[CPU] upgrade dependent torch ver to PT2.12 (#21456)
Co-authored-by: Ma Mingfei <mingfei.ma@intel.com>
This commit is contained in:
@@ -126,7 +126,7 @@ jobs:
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timeout-minutes: 5
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run: |
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docker exec -w /sglang-checkout/ ci_sglang_${{ matrix.runner }} \
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bash -c "source /opt/.venv/bin/activate && python3 -c 'import torch; import sgl_kernel; assert torch._C._cpu._is_amx_tile_supported(); assert hasattr(torch.ops.sgl_kernel, \"convert_weight_packed\"); '"
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bash -c "source /opt/.venv/bin/activate && python3 -c 'import torch; import sgl_kernel; assert torch.cpu._is_amx_tile_supported(); assert hasattr(torch.ops.sgl_kernel, \"convert_weight_packed\"); '"
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- name: Run unit tests
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timeout-minutes: 120
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@@ -56,14 +56,14 @@ dependencies = [
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"tabulate",
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"tiktoken",
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"timm==1.0.16",
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"torch==2.9.0",
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"torchao==0.14.1",
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"torchaudio==2.9.0",
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"torchvision==0.24.0",
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"torch==2.12.0",
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"torchao==0.17.0",
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"torchaudio==2.11.0",
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"torchvision==0.27.0",
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"tqdm",
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"mistral_common>=1.11.0",
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"transformers==5.8.1",
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"triton==3.5.0",
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"triton==3.7.0",
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"uvicorn",
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"uvloop",
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"xgrammar==0.2.1",
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@@ -330,9 +330,9 @@ except:
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is_intel_amx_backend_available = False
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try:
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# move torch._C._cpu._is_amx_tile_supported() from cpu_has_amx_support
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# move torch.cpu._is_amx_tile_supported() from cpu_has_amx_support
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# to support torch compile
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is_amx_tile_supported = torch._C._cpu._is_amx_tile_supported()
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is_amx_tile_supported = torch.cpu._is_amx_tile_supported()
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except:
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is_amx_tile_supported = False
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@@ -305,9 +305,9 @@ except:
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is_intel_amx_backend_available = False
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try:
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# move torch._C._cpu._is_amx_tile_supported() from cpu_has_amx_support
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# move torch.cpu._is_amx_tile_supported() from cpu_has_amx_support
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# to support torch compile
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is_amx_tile_supported = torch._C._cpu._is_amx_tile_supported()
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is_amx_tile_supported = torch.cpu._is_amx_tile_supported()
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except:
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is_amx_tile_supported = False
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@@ -1,7 +1,7 @@
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[build-system]
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requires = [
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"scikit-build-core>=0.10",
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"torch==2.9.0",
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"torch==2.12.0",
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"wheel",
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]
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build-backend = "scikit_build_core.build"
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@@ -9,7 +9,7 @@ build-backend = "scikit_build_core.build"
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[project]
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name = "sglang-kernel-cpu"
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version = "0.4.3"
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description = "Kernel Library for SGLang"
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description = "CPU Kernel Library for SGLang"
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readme = "README.md"
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requires-python = ">=3.10"
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license = { file = "LICENSE" }
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@@ -8,7 +8,7 @@ from sglang.srt.layers.amx_utils import CPUQuantMethod
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kernel = torch.ops.sgl_kernel
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torch.manual_seed(1234)
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torch.manual_seed(1183)
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from utils import (
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BLOCK_K,
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@@ -177,7 +177,7 @@ class TestROPE(CustomTestCase):
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num_kv_heads: int,
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):
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set_global_server_args_for_scheduler(ServerArgs(model_path="dummy"))
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torch.manual_seed(100)
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torch.manual_seed(1234)
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rope_ref = RotaryEmbedding(
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head_size,
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rotary_dim,
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@@ -15,13 +15,11 @@ from sglang.test.test_utils import CustomTestCase
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register_cpu_ci(est_time=10, suite="base-b-test-cpu")
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torch.manual_seed(1234)
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# This is used by the Deepseek-V2 model
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class TestGroupedTopK(CustomTestCase):
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def _run_single_test(self, M, E, G, topk, topk_group, renormalize, dtype):
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torch.manual_seed(1234)
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torch.manual_seed(12)
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# expand gating_output by M, otherwise bfloat16 fall into same value aftering truncating
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hidden_states = torch.randn(M, 100, dtype=dtype)
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@@ -9,7 +9,7 @@ from sglang.srt.layers.amx_utils import CPUQuantMethod
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kernel = torch.ops.sgl_kernel
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torch.manual_seed(128)
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torch.manual_seed(1183)
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from utils import (
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BLOCK_K,
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@@ -174,7 +174,7 @@ class TestROPE(CustomTestCase):
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num_kv_heads: int,
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):
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set_global_server_args_for_scheduler(ServerArgs(model_path="dummy"))
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torch.manual_seed(100)
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torch.manual_seed(1234)
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rope_ref = RotaryEmbedding(
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head_size,
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rotary_dim,
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@@ -10,13 +10,11 @@ from sglang.srt.layers.moe.topk import grouped_topk_gpu as native_grouped_topk
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from sglang.srt.models.llama4 import Llama4MoE
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from sglang.test.test_utils import CustomTestCase
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torch.manual_seed(1234)
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# This is used by the Deepseek-V2 model
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class TestGroupedTopK(CustomTestCase):
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def _run_single_test(self, M, E, G, topk, topk_group, renormalize, dtype):
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torch.manual_seed(1234)
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torch.manual_seed(12)
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# expand gating_output by M, otherwise bfloat16 fall into same value aftering truncating
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hidden_states = torch.randn(M, 100, dtype=dtype)
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