[Model] Support DeepSeek-V4

vllm_mlu/v1/core/sched/output.py

@@ -0,0 +1,111 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-MLU project
+
+from dataclasses import dataclass
+from functools import cached_property
+from typing import TYPE_CHECKING
+
+from typing_extensions import deprecated
+
+from vllm._bc_linter import bc_linter_include
+
+if TYPE_CHECKING:
+    import numpy as np
+    import numpy.typing as npt
+    import torch
+
+    from vllm.distributed.ec_transfer.ec_connector.base import ECConnectorMetadata
+    from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata
+    from vllm.lora.request import LoRARequest
+    from vllm.multimodal.inputs import MultiModalFeatureSpec
+    from vllm.pooling_params import PoolingParams
+    from vllm.sampling_params import SamplingParams
+    from vllm.v1.request import Request
+else:
+    ECConnectorMetadata = object
+    KVConnectorMetadata = object
+    LoRARequest = object
+    MultiModalFeatureSpec = object
+    PoolingParams = object
+    SamplingParams = object
+    Request = object
+
+'''
+=============================
+Modify by vllm_mlu
+=============================
+@brief: Add new_toked_ids to pass the first token generated
+by the prefiller to the decoder's model_runner.
+'''
+@bc_linter_include
+@dataclass
+class NewRequestData:
+    req_id: str
+    prompt_token_ids: list[int] | None
+    mm_features: list[MultiModalFeatureSpec]
+    sampling_params: SamplingParams | None
+    pooling_params: PoolingParams | None
+    block_ids: tuple[list[int], ...]
+    num_computed_tokens: int
+    lora_request: LoRARequest | None
+    new_token_ids: list[list[int]]
+    prompt_embeds: "torch.Tensor | None" = None
+
+    @classmethod
+    def from_request(
+        cls,
+        request: Request,
+        block_ids: tuple[list[int], ...],
+    ) -> "NewRequestData":
+        return cls(
+            req_id=request.request_id,
+            prompt_token_ids=request.prompt_token_ids,
+            mm_features=request.mm_features,
+            sampling_params=request.sampling_params,
+            pooling_params=request.pooling_params,
+            block_ids=block_ids,
+            num_computed_tokens=request.num_computed_tokens,
+            lora_request=request.lora_request,
+            prompt_embeds=request.prompt_embeds,
+            new_token_ids=request._output_token_ids,
+        )
+
+    def __repr__(self) -> str:
+        prompt_embeds_shape = self.prompt_embeds.shape if self.prompt_embeds else None
+        return (
+            f"NewRequestData("
+            f"req_id={self.req_id},"
+            f"prompt_token_ids={self.prompt_token_ids},"
+            f"mm_features={self.mm_features},"
+            f"sampling_params={self.sampling_params},"
+            f"block_ids={self.block_ids},"
+            f"num_computed_tokens={self.num_computed_tokens},"
+            f"lora_request={self.lora_request},"
+            f"prompt_embeds_shape={prompt_embeds_shape},"
+            f"new_token_ids={self.new_token_ids}"
+            ")"
+        )
+
+    # Version of __repr__ with the prompt data obfuscated
+    def anon_repr(self) -> str:
+        prompt_token_ids_len = (
+            len(self.prompt_token_ids) if self.prompt_token_ids is not None else None
+        )
+        prompt_embeds_shape = self.prompt_embeds.shape if self.prompt_embeds else None
+        return (
+            f"NewRequestData("
+            f"req_id={self.req_id},"
+            f"prompt_token_ids_len={prompt_token_ids_len},"
+            f"mm_features={self.mm_features},"
+            f"sampling_params={self.sampling_params},"
+            f"block_ids={self.block_ids},"
+            f"num_computed_tokens={self.num_computed_tokens},"
+            f"lora_request={self.lora_request},"
+            f"prompt_embeds_shape={prompt_embeds_shape}"
+            ")"
+        )
+'''
+==================
+End of MLU Hijack
+==================
+'''