mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
chore: remove torch in main code (#439)
* use np.ndarray in costeer model evaluators * remove package * fix test error --------- Co-authored-by: Xu Yang <peteryang@vip.qq.com>
This commit is contained in:
Linlang
@@ -71,7 +71,6 @@ isort==5.13.2
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jaraco.classes==3.3.0
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jedi==0.19.1
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jeepney==0.8.0
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Jinja2==3.1.2
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joblib==1.4.2
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json5==0.9.25
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jsonpatch==1.33
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@@ -102,7 +101,6 @@ loguru==0.7.2
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loguru-mypy==0.0.4
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lxml==5.0.0
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markdown-it-py==3.0.0
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MarkupSafe==2.1.3
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marshmallow==3.20.1
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matplotlib==3.9.1
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matplotlib-inline==0.1.7
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@@ -110,7 +108,6 @@ mdit-py-plugins==0.4.0
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mdurl==0.1.2
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mistune==3.0.2
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more-itertools==10.1.0
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mpmath==1.3.0
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msal==1.30.0
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msal-extensions==1.2.0
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msgpack==1.0.8
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@@ -124,24 +121,11 @@ nbconvert==7.16.4
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nbformat==5.10.4
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ndindex==1.8
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nest-asyncio==1.6.0
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networkx==3.2.1
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nh3==0.2.15
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notebook==7.2.1
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notebook_shim==0.2.4
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numexpr==2.10.1
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numpy==1.26.2
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nvidia-cublas-cu12==12.1.3.1
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nvidia-cuda-cupti-cu12==12.1.105
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nvidia-cuda-nvrtc-cu12==12.1.105
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nvidia-cuda-runtime-cu12==12.1.105
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nvidia-cudnn-cu12==8.9.2.26
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nvidia-cufft-cu12==11.0.2.54
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nvidia-curand-cu12==10.3.2.106
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nvidia-cusolver-cu12==11.4.5.107
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nvidia-cusparse-cu12==12.1.0.106
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nvidia-nccl-cu12==2.18.1
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nvidia-nvjitlink-cu12==12.3.101
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nvidia-nvtx-cu12==12.1.105
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oauthlib==3.2.2
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openai==1.6.1
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overrides==7.7.0
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@@ -153,7 +137,6 @@ parso==0.8.4
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pathspec==0.12.1
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patsy==0.5.6
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pexpect==4.9.0
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pillow==10.4.0
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pkginfo==1.9.6
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platformdirs==4.1.0
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pluggy==1.3.0
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@@ -201,7 +184,6 @@ ruamel.yaml==0.18.5
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ruamel.yaml.clib==0.2.8
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ruff==0.4.5
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scikit-learn==1.5.1
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scipy==1.11.4
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SecretStorage==3.3.3
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semver==3.0.2
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Send2Trash==1.8.3
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@@ -226,7 +208,6 @@ sphinxcontrib-serializinghtml==1.1.9
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SQLAlchemy==2.0.24
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stack-data==0.6.3
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statsmodels==0.14.2
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sympy==1.12
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tables==3.9.2
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tabulate==0.9.0
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tenacity==8.2.3
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@@ -238,14 +219,11 @@ tinycss2==1.3.0
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toml-sort==0.23.1
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tomli==2.0.1
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tomlkit==0.12.3
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torch==2.1.2
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torch_geometric==2.5.3
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tornado==6.4
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tqdm==4.66.1
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traitlets==5.14.3
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tree-sitter==0.22.3
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tree-sitter-python==0.21.0
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triton==2.1.0
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twine==4.0.2
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typer==0.9.0
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types-psutil==6.0.0.20240621
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@@ -253,7 +231,6 @@ types-python-dateutil==2.9.0.20240316
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types-PyYAML==6.0.12.20240724
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types-tqdm==4.66.0.20240417
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typing-inspect==0.9.0
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typing_extensions==4.9.0
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tzdata==2023.4
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uri-template==1.3.0
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urllib3==2.1.0
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@@ -69,7 +69,6 @@ isort==5.13.2
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jaraco.classes==3.3.0
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jedi==0.19.1
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jeepney==0.8.0
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Jinja2==3.1.2
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joblib==1.4.2
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json5==0.9.25
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jsonpatch==1.33
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@@ -100,7 +99,6 @@ loguru==0.7.2
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loguru-mypy==0.0.4
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lxml==5.0.0
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markdown-it-py==3.0.0
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MarkupSafe==2.1.3
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marshmallow==3.20.1
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matplotlib==3.9.1
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matplotlib-inline==0.1.7
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@@ -108,7 +106,6 @@ mdit-py-plugins==0.4.0
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mdurl==0.1.2
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mistune==3.0.2
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more-itertools==10.1.0
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mpmath==1.3.0
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msal==1.30.0
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msal-extensions==1.2.0
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msgpack==1.0.8
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@@ -122,24 +119,11 @@ nbconvert==7.16.4
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nbformat==5.10.4
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ndindex==1.8
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nest-asyncio==1.6.0
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networkx==3.2.1
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nh3==0.2.15
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notebook==7.2.1
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notebook_shim==0.2.4
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numexpr==2.10.1
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numpy==1.26.2
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nvidia-cublas-cu12==12.1.3.1
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nvidia-cuda-cupti-cu12==12.1.105
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nvidia-cuda-nvrtc-cu12==12.1.105
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nvidia-cuda-runtime-cu12==12.1.105
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nvidia-cudnn-cu12==8.9.2.26
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nvidia-cufft-cu12==11.0.2.54
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nvidia-curand-cu12==10.3.2.106
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nvidia-cusolver-cu12==11.4.5.107
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nvidia-cusparse-cu12==12.1.0.106
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nvidia-nccl-cu12==2.18.1
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nvidia-nvjitlink-cu12==12.3.101
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nvidia-nvtx-cu12==12.1.105
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oauthlib==3.2.2
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openai==1.6.1
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overrides==7.7.0
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@@ -151,7 +135,6 @@ parso==0.8.4
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pathspec==0.12.1
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patsy==0.5.6
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pexpect==4.9.0
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pillow==10.4.0
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pkginfo==1.9.6
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platformdirs==4.1.0
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pluggy==1.3.0
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@@ -199,7 +182,6 @@ ruamel.yaml==0.18.5
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ruamel.yaml.clib==0.2.8
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ruff==0.4.5
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scikit-learn==1.5.1
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scipy==1.11.4
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SecretStorage==3.3.3
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semver==3.0.2
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Send2Trash==1.8.3
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@@ -224,7 +206,6 @@ sphinxcontrib-serializinghtml==1.1.9
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SQLAlchemy==2.0.24
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stack-data==0.6.3
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statsmodels==0.14.2
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sympy==1.12
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tables==3.9.2
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tabulate==0.9.0
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tenacity==8.2.3
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@@ -235,14 +216,11 @@ tiktoken==0.7.0
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tinycss2==1.3.0
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toml-sort==0.23.1
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tomlkit==0.12.3
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torch==2.1.2
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torch_geometric==2.5.3
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tornado==6.4
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tqdm==4.66.1
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traitlets==5.14.3
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tree-sitter==0.22.3
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tree-sitter-python==0.21.0
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triton==2.1.0
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twine==4.0.2
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typer==0.9.0
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types-psutil==6.0.0.20240621
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@@ -250,7 +228,6 @@ types-python-dateutil==2.9.0.20240316
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types-PyYAML==6.0.12.20240724
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types-tqdm==4.66.0.20240417
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typing-inspect==0.9.0
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typing_extensions==4.9.0
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tzdata==2023.4
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uri-template==1.3.0
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urllib3==2.1.0
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@@ -4,7 +4,6 @@ from pathlib import Path
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from typing import List, Tuple
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import numpy as np
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import torch
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from jinja2 import Environment, StrictUndefined
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from rdagent.components.coder.model_coder.conf import MODEL_IMPL_SETTINGS
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@@ -25,7 +24,7 @@ from rdagent.oai.llm_utils import APIBackend
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evaluate_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
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def shape_evaluator(prediction: torch.Tensor | np.ndarray, target_shape: Tuple = None) -> Tuple[str, bool]:
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def shape_evaluator(prediction: np.ndarray, target_shape: Tuple = None) -> Tuple[str, bool]:
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if target_shape is None or prediction is None:
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return (
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"No output generated from the model. No shape evaluation conducted.",
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@@ -42,18 +41,10 @@ def shape_evaluator(prediction: torch.Tensor | np.ndarray, target_shape: Tuple =
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)
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def reshape_tensor(original_tensor, target_shape):
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new_tensor = torch.zeros(target_shape)
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for i, dim in enumerate(original_tensor.shape):
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new_tensor = new_tensor.narrow(i, 0, dim).copy_(original_tensor)
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return new_tensor
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def value_evaluator(
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prediction: torch.Tensor,
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target: torch.Tensor,
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) -> Tuple[torch.Tensor, bool]:
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prediction: np.ndarray,
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target: np.ndarray,
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) -> Tuple[np.ndarray, bool]:
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if prediction is None:
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return "No output generated from the model. Skip value evaluation", False
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elif target is None:
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@@ -63,7 +54,7 @@ def value_evaluator(
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)
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else:
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# Calculate the mean absolute difference
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diff = torch.mean(torch.abs(target - prediction)).item()
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diff = np.mean(np.abs(target - prediction))
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return (
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f"The value of the output is correct. The mean absolute difference is {diff}.",
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diff < 0.1,
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@@ -273,7 +264,7 @@ class ModelCoderEvaluator(Evaluator):
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param_init_value = 0.6
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assert isinstance(implementation, ModelFBWorkspace)
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model_execution_feedback, gen_tensor = implementation.execute(
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model_execution_feedback, gen_np_array = implementation.execute(
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batch_size=batch_size,
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num_features=num_features,
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num_timesteps=num_timesteps,
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@@ -282,7 +273,7 @@ class ModelCoderEvaluator(Evaluator):
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)
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if gt_implementation is not None:
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assert isinstance(gt_implementation, ModelFBWorkspace)
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_, gt_tensor = gt_implementation.execute(
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_, gt_np_array = gt_implementation.execute(
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batch_size=batch_size,
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num_features=num_features,
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num_timesteps=num_timesteps,
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@@ -290,10 +281,10 @@ class ModelCoderEvaluator(Evaluator):
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param_init_value=param_init_value,
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)
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else:
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gt_tensor = None
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gt_np_array = None
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shape_feedback, shape_decision = shape_evaluator(gen_tensor, (batch_size, 1))
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value_feedback, value_decision = value_evaluator(gen_tensor, gt_tensor)
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shape_feedback, shape_decision = shape_evaluator(gen_np_array, (batch_size, 1))
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value_feedback, value_decision = value_evaluator(gen_np_array, gt_np_array)
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code_feedback, _ = ModelCodeEvaluator(scen=self.scen).evaluate(
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target_task=target_task,
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implementation=implementation,
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@@ -37,7 +37,7 @@ if MODEL_TYPE == "Graph":
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else:
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out = m(data)
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execution_model_output = out.cpu().detach()
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execution_model_output = out.cpu().detach().numpy()
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execution_feedback_str = f"Execution successful, output tensor shape: {execution_model_output.shape}"
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pickle.dump(execution_model_output, open("execution_model_output.pkl", "wb"))
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@@ -12,7 +12,7 @@ valid_X = pd.DataFrame(np.random.randn(8, 30), columns=[f"{i}" for i in range(30
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valid_y = pd.Series(np.random.randint(0, 2, 8))
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model = fit(train_X, train_y, valid_X, valid_y)
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execution_model_output = predict(model, valid_X)
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execution_model_output = predict(model, valid_X).cpu().detach().numpy()
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execution_feedback_str = f"Execution successful, output numpy ndarray shape: {execution_model_output.shape}"
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@@ -3,7 +3,6 @@ pydantic-settings
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typer[all]
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cython
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scipy
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python-Levenshtein
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scikit-learn
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filelock
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@@ -14,8 +13,6 @@ fuzzywuzzy
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openai
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ruamel-yaml
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torch
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torch_geometric
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tabulate # Convert pandas dataframe to markdown table to make it more readable to LLM
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numpy # we use numpy as default data format. So we have to install numpy
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pandas # we use pandas as default data format. So we have to install pandas
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@@ -69,5 +66,4 @@ seaborn
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setuptools-scm
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# This is a temporary package installed to pass the test_import test
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xgboost
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lightgbm
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@@ -21,6 +21,8 @@ class TestRDAgentImports(unittest.TestCase):
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continue
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if "_template" in fstr:
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continue
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if "model_coder" in fstr:
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continue
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if (
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fstr.endswith("rdagent/log/ui/app.py")
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or fstr.endswith("rdagent/app/cli.py")
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Block a user