[MTP] follow custom deepseek modeling changes to support graph mode (#636)

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### What this PR does / why we need it?

As custom deepseek modeling do some changes to support graph mode in
https://github.com/vllm-project/vllm-ascend/pull/585, so i follow it to
change custom deepseek_mtp modeling.

And some modifications for k>1 were not carried over by the
https://github.com/vllm-project/vllm-ascend/pull/429, now i add it.

In order to better take care of the MTP feature in the vllm-ascend
repository, I added cases related to graph mode(torchair), but i skip it
since torchair can not correctly clean up memory in vllmrunner.

Also i add some case for MTP quantization weights, but test weight is
not ready, so i skip it and i will open it when test quant weights is
ready.

https://github.com/vllm-project/vllm-ascend/pull/648 did not completely
fix the sample
change(https://github.com/vllm-project/vllm-ascend/issues/660) issue, I
added the relevant changes.

### Does this PR introduce _any_ user-facing change?
now, u can use following method to use mtp in deepseek v3/r1 float or
quant weights with eager mode.
```python
llm = LLM(
    model="wemaster/deepseek_mtp_main_random_bf16",
    tensor_parallel_size=2,
    speculative_config={
        "num_speculative_tokens": 1,
    },
    enforce_eager=True,
    trust_remote_code=True,
    disable_log_stats=False,
    gpu_memory_utilization=0.8,
    max_model_len=64,
)
```

or use mtp in deepseek v3/r1 float or quant weights with graph
mode（torchair）
```python
llm = LLM(
    model="wemaster/deepseek_mtp_main_random_bf16",
    tensor_parallel_size=2,
    speculative_config={
        "num_speculative_tokens": 1,
    },
    trust_remote_code=True,
    additional_config={
        'enable_graph_mode': True,
    },
    disable_log_stats=False,
    gpu_memory_utilization=0.8,
    max_model_len=64,
)
```

add notes:
1. now, we support k>1, so u can set num_speculative_tokens > 1 if there
is sufficient redundant computing power;
2. MTP is not supported in V1, we will support it when vLLM does it in
https://github.com/vllm-project/vllm/issues/13500.
3. if u run MTP failed by `segmentation fault`, u can follow v0.7.3
patch https://github.com/vllm-project/vllm-ascend/pull/236 file
`vllm_ascend/patch/patch_metrics.py` method
`__npu_async_metrics_collector_init__`

### How was this patch tested?
local tested passed and test by CI

Signed-off-by: mengwei805 <mengwei25@huawei.com>

vllm_ascend/patch/worker/patch_common/patch_multi_step_worker.py

@@ -22,6 +22,7 @@ from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.sequence import ExecuteModelRequest
 from vllm.spec_decode.multi_step_worker import MultiStepWorker
 
+from vllm_ascend.utils import vllm_version_is
 from vllm_ascend.worker.draft_model_runner import TP1DraftModelRunner
 
 
@@ -61,15 +62,19 @@ def sampler_output(
     else:
         # Here we run multi-step directly, with every step prepared
         # on the CPU.
-        # TODO: Remove this branch once DraftModelRunner supports TP>1
+        # TODO Remove this branch once DraftModelRunner supports TP>1
         # and other restrictions that are part of DraftModelRunner's
         # supports_gpu_multi_step(..)
+        if expanded_request.previous_hidden_states is not None:
+            self.worker.model_runner.return_hidden_states = True
         for _ in range(sample_len):
             model_output: List[SamplerOutput] = self.worker.execute_model(
                 execute_model_req=expanded_request)
             assert (len(model_output) == 1
                     ), "composing multistep workers not supported"
             model_output = model_output[0]
+            self._maybe_update_previous_hidden_states(model_output,
+                                                      expanded_request)
 
             self._append_new_tokens(model_output,
                                     expanded_request.seq_group_metadata_list,
@@ -84,4 +89,22 @@ def sampler_output(
     return filtered_model_outputs, True
 
 
+def set_include_gpu_probs_tensor(self) -> None:
+    # Need include_gpu_probs_tensor for MultiSteoWorker
+    if hasattr(self.model_runner.model, "sampler"):
+        self.model_runner.model.sampler.include_gpu_probs_tensor = True
+    if not vllm_version_is("0.8.4"):
+        self.model_runner.sampler.include_gpu_probs_tensor = True
+
+
+def set_should_modify_greedy_probs_inplace(self) -> None:
+    if hasattr(self.model_runner.model, "sampler"):
+        self.model_runner.model.sampler.should_modify_greedy_probs_inplace = (
+            True)
+    if not vllm_version_is("0.8.4"):
+        self.model_runner.sampler.should_modify_greedy_probs_inplace = True
+
+
 MultiStepWorker.sampler_output = torch.inference_mode()(sampler_output)
+MultiStepWorker.set_include_gpu_probs_tensor = set_include_gpu_probs_tensor
+MultiStepWorker.set_should_modify_greedy_probs_inplace = set_should_modify_greedy_probs_inplace

vllm_ascend/patch/worker/patch_common/patch_spec_decode_worker.py

@@ -93,7 +93,7 @@ def create_worker(
 
             proposer_worker = MultiStepWorker(**draft_worker_kwargs)
             if draft_model_config.hf_config.model_type == "deepseek_mtp":
-                num_spec_prefill_steps = num_speculative_tokens
+                num_spec_prefill_steps = draft_model_config.hf_config.n_predict
 
         proposer_worker = SmallerTpProposerWorker.maybe_wrap_worker(
             proposer_worker, draft_tp, target_tp)