[CPU] update torch 2.8 and fix missing fields in TorchSDPAMetadata (#25652)

Signed-off-by: jiang1.li <jiang1.li@intel.com>

.buildkite/scripts/hardware_ci/run-cpu-test.sh

@@ -58,11 +58,8 @@ function cpu_tests() {
     # pytest -x -v -s tests/kernels/attention/test_cache.py -m cpu_model
     # pytest -x -v -s tests/kernels/attention/test_mla_decode_cpu.py -m cpu_model
 
-    # Note: disable Bart until supports V1
-    pytest -x -v -s tests/models/language/generation -m cpu_model \
-                --ignore=tests/models/language/generation/test_bart.py
-    VLLM_CPU_SGL_KERNEL=1 pytest -x -v -s tests/models/language/generation -m cpu_model \
-                --ignore=tests/models/language/generation/test_bart.py
+    pytest -x -v -s tests/models/language/generation -m cpu_model
+    VLLM_CPU_SGL_KERNEL=1 pytest -x -v -s tests/models/language/generation -m cpu_model
 
     pytest -x -v -s tests/models/language/pooling -m cpu_model
     pytest -x -v -s tests/models/multimodal/generation \

docker/Dockerfile.cpu

@@ -114,9 +114,6 @@ WORKDIR /workspace/vllm
 RUN --mount=type=bind,src=requirements/test.in,target=requirements/test.in \
     cp requirements/test.in requirements/cpu-test.in && \
     sed -i '/mamba_ssm/d' requirements/cpu-test.in && \
-    sed -i 's/^torch==.*/torch==2.6.0/g' requirements/cpu-test.in && \
-    sed -i 's/torchaudio.*/torchaudio/g' requirements/cpu-test.in && \
-    sed -i 's/torchvision.*/torchvision/g' requirements/cpu-test.in && \
     uv pip compile requirements/cpu-test.in -o requirements/cpu-test.txt --index-strategy unsafe-best-match --torch-backend cpu
 
 RUN --mount=type=cache,target=/root/.cache/uv \

requirements/cpu-build.txt

@@ -1,12 +1,10 @@
-# Temporarily used for x86 CPU backend to avoid performance regression of torch>2.6.0+cpu,
-# see https://github.com/pytorch/pytorch/pull/151218
 cmake>=3.26.1
 ninja
 packaging>=24.2
 setuptools>=77.0.3,<80.0.0
 setuptools-scm>=8
 --extra-index-url https://download.pytorch.org/whl/cpu
-torch==2.6.0+cpu; platform_machine == "x86_64" # torch>2.6.0+cpu has performance regression on x86 platform, see https://github.com/pytorch/pytorch/pull/151218
+torch==2.8.0+cpu; platform_machine == "x86_64"
 torch==2.8.0; platform_machine == "ppc64le" or platform_machine == "aarch64" or platform_system == "Darwin"
 wheel
 jinja2>=3.1.6

requirements/cpu.txt

@@ -8,7 +8,7 @@ numba == 0.61.2; python_version > '3.9' and platform_machine != "s390x"
 packaging>=24.2
 setuptools>=77.0.3,<80.0.0
 --extra-index-url https://download.pytorch.org/whl/cpu
-torch==2.6.0+cpu; platform_machine == "x86_64" # torch>2.6.0+cpu has performance regression on x86 platform, see https://github.com/pytorch/pytorch/pull/151218
+torch==2.8.0+cpu; platform_machine == "x86_64"
 torch==2.8.0; platform_system == "Darwin"
 torch==2.8.0; platform_machine == "ppc64le" or platform_machine == "aarch64"
 
@@ -23,7 +23,7 @@ datasets # for benchmark scripts
 
 # Intel Extension for PyTorch, only for x86_64 CPUs
 intel-openmp==2024.2.1; platform_machine == "x86_64"
-intel_extension_for_pytorch==2.6.0; platform_machine == "x86_64" # torch>2.6.0+cpu has performance regression on x86 platform, see https://github.com/pytorch/pytorch/pull/151218
+intel_extension_for_pytorch==2.8.0; platform_machine == "x86_64"
 triton==3.2.0; platform_machine == "x86_64" # Triton is required for torch 2.6+cpu, as it is imported in torch.compile.
 
 # Use this to gather CPU info and optimize based on ARM Neoverse cores

vllm/v1/attention/backends/cpu_attn.py

@@ -85,6 +85,19 @@ class TorchSDPABackend(AttentionBackend):
 
 @dataclass
 class TorchSDPAMetadata(AttentionMetadata):
+    """Attention metadata for prefill and decode batched together."""
+    # Total number of prefill requests.
+    num_prefills: int
+    # Number of prefill tokens.
+    num_prefill_tokens: int
+    # Number of decode tokens. Note that it is equivalent to the number of
+    # decode requests.
+    num_decode_tokens: int
+    # (num_tokens,). The indices of the token slots that input tokens will be
+    # stored into. E.g., if `slot_mapping` is [35, 2, 17] and the block size
+    # is 16, the three tokens are stored in the 3rd slot in block 2, 2nd slot
+    # in block 0, and 1st slot in block 1, respectively.
+    slot_mapping: torch.Tensor
     """Metadata for PagedAttention."""
     # (batch_size,). The length of sequences (entire tokens seen so far) per
     # sequence.
@@ -420,7 +433,6 @@ class TorchSDPAMetadataBuilderV1(AttentionMetadataBuilder[TorchSDPAMetadata]):
                                                     num_prompt_req],  # prefill
             query_start_loc=query_start_loc_cpu[:num_reqs +
                                                 1],  # for logits index
-            enable_kv_scales_calculation=False,
         )
 
         return attn_metadata

vllm/v1/sample/ops/topk_topp_sampler.py

@@ -68,6 +68,8 @@ class TopKTopPSampler(nn.Module):
                     "native implementation of top-p & top-k sampling. For the "
                     "best performance, please install FlashInfer.")
                 self.forward = self.forward_native
+        elif current_platform.is_cpu():
+            self.forward = self.forward_cpu
         else:
             self.forward = self.forward_native
 
@@ -119,6 +121,45 @@ class TopKTopPSampler(nn.Module):
         # because of slicing operation in logits_processor.
         return flashinfer_sample(logits.contiguous(), k, p, generators), None
 
+    def forward_cpu(
+        self,
+        logits: torch.Tensor,
+        generators: dict[int, torch.Generator],
+        k: Optional[torch.Tensor],
+        p: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        """
+        PyTorch-native implementation of top-k and top-p sampling for CPU.
+
+        The logits tensor may be updated in-place.
+        """
+        logits = self.apply_top_k_top_p(logits, k, p)
+        logits_to_return = None
+        if self.logprobs_mode == "processed_logits":
+            logits_to_return = logits
+        elif self.logprobs_mode == "processed_logprobs":
+            logits_to_return = logits.log_softmax(dim=-1, dtype=torch.float32)
+
+        # Note: this is a workaround for
+        # https://github.com/pytorch/pytorch/pull/151218
+        @torch.compile(dynamic=True)
+        def compiled_random_sample(logits: torch.Tensor) -> torch.Tensor:
+            probs = logits.softmax(dim=-1, dtype=torch.float32)
+            q = torch.empty_like(probs)
+            q.exponential_()
+            return probs.div(q).argmax(dim=-1).view(-1)
+
+        if len(generators) != logits.shape[0]:
+            return compiled_random_sample(logits), logits_to_return
+        else:
+            probs = logits.softmax(dim=-1, dtype=torch.float32)
+            q = torch.empty_like(probs)
+            q.exponential_()
+            for i, generator in generators.items():
+                q[i].exponential_(generator=generator)
+
+            return probs.div_(q).argmax(dim=-1).view(-1), logits_to_return
+
 
 def apply_top_k_top_p(
     logits: torch.Tensor,

vllm/v1/worker/cpu_worker.py

@@ -8,18 +8,13 @@ import torch
 
 from vllm import envs
 from vllm.config import VllmConfig
-from vllm.distributed.parallel_state import get_pp_group, get_tp_group
 from vllm.logger import init_logger
 from vllm.model_executor.utils import set_random_seed
 from vllm.platforms import CpuArchEnum, current_platform
 from vllm.platforms.cpu import CpuPlatform, LogicalCPUInfo
-from vllm.sequence import IntermediateTensors
-from vllm.v1.core.sched.output import SchedulerOutput
-from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.worker.cpu_model_runner import CPUModelRunner
 from vllm.v1.worker.gpu_worker import (Worker,
                                        init_worker_distributed_environment)
-from vllm.v1.worker.utils import is_residual_scattered_for_sp
 
 logger = init_logger(__name__)
 
@@ -102,40 +97,6 @@ class CPUWorker(Worker):
         set_random_seed(self.model_config.seed)
         self.model_runner.warming_up_model()
 
-    @torch.inference_mode()
-    def execute_model(
-        self,
-        scheduler_output: "SchedulerOutput",
-    ) -> Optional[ModelRunnerOutput]:
-        intermediate_tensors = None
-        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
-        num_input_tokens = self.model_runner._get_num_input_tokens(
-            num_scheduled_tokens)
-        all_gather_tensors = {
-            "residual":
-            not is_residual_scattered_for_sp(self.vllm_config,
-                                             num_input_tokens)
-        }
-        if not get_pp_group().is_first_rank:
-            intermediate_tensors = IntermediateTensors(
-                get_pp_group().recv_tensor_dict(
-                    all_gather_group=get_tp_group(),
-                    all_gather_tensors=all_gather_tensors))
-
-        output = self.model_runner.execute_model(scheduler_output,
-                                                 intermediate_tensors)
-
-        if not get_pp_group().is_last_rank:
-            assert isinstance(output, IntermediateTensors)
-            get_pp_group().send_tensor_dict(
-                output.tensors,
-                all_gather_group=get_tp_group(),
-                all_gather_tensors=all_gather_tensors)
-            return None
-
-        assert isinstance(output, ModelRunnerOutput)
-        return output if self.is_driver_worker else None
-
     def _get_autobind_cpu_ids(
         self, cpu_selector: Callable[[list[LogicalCPUInfo]],
                                      list[LogicalCPUInfo]]