy/test_llm_api_pytorch.py::TestSeedOss_36B::test_auto_dtype SKIP (https://nvbugs/5612438)
accuracy/test_llm_api_autodeploy.py::TestNemotronH::test_auto_dtype[True] SKIP (https://nvbugs/5688721)
accuracy/test_llm_api_autodeploy.py::TestLlama3_1_8B::test_auto_dtype[False-2] SKIP (https://nvbugs/5769712)
@ -380,7 +379,6 @@ accuracy/test_llm_api_pytorch.py::TestNemotronUltra::test_fp8_prequantized[tp8-c
accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_chunked_prefill[cutlass-auto] SKIP (https://nvbugs/5596343)
unittest/_torch/speculative/test_spec_gate.py::test_spec_gate_e2e SKIP (https://nvbugs/5710045)
accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus[moe_backend=TRTLLM-mtp_nextn=0-ep4-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-torch_compile=False] SKIP (https://nvbugs/5721661)
unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_flashinfer_attention_op.py::test_flashinfer_attention_op_context_input_pos[cuda-dtype0-4-8-seq6] SKIP (https://nvbugs/5721907)
accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4[moe_backend=CUTLASS-mtp_nextn=0-fp8kv=True-attention_dp=True-cuda_graph=True-overlap_scheduler=True-torch_compile=False] SKIP (https://nvbugs/5722629)
accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_2gpus[cutlass-two_model-overlap_scheduler] SKIP (https://nvbugs/5702826)
accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_4gpus[cutlass-two_model-overlap_scheduler] SKIP (https://nvbugs/5702826)
@ -421,7 +419,6 @@ accuracy/test_cli_flow.py::TestPhi3_5MiniInstruct::test_auto_dtype SKIP (https:/
unittest/_torch/auto_deploy/unit/singlegpu/models/test_llama4_vlm_patch.py::test_build_run_llama4_vlm SKIP (https://nvbugs/5747878)
accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_4gpus_online_eplb[fp8kv=True-moe_backend=TRTLLM] SKIP (https://nvbugs/5740377)
cpp/test_multi_gpu.py::TestDisagg::test_symmetric_executor[gpt-2proc-mpi_kvcache-90] SKIP (https://nvbugs/5755941)
accuracy/test_disaggregated_serving.py::TestDeepSeekV3Lite::test_guided_decoding[xgrammar-mtp_nextn=0] SKIP (https://nvbugs/5748600)
examples/test_bert.py::test_llm_bert_general[compare_hf-enable_remove_input_padding-use_attention_plugin-enable_context_fmha-tp:1-pp:1-float16-BertForQuestionAnswering-bert/bert-base-cased-squad2] SKIP (https://nvbugs/5608979)
examples/test_bert.py::test_llm_bert_general[compare_hf-enable_remove_input_padding-use_attention_plugin-enable_context_fmha-tp:1-pp:1-float16-RobertaForQuestionAnswering-bert/roberta-base-squad2] SKIP (https://nvbugs/5608979)
examples/test_bert.py::test_llm_bert_general[compare_hf-disable_remove_input_padding-use_attention_plugin-disable_context_fmha-tp:2-pp:1-float16-BertForQuestionAnswering-bert/bert-base-cased-squad2] SKIP (https://nvbugs/5608979)
@ -516,3 +513,6 @@ disaggregated/test_auto_scaling.py::test_worker_restart[etcd-round_robin] SKIP (
accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_vswa_reuse_4gpus[one_model] SKIP (https://nvbugs/5756028)
accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_eagle3_vswa_reuse_4gpus[two_model] SKIP (https://nvbugs/5756028)
accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_fp8[latency-torch_compile=False] SKIP (https://nvbugs/5785206)
examples/test_gpt.py::test_llm_gpt2_parallel_embedding_2gpu[float16-0] SKIP (https://nvbugs/5784518)
accuracy/test_llm_api_pytorch.py::TestLlama3_2_1B::test_fp8_prequantized SKIP (https://nvbugs/5785465)
accuracy/test_llm_api_pytorch.py::TestMinistral8BInstruct::test_fp8 SKIP (https://nvbugs/5785485)

1305
tests/scripts/cute_dsl_kernels/run_blockscaled_contiguous_gather_grouped_gemm_swiglu_fusion.py Normal file
View File

File diff suppressed because it is too large Load Diff

11
tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_flashinfer_attention_op.py
View File

@ -574,6 +574,17 @@ def test_flashinfer_attention_op_context_input_pos(seq, batch_size, n_heads, dty
(MAX_BATCH_SIZE, MAX_SEQ_LEN, N_HEADS, D_HEAD), dtype=DTYPE, device=device
)
# Initialize the prefilled portion of the cache with random data
# This simulates a chunked prefill scenario where previous chunks have already
# populated the cache at positions 0:PREFILL_SEQ_LEN
if PREFILL_SEQ_LEN > 0:
k_cache[0:BATCH_SIZE, 0:PREFILL_SEQ_LEN, :, :] = torch.randn(
BATCH_SIZE, PREFILL_SEQ_LEN, N_HEADS, D_HEAD, dtype=DTYPE, device=device
)
v_cache[0:BATCH_SIZE, 0:PREFILL_SEQ_LEN, :, :] = torch.randn(
BATCH_SIZE, PREFILL_SEQ_LEN, N_HEADS, D_HEAD, dtype=DTYPE, device=device
)
# make sure planner is initialized
workspace = torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device=device)
_GlobalFlashInferPlanner.init_workspace(workspace)

5
tests/unittest/_torch/auto_deploy/unit/singlegpu/models/test_llama4_vlm_patch.py
View File

@ -1,4 +1,3 @@
import pytest
import torch
from _model_test_utils import get_small_model_config
from build_and_run_ad import ExperimentConfig
@ -10,10 +9,6 @@ from tensorrt_llm._torch.auto_deploy.utils._graph import move_to_device
def test_build_run_llama4_vlm():
pytest.skip(
"Skipping test_build_run_llm4_vlm because Llama4 is giving an error on upgrading transformers version to 4.57.1"
"https://nvbugspro.nvidia.com/bug/5732942"
)
atol = 1e-3
rtol = 1e-3

13
tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_build_small_single.py
View File

@ -201,19 +201,6 @@ def _check_ad_config(experiment_config: ExperimentConfig, llm_args: LlmArgs):
],
)
def test_build_ad(model_hub_id: str, llm_extra_args: dict):
if (
model_hub_id == "mistralai/Mixtral-8x7B-Instruct-v0.1"
and llm_extra_args.get("mode") != "transformers"
):
pytest.skip(
"Mixtral-8x7B-Instruct-v0.1 is giving an error on upgrading transformers version to 4.57.1"
"https://nvbugspro.nvidia.com/bug/5732942"
)
if model_hub_id == "Qwen/Qwen3-30B-A3B" and llm_extra_args.get("mode") != "transformers":
pytest.skip(
"Qwen3-30B-A3B is giving an error on upgrading transformers version to 4.57.1"
"https://nvbugspro.nvidia.com/bug/5732942"
)
experiment_config = get_small_model_config(model_hub_id, **llm_extra_args)
experiment_config["args"]["runtime"] = "demollm" # Default runtime set to demollm
experiment_config["args"]["world_size"] = 0 # Default world_size set to 0

112
tests/unittest/_torch/speculative/test_eagle3.py
View File

@ -780,117 +780,5 @@ def test_eagle3_cdl_sampling(disable_overlap_scheduler: bool):
llm_spec.shutdown()
@pytest.mark.parametrize(
"enable_block_reuse,use_one_model,enable_chunked_prefill,fp8_target", [
[True, True, True, True],
])
@pytest.mark.high_cuda_memory
def test_qwen3_eagle3(enable_block_reuse: bool, use_one_model: bool,
enable_chunked_prefill: bool, fp8_target: bool):
# Eagle3 one model works with overlap scheduler and block reuse.
total_mem_gb = torch.cuda.get_device_properties(0).total_memory / 1e9
if total_mem_gb < 35:
pytest.skip("Not enough memory to load target + draft model")
use_cuda_graph = True
attn_backend = "TRTLLM"
disable_overlap_scheduler = False
use_chain_drafter = True
multi_batch = False
attention_dp = False
models_path = llm_models_root()
eagle_model_dir = f"{models_path}/Zhi-Create-Qwen3-32B-Eagle3"
target_model_dir = f"{models_path}/Qwen3/Qwen3-32B"
if fp8_target:
target_model_dir = f"{models_path}/Qwen3/Qwen3-32B-FP8/"
# bs > 1 gives non-deterministic when doing IFB. There are slight chances
# that ref and spec does not match 100%
max_batch_size = 4 if multi_batch else 1
max_draft_len = 3
kv_cache_config = KvCacheConfig(enable_block_reuse=enable_block_reuse,
max_tokens=8192)
if fp8_target:
kv_cache_config.dtype = 'fp8'
cuda_graph_config = CudaGraphConfig(
batch_sizes=[i for i in range(1, max_batch_size +
1)]) if use_cuda_graph else None
llm_common_config = dict(
model=target_model_dir,
attn_backend=attn_backend,
disable_overlap_scheduler=disable_overlap_scheduler,
cuda_graph_config=cuda_graph_config,
max_batch_size=max_batch_size,
kv_cache_config=kv_cache_config,
enable_attention_dp=attention_dp,
max_seq_len=8192,
enable_chunked_prefill=enable_chunked_prefill,
)
if enable_chunked_prefill:
# Use a small max_num_tokens so that the chunked prefill path gets exercised.
llm_common_config['max_num_tokens'] = 64
spec_config = EagleDecodingConfig(
max_draft_len=max_draft_len,
speculative_model_dir=eagle_model_dir,
eagle3_one_model=use_one_model,
)
spec_config._allow_chain_drafter = use_chain_drafter
# Create the LLM instance
llm_spec = LLM(**llm_common_config, speculative_config=spec_config)
# Acceptance rate tests
if enable_chunked_prefill:
# Use a long prompt for chunked prefill tests.
prompts = [
"The capital of France is a city of romance, art, fashion, and cuisine. Paris is a must-visit destination for anyone who loves history, architecture, and culture. From the iconic Eiffel Tower to the world-famous Louvre Museum, Paris has something to offer for every interest and age.\nThe city is divided into 20 arrondissements, each with its own unique character and charm. The Latin Quarter is a popular area for students and young travelers, while the Champs-Élysées is a hub for shopping and dining. The Montmartre neighborhood is famous for its bohemian vibe and stunning views of the city.\nParis is also known for its beautiful parks and gardens, such as the Luxembourg Gardens and the Tuileries Garden. The city has a rich history, with landmarks like the Notre-Dame Cathedral and the Arc de Triomphe. Visitors can also explore the city's many museums, including the Musée d'Orsay and the Musée Rodin.\nIn addition to its cultural and historical attractions, Paris is also a great destination for foodies. The city is famous for its cuisine, including croissants, baguettes, and cheese. Visitors can sample the city's famous dishes at one of the many restaurants, cafes, and "
]
tok_ids = [llm_spec.tokenizer.encode(prompts[0])]
else:
prompts = [
"The capital of France is",
"The president of the United States is",
]
tok_ids = [llm_spec.tokenizer.encode("The future of AI is")]
if multi_batch:
tok_ids.append(llm_spec.tokenizer.encode(prompts))
sampling_params = SamplingParams(max_tokens=128, temperature=0)
for i in range(len(tok_ids)):
num_tokens = 0
num_drafted = 0
num_accepted = 0
for output in llm_spec.generate_async(tok_ids[i],
sampling_params,
streaming=True):
new_tokens = output.outputs[0].token_ids
num_drafted += max_draft_len
num_accepted += len(new_tokens) - num_tokens - 1
num_tokens = len(new_tokens)
accept_rate = num_accepted / num_drafted
assert accept_rate > 0.10
# Output tests
sampling_params = SamplingParams(max_tokens=10, temperature=0)
results_spec = llm_spec.generate(prompts, sampling_params)
generated_text_spec = [result.outputs[0].text for result in results_spec]
llm_spec.shutdown()
llm_ref = LLM(**llm_common_config)
results_ref = llm_ref.generate(prompts, sampling_params)
generated_text_ref = [result.outputs[0].text for result in results_ref]
llm_ref.shutdown()
for text_spec, text_ref in zip(generated_text_spec, generated_text_ref):
# The spec decode algorithm currently guarantees identical results
assert text_spec == text_ref
if __name__ == "__main__":
unittest.main()