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unpin nightly builds (#1888)
* Update [ghstack-poisoned] * Update [ghstack-poisoned]
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.github/workflows/float8_test.yml

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include:
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- name: SM-89
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runs-on: linux.g6.4xlarge.experimental.nvidia.gpu
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torch-spec: '--pre torch==2.7.0.dev20250122 --index-url https://download.pytorch.org/whl/nightly/cu124'
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torch-spec: '--pre torch --index-url https://download.pytorch.org/whl/nightly/cu124'
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gpu-arch-type: "cuda"
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gpu-arch-version: "12.4"
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- name: H100

.github/workflows/nightly_smoke_test.yml

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include:
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- name: CUDA Nightly
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runs-on: linux.g5.12xlarge.nvidia.gpu
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torch-spec: '--pre torch==2.7.0.dev20250122 --index-url https://download.pytorch.org/whl/nightly/cu124'
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torch-spec: '--pre torch --index-url https://download.pytorch.org/whl/nightly/cu124'
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gpu-arch-type: "cuda"
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gpu-arch-version: "12.4"
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.github/workflows/regression_test.yml

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include:
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- name: CUDA Nightly
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runs-on: linux.g5.12xlarge.nvidia.gpu
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torch-spec: '--pre torch==2.7.0.dev20250122 --index-url https://download.pytorch.org/whl/nightly/cu124'
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torch-spec: '--pre torch --index-url https://download.pytorch.org/whl/nightly/cu124'
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gpu-arch-type: "cuda"
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gpu-arch-version: "12.4"
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- name: CPU Nightly
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runs-on: linux.4xlarge
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torch-spec: '--pre torch==2.7.0.dev20250122 --index-url https://download.pytorch.org/whl/nightly/cpu'
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torch-spec: '--pre torch --index-url https://download.pytorch.org/whl/nightly/cpu'
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gpu-arch-type: "cpu"
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gpu-arch-version: ""
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.github/workflows/regression_test_rocm.yml

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include:
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- name: ROCM Nightly
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runs-on: linux.rocm.gpu.mi300.2
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torch-spec: '--pre torch==2.7.0.dev20250122 --index-url https://download.pytorch.org/whl/nightly/rocm6.3'
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torch-spec: '--pre torch --index-url https://download.pytorch.org/whl/nightly/rocm6.3'
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gpu-arch-type: "rocm"
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gpu-arch-version: "6.3"
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test/prototype/test_smoothquant.py

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@pytest.mark.parametrize("quant_mode", quant_mode_list)
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@pytest.mark.parametrize("device", devices)
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@pytest.mark.parametrize("idtype", idtypes)
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@pytest.mark.skip("this test is broken on recent PyTorch, TODO(#1639): fix it")
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def test_compute(bias, alpha, quant_mode, device, idtype):
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class Linear(torch.nn.Module):
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def __init__(self, bias: bool):
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@pytest.mark.parametrize("quant_mode", quant_mode_list)
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@pytest.mark.parametrize("device", devices)
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@pytest.mark.parametrize("idtype", idtypes)
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@pytest.mark.skip("this test is broken on recent PyTorch, TODO(#1639): fix it")
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def test_save_load_recipe(alpha, quant_mode, device, idtype):
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dataset_size = 20
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l1, l2, l3 = 512, 256, 128

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