[Misc] Code clean up (#1674)

Remove useless function
- vLLM version: v0.9.2
- vLLM main:
vllm-project/vllm@b942c09

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

Author: wangxiyuan

vllm_ascend/worker/eagle_proposer_v1.py

@@ -384,46 +384,3 @@ def prepare_eagle_input_sequential(out_tensor: torch.Tensor,
                    (target_indices < end_pos) & \
                    (offset_tensor < num_tokens)
             out_tensor[target_indices[mask]] = values_to_store[mask]
-
-
-# NOTE(woosuk): Currently, the below code is not used and we always use argmax
-# to sample the draft tokens. We will use this after we find a way to manage
-# the draft prob tensor.
-# Refer to https://github.com/vllm-project/vllm/pull/16899 for the details.
-# FIXME(woosuk): The logic here is duplicated with the main sampling code.
-# We should refactor this to reuse the same sampling implementation.
-def compute_probs_and_sample_next_token(
-    logits: torch.Tensor,
-    sampling_metadata: SamplingMetadata,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    if sampling_metadata.all_greedy:
-        # For greedy requests, draft_probs is not used in rejection sampling.
-        # Therefore, we can just return the logits.
-        probs = logits
-        next_token_ids = logits.argmax(dim=-1)
-        return next_token_ids, probs
-
-    is_greedy = sampling_metadata.temperature == -1
-    temperature = torch.where(is_greedy, 1.0, sampling_metadata.temperature)
-    logits.div_(temperature.view(-1, 1))
-    probs = logits.softmax(dim=-1, dtype=torch.float32)
-
-    # NOTE(woosuk): Currently, we ignore most of the sampling parameters in
-    # generating the draft tokens. We only use the temperature. While this
-    # could degrade the acceptance rate, it does not affect the distribution
-    # of the generated tokens after rejection sampling.
-
-    # TODO(woosuk): Consider seeds.
-    q = torch.empty_like(probs)
-    q.exponential_()
-    # NOTE(woosuk): We shouldn't use `probs.div_(q)` because the draft_probs
-    # will be used later for rejection sampling.
-    next_token_ids = probs.div(q).argmax(dim=-1).view(-1)
-    if not sampling_metadata.all_random:
-        greedy_token_ids = probs.argmax(dim=-1)
-        next_token_ids = torch.where(
-            is_greedy,
-            greedy_token_ids,
-            next_token_ids,
-        )
-    return next_token_ids, probs

vllm_ascend/worker/mtp_proposer_v1.py

@@ -12,43 +12,6 @@
 from vllm_ascend.models.deepseek_mtp import CustomDeepSeekMTP
 
 
-# FIXME(woosuk): The logic here is duplicated with the main sampling code.
-# We should refactor this to reuse the same sampling implementation.
-def compute_probs_and_sample_next_token(
-    logits: torch.Tensor,
-    sampling_metadata: SamplingMetadata,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    if sampling_metadata.all_greedy:
-        # For greedy requests, draft_probs is not used in rejection sampling.
-        # Therefore, we can just return the logits.
-        probs = logits
-        next_token_ids = logits.argmax(dim=-1)
-        return next_token_ids, probs
-
-    is_greedy = sampling_metadata.temperature == -1
-    temperature = torch.where(is_greedy, 1.0, sampling_metadata.temperature)
-    logits.div_(temperature.view(-1, 1))
-    probs = logits.softmax(dim=-1, dtype=torch.float32)
-
-    # NOTE(woosuk): Currently, we ignore most of the sampling parameters in
-    # generating the draft tokens. We only use the temperature. While this
-    # could degrade the acceptance rate, it does not affect the distribution
-    # of the generated tokens after rejection sampling.
-
-    # TODO(woosuk): Consider seeds.
-    q = torch.empty_like(probs)
-    q.exponential_()
-    next_token_ids = probs.div_(q).argmax(dim=-1).view(-1)
-    if not sampling_metadata.all_random:
-        greedy_token_ids = probs.argmax(dim=-1)
-        next_token_ids = torch.where(
-            is_greedy,
-            greedy_token_ids,
-            next_token_ids,
-        )
-    return next_token_ids, probs
-
-
 class MtpProposer:
 
     def __init__(
@@ -121,7 +84,7 @@ def propose(
         # [batch_size, max_num_blocks_per_req]
         block_table: torch.Tensor,
         sampling_metadata: SamplingMetadata,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    ) -> torch.Tensor:
         num_tokens = target_token_ids.shape[0]
         batch_size = next_token_ids.shape[0]
         last_token_indices = cu_num_tokens[1:] - 1