adapt to main2main for model runner v2 (#7578)

### What this PR does / why we need it?
This PR aims to adapt to newest commit of vllm main branch for model
runner v2. please refer to
https://github.com/vllm-project/vllm-ascend/issues/5208
### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?

- vLLM version: v0.18.0
- vLLM main:
ed359c497a

---------

Signed-off-by: Ronald1995 <ronaldautomobile@163.com>

vllm_ascend/patch/worker/patch_v2/patch_block_table.py

@@ -0,0 +1,25 @@
+# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/block_table.py
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+from vllm.v1.worker.gpu import model_runner
+
+from vllm_ascend.worker.v2.block_table import AscendBlockTables
+
+# vllm-ascend need to initialize slot mapping as torch.int32 dtype,
+# but vllm default is torch.int64 dtype.
+model_runner.BlockTables = AscendBlockTables

vllm_ascend/patch/worker/patch_v2/patch_eagle.py

@@ -0,0 +1,171 @@
+# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/sample/spec_decode/eagle.py
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+import torch
+import vllm
+from vllm.v1.worker.gpu.attn_utils import build_slot_mappings_by_layer
+from vllm.v1.worker.gpu.input_batch import InputBatch
+from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample
+from vllm.v1.worker.gpu.spec_decode.eagle.speculator import prepare_eagle_decode, prepare_eagle_inputs
+
+from vllm_ascend.worker.v2.attn_utils import build_attn_metadata
+
+
+@torch.inference_mode()
+def propose(
+    self,
+    input_batch: InputBatch,
+    # [num_tokens, hidden_size]
+    last_hidden_states: torch.Tensor,
+    # num_layers x [num_tokens, hidden_size]
+    aux_hidden_states: list[torch.Tensor] | None,
+    # [num_reqs]
+    num_sampled: torch.Tensor,
+    # [num_reqs]
+    num_rejected: torch.Tensor,
+    # [max_num_reqs]
+    last_sampled: torch.Tensor,
+    # [max_num_reqs]
+    next_prefill_tokens: torch.Tensor,
+    # [max_num_reqs]
+    temperature: torch.Tensor,
+    # [max_num_reqs]
+    seeds: torch.Tensor,
+) -> torch.Tensor:
+    # NOTE(woosuk): To avoid CPU-GPU synchronization without CPU knowing the
+    # number of rejected tokens, we maintain the size of eagle's input_ids and
+    # hidden_states the same as the target model's. This means, we pad each
+    # request's query length to include any rejected positions. By doing so,
+    # we can also reuse the attention metadata (e.g., query_start_loc,
+    # seq_lens) of the target model.
+    if aux_hidden_states:
+        assert self.method == "eagle3"
+        hidden_states = self.model.combine_hidden_states(torch.cat(aux_hidden_states, dim=-1))
+    else:
+        hidden_states = last_hidden_states
+    num_tokens = input_batch.num_tokens_after_padding
+    self.hidden_states[:num_tokens] = hidden_states
+
+    # Get the input ids and last token indices for the speculator.
+    last_token_indices = prepare_eagle_inputs(
+        self.input_buffers,
+        input_batch,
+        num_sampled,
+        num_rejected,
+        last_sampled,
+        next_prefill_tokens,
+    )
+
+    # Prefill: Run the eagle speculator with eager mode.
+    # TODO(woosuk): Support CUDA graph for prefill.
+    last_hidden_states, hidden_states = self.run_model(
+        num_tokens,
+        input_batch.attn_metadata,
+        input_batch.slot_mappings,
+        num_tokens_across_dp=None,  # FIXME
+    )
+    sample_hidden_states = last_hidden_states[last_token_indices]
+    logits = self.model.compute_logits(sample_hidden_states)
+
+    num_reqs = input_batch.num_reqs
+    # NOTE(woosuk): For draft sampling, we only consider the temperature
+    # and ignore the other sampling parameters such as top_k and top_p,
+    # for simplicity and performance.
+    # While this may slightly degrade the acceptance rate, it does not
+    # affect the output distribution after rejection sampling.
+    # NOTE(Ronald1995): torch.gather will pollute the cache such as self.input_buffers.positions
+    # the bug is reported to huawei CANN team, but not fixed yet.
+    # So we clone the tensors before calling torch.gather to avoid the issue.
+    idx_mapping = self.idx_mapping[:num_reqs]
+    idx_mapping.copy_(input_batch.idx_mapping)
+    self.temperature.copy_(temperature)
+    self.seeds.copy_(seeds)
+    pos = self.input_buffers.positions[:num_reqs].clone()
+    # Gather the values and copy them to the pre-allocated buffers.
+    torch.gather(input_batch.positions, 0, last_token_indices, out=pos)
+    # NOTE(woosuk): We must add 1 to the positions to match the Gumbel noise
+    # used for draft and target sampling.
+    draft_tokens = gumbel_sample(
+        logits,
+        idx_mapping,
+        self.temperature,
+        self.seeds,
+        pos + 1,
+        apply_temperature=True,
+    )
+    if self.num_speculative_steps == 1:
+        # Early exit.
+        return draft_tokens.view(-1, 1)
+
+    # Save the draft tokens for the first step.
+    self.draft_tokens[:num_reqs, 0] = draft_tokens
+    # Prepare the inputs for the decode steps.
+    prepare_eagle_decode(
+        draft_tokens,
+        hidden_states,
+        last_token_indices,
+        input_batch.seq_lens,
+        num_rejected,
+        self.input_buffers,
+        self.hidden_states,
+        self.max_model_len,
+        self.max_num_reqs,
+    )
+    query_start_loc = self.input_buffers.query_start_loc[: num_reqs + 1]
+    slot_mappings = self.block_tables.compute_slot_mappings(
+        idx_mapping,
+        query_start_loc,
+        pos,
+    )
+
+    cudagraph_size = self.cudagraph_manager.get_cudagraph_size(num_reqs)
+    if cudagraph_size is not None:
+        # Run CUDA graph.
+        self.cudagraph_manager.run(cudagraph_size)
+        return self.draft_tokens[:num_reqs]
+
+    # Run eager mode.
+    query_start_loc_cpu = torch.arange(num_reqs + 1, dtype=torch.int32, device="cpu")
+    # HACK(woosuk)
+    block_tables = [x[:num_reqs] for x in self.block_tables.input_block_tables]
+
+    # FIXME(woosuk): This is UNSAFE!!
+    attn_metadata = build_attn_metadata(
+        attn_groups=self.attn_groups,
+        num_reqs=num_reqs,
+        num_tokens=num_reqs,
+        query_start_loc_gpu=query_start_loc,
+        query_start_loc_cpu=query_start_loc_cpu,
+        max_query_len=1,
+        seq_lens=self.input_buffers.seq_lens[:num_reqs],
+        max_seq_len=self.max_model_len,
+        block_tables=block_tables,
+        slot_mappings=slot_mappings,
+        kv_cache_config=self.kv_cache_config,
+    )
+    slot_mappings_by_layer = build_slot_mappings_by_layer(slot_mappings, self.kv_cache_config)
+    self.generate_draft(
+        num_reqs,
+        attn_metadata,
+        slot_mappings_by_layer,
+        num_tokens_across_dp=None,
+    )  # FIXME
+    return self.draft_tokens[:num_reqs]
+
+
+vllm.v1.worker.gpu.spec_decode.eagle.speculator.EagleSpeculator.propose = propose

vllm_ascend/patch/worker/patch_v2/patch_input_batch.py

@@ -0,0 +1,27 @@
+# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/input_batch.py
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+
+
+# 显式导入模块，确保模块被加载后再进行 patch
+from vllm.v1.worker.gpu import cudagraph_utils, model_runner
+
+from vllm_ascend.worker.v2.input_batch import AscendInputBatch
+
+cudagraph_utils.InputBatch = AscendInputBatch
+model_runner.InputBatch = AscendInputBatch

vllm_ascend/patch/worker/patch_v2/patch_model_state.py

@@ -0,0 +1,26 @@
+# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/model_states/default.py
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+
+from vllm.v1.worker.gpu import model_runner
+
+from vllm_ascend.worker.v2.model_states import init_asecnd_model_state
+
+# prepare_attn in AscendModelState is different from vllm,
+# we need to override init_model_state.
+model_runner.init_model_state = init_asecnd_model_state

vllm_ascend/patch/worker/patch_v2/patch_triton.py

@@ -0,0 +1,12 @@
+from vllm.v1.worker.gpu import input_batch
+from vllm.v1.worker.gpu.sample import gumbel, logprob, penalties
+
+from vllm_ascend.worker.v2.input_batch import post_update
+from vllm_ascend.worker.v2.sample.gumbel import gumbel_sample
+from vllm_ascend.worker.v2.sample.logprob import compute_token_logprobs
+from vllm_ascend.worker.v2.sample.penalties import apply_penalties
+
+logprob.compute_token_logprobs = compute_token_logprobs
+penalties.apply_penalties = apply_penalties
+gumbel.gumbel_sample = gumbel_sample
+input_batch.post_update = post_update

vllm_ascend/patch/worker/patch_v2/patch_uva.py

@@ -0,0 +1,125 @@
+# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/block_table.py
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+from collections.abc import Callable, Sequence
+
+import numpy as np
+import torch
+import vllm.v1.worker.gpu.buffer_utils
+
+
+def get_row_indices_from_key(key: int | slice | tuple, dim_size: int) -> set[int]:
+    """get the set of row indices involved in the given key."""
+    if isinstance(key, int):
+        # parse index such as np[1]
+        key = key if key >= 0 else dim_size + key
+        # handle negative index
+        if key < 0 or key >= dim_size:
+            raise IndexError(f"row index {key} out of [0, {dim_size})")
+        return {key}
+    elif isinstance(key, slice):
+        # parse slice such as np[1:3]
+        start, stop, step = key.indices(dim_size)
+        return set(range(start, stop, step))
+    elif isinstance(key, tuple):
+        # parse row slice such as np[1,:100]
+        if len(key) == 0:
+            return set(range(dim_size))
+        return get_row_indices_from_key(key[0], dim_size)
+    else:
+        # for other types such as list/ndarray, we return all rows.
+        return set(range(dim_size))
+
+
+class MonitoredNumPyArray:
+    """A wrapper around a NumPy array that monitors modifications."""
+
+    def __init__(self, array: np.ndarray, callback: Callable):
+        self._array = array
+        self._callback = callback
+
+    def __setitem__(self, key, value):
+        self._array[key] = value
+        dim_size = self._array.shape[0]
+        row_indices = get_row_indices_from_key(key, dim_size)
+        for row in row_indices:
+            self._callback(row)
+
+    def __getitem__(self, key):
+        return self._array[key]
+
+    def __getattr__(self, name):
+        return getattr(self._array, name)
+
+
+class MonitoredTorchTensor:
+    """A wrapper around a torch tensor that monitors modifications."""
+
+    def __init__(self, tensor: torch.Tensor, callback: Callable):
+        self._tensor = tensor
+        self._callback = callback
+
+    def __setitem__(self, key, value):
+        self._tensor[key] = value
+        dim_size = self._tensor.size(0)
+        row_indices = get_row_indices_from_key(key, dim_size)
+        for row in row_indices:
+            self._callback(row)
+
+    def __getitem__(self, key):
+        return self._tensor[key]
+
+    def __getattr__(self, name):
+        return getattr(self._tensor, name)
+
+
+class UvaBufferWrapper:
+    """Ascend NPU doesn't support UVA tensors directly. This is a wrapper class
+    that provides CPU and NPU views of a UVA tensor."""
+
+    def __init__(self, size: int | Sequence[int], dtype: torch.dtype):
+        self._cpu: torch.Tensor = torch.zeros(size, dtype=dtype, device="cpu", pin_memory=True)
+        self._np = self._cpu.numpy()
+        self._uva: torch.Tensor = torch.zeros_like(self._cpu, device="npu")
+        self._modified_indices: set[int] = set()
+
+    def _mark_cpu_modified(self, key: int):
+        self._modified_indices.add(key)
+
+    @property
+    def cpu(self):
+        return MonitoredTorchTensor(self._cpu, self._mark_cpu_modified)
+
+    @property
+    def np(self):
+        return MonitoredNumPyArray(self._np, self._mark_cpu_modified)
+
+    @property
+    def uva(self):
+        """Get the device data of the buffer."""
+        if self._modified_indices:
+            # Sort for better memory access locality
+            dirty_rows = sorted(self._modified_indices)
+            # can't use copy_ method, because copy_ for index tensor
+            #  will malloc new memory.
+            self._uva[dirty_rows] = self._cpu[dirty_rows].to(device="npu", non_blocking=True)
+            self._modified_indices.clear()
+        return self._uva
+
+
+vllm.v1.worker.gpu.buffer_utils.UvaBuffer = UvaBufferWrapper