ba3dfbd59e
### What this PR does / why we need it?
A refactoring of forward_context and model_runner_v1, add some context
which is necessary in model inference into forward_context, and refactor
dummy_run logic, make it more reasonable.
Some details for this PR:
Add `ascend_forward_context`;
Update mc2_v2 op, and support `active_mask` param;
Update scripts in examples dir;
refactor `dummy_run` logic;
Add soc_version for A2 and A3;
### Does this PR introduce _any_ user-facing change?
No change at user-facing.
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
57c22e57f9
Signed-off-by: zzzzwwjj <1183291235@qq.com>
182 lines
6.8 KiB
Python
182 lines
6.8 KiB
Python
import torch
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from vllm.attention.layer import Attention
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from vllm.config import (VllmConfig, get_layers_from_vllm_config,
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set_current_vllm_config)
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from vllm.model_executor.model_loader import get_model_loader
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from vllm.model_executor.model_loader.utils import (
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process_weights_after_loading, set_default_torch_dtype)
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm_ascend.ascend_forward_context import set_ascend_forward_context
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from vllm_ascend.models.deepseek_mtp import CustomDeepSeekMTP
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class MtpProposer:
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def __init__(
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self,
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vllm_config: VllmConfig,
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runner,
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):
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self.vllm_config = vllm_config
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self.num_speculative_tokens = (
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vllm_config.speculative_config.num_speculative_tokens)
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self.block_size = vllm_config.cache_config.block_size
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self.runner = runner
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@staticmethod
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def prepare_inputs(
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# [batch_size + 1]
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cu_target_query_lens: torch.Tensor,
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# [batch_size]
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num_rejected_tokens: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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# cu_target_query_lens: [0, a, a + b, a + b + c]
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# num_rejected_tokens: [n1, n2, n3]
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# num_tokens_per_req: [a - n1, b - n2, c - n3]
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# cu_num_tokens: [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
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# token_indices: [0, 1, ..., a - n1 - 1,
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# a, a + 1, ..., a + b - n2 - 1,
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# a + b, a + b + 1, ..., a + b + c - n3 - 1]
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# [0, a, a + b, a + b + c] -> [a, b, c]
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query_len_per_req = (cu_target_query_lens[1:] -
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cu_target_query_lens[:-1])
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# [a, b, c] -> [a - n1, b - n2, c - n3]
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num_tokens_per_req = query_len_per_req - num_rejected_tokens
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cu_num_tokens = torch.empty_like(cu_target_query_lens)
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torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:])
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cu_num_tokens[0] = 0
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# FIXME(woosuk): Avoid synchronization.
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num_tokens = cu_num_tokens[-1].item()
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token_indices = torch.empty(
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num_tokens,
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dtype=torch.int32,
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device=cu_num_tokens.device,
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)
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BLOCK_SIZE = 1024
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prepare_input_kernel(
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token_indices,
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cu_target_query_lens,
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cu_num_tokens,
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block_size=BLOCK_SIZE,
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)
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return cu_num_tokens, token_indices
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def propose(
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self,
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# [num_tokens]
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target_token_ids: torch.Tensor,
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# [num_tokens]
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target_positions: torch.Tensor,
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# [num_tokens, hidden_size]
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target_hidden_states: torch.Tensor,
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# [num_tokens]
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target_slot_mapping: torch.Tensor,
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# [batch_size]
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next_token_ids: torch.Tensor,
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# [batch_size + 1] starting with 0
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cu_num_tokens: torch.Tensor,
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# [batch_size, max_num_blocks_per_req]
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block_table: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> torch.Tensor:
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num_tokens = target_token_ids.shape[0]
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batch_size = next_token_ids.shape[0]
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last_token_indices = cu_num_tokens[1:] - 1
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input_ids = torch.empty_like(target_token_ids)
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# Shift the input ids by one token.
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# E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
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input_ids[:-1] = target_token_ids[1:]
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# Replace the last token with the next token.
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# E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
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input_ids[last_token_indices] = next_token_ids
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query_lens = cu_num_tokens[1:] - cu_num_tokens[:-1]
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max_query_len = query_lens.max().item()
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# FIXME: reorder_batch() needs to be called before build()
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# because fields of attn_metadata_builder needs to be updated.
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# However, currently reorder_batch() takes input_batch and
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# scheduler_output as arguments, we should probably refactor
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# the method to use new data structures which are independent
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# from input_batch and scheduler_output.
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# self.runner.attn_metadata_builder.reorder_batch(
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# input_batch=self.runner.input_batch,
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# scheduler_output=self.runner.scheduler_output,
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# )
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attn_metadata = self.runner.attn_metadata_builder.build(
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num_reqs=batch_size,
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num_actual_tokens=num_tokens,
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max_query_len=max_query_len,
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query_start_loc=cu_num_tokens,
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)
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with set_ascend_forward_context(attn_metadata, self.vllm_config):
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hidden_states = self.model(
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input_ids=input_ids,
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positions=target_positions,
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previous_hidden_states=target_hidden_states,
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)
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sample_hidden_states = hidden_states[last_token_indices]
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logits = self.model.compute_logits(sample_hidden_states, None)
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draft_token_ids = logits.argmax(dim=-1)
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# [batch_size, 1]
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return draft_token_ids.view(-1, 1)
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def load_model(self) -> None:
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loader = get_model_loader(self.vllm_config.load_config)
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target_attn_layer_names = set(
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get_layers_from_vllm_config(self.vllm_config, Attention).keys())
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draft_model_config = \
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self.vllm_config.speculative_config.draft_model_config
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target_device = self.vllm_config.device_config.device
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with set_default_torch_dtype(
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draft_model_config.dtype), set_current_vllm_config(
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self.vllm_config):
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self.model = CustomDeepSeekMTP(
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vllm_config=self.vllm_config).to(target_device)
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draft_attn_layer_names = (
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get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
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target_attn_layer_names)
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assert len(draft_attn_layer_names) == 1
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self.attn_layer_name = next(iter(draft_attn_layer_names))
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self.model.load_weights(
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loader.get_all_weights(
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self.vllm_config.speculative_config.draft_model_config,
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self.model))
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process_weights_after_loading(self.model, draft_model_config,
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target_device)
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# TODO Using torch instead of triton may result in poor performance
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def prepare_input_kernel(out_ptr: torch.Tensor, cu_query_lens: torch.Tensor,
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cu_num_tokens: torch.Tensor, block_size: int):
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device = cu_query_lens.device
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dtype = out_ptr.dtype
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offsets = torch.arange(block_size, device=device, dtype=dtype)
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start_pos = cu_num_tokens[:-1]
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end_pos = cu_num_tokens[1:]
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num_tokens = end_pos - start_pos
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global_indices = (start_pos.view(-1, 1) + offsets.view(1, -1))
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values = (cu_query_lens[:-1].view(-1, 1) + offsets.view(1, -1))
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mask = (offsets.view(1, -1) < num_tokens.view(-1, 1))
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global_indices_flat = global_indices[mask]
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values_flat = values[mask]
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out_ptr[global_indices_flat] = values_flat
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