Drop 0.11.0 support (#4377)

There is a lot hack code for v0.11.0, which makes the code hard to
upgrade to newer vLLM version. Since v0.11.0 will release soon. Let's
drop v0.11.0 support first. Then we'll upgrade to v0.11.2 soon.


- vLLM version: v0.11.0
- vLLM main:
2918c1b49c

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/patch/worker/patch_deepseek_mtp.py

@@ -1,94 +0,0 @@
-import torch
-import torch.nn as nn
-from transformers import PretrainedConfig
-from vllm.config import VllmConfig
-from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.quantization import QuantizationConfig
-from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
-from vllm.model_executor.models.deepseek_mtp import \
-    DeepSeekMultiTokenPredictorLayer
-from vllm.model_executor.models.deepseek_v2 import DeepseekV2DecoderLayer
-from vllm.model_executor.models.utils import maybe_prefix
-
-from vllm_ascend.utils import vllm_version_is
-
-if vllm_version_is("0.11.0"):
-    from vllm.compilation.decorators import support_torch_compile
-    from vllm.model_executor.models.deepseek_mtp import DeepSeekMTP
-
-
-class SharedHead(nn.Module):
-
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        prefix: str,
-        quant_config: QuantizationConfig = None,
-    ) -> None:
-        super().__init__()
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.head = ParallelLMHead(
-            config.vocab_size,
-            config.hidden_size,
-            quant_config=quant_config,
-            prefix=maybe_prefix(prefix, "head"),
-        )
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        return self.norm(hidden_states)
-
-
-def predictor_init(self, vllm_config: VllmConfig, prefix: str) -> None:
-    nn.Module.__init__(self)
-    config = vllm_config.model_config.hf_config
-    quant_config = vllm_config.quant_config
-
-    self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-    self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-    self.eh_proj = nn.Linear(config.hidden_size * 2,
-                             config.hidden_size,
-                             bias=False)
-    # We don't need topk_indices_buffer in Ascend
-    topk_indices_buffer = None
-    self.shared_head = SharedHead(config=config,
-                                  prefix=prefix,
-                                  quant_config=quant_config)
-    self.mtp_block = DeepseekV2DecoderLayer(vllm_config, prefix,
-                                            topk_indices_buffer)
-
-
-def predictor_forward(
-    self,
-    input_ids: torch.Tensor,
-    positions: torch.Tensor,
-    previous_hidden_states: torch.Tensor,
-    inputs_embeds: torch.Tensor | None = None,
-    spec_step_index: int = 0,
-) -> torch.Tensor:
-    assert inputs_embeds is not None
-    # masking inputs at position 0, as not needed by MTP
-    inputs_embeds = torch.where(positions.unsqueeze(-1) == 0, 0, inputs_embeds)
-    inputs_embeds = self.enorm(inputs_embeds)
-    previous_hidden_states = self.hnorm(previous_hidden_states)
-
-    hidden_states = self.eh_proj(
-        torch.cat([inputs_embeds, previous_hidden_states], dim=-1))
-
-    hidden_states, residual = self.mtp_block(positions=positions,
-                                             hidden_states=hidden_states,
-                                             residual=None)
-    hidden_states = residual + hidden_states
-    return hidden_states
-
-
-# Patch this only for aclgraph support, as this is not support in vLLM 0.11.0
-@support_torch_compile
-class AscendDeepSeekMTP(DeepSeekMTP):
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__(vllm_config=vllm_config, prefix=prefix)
-
-
-DeepSeekMultiTokenPredictorLayer.__init__ = predictor_init
-if vllm_version_is("0.11.0"):
-    DeepSeekMultiTokenPredictorLayer.forward = predictor_forward