d886b81971
### What this PR does / why we need it?
According to the official documentation, the parameter
"draft_tensor_parallel_size": 1 is supposed to be applied to the Eagle3
model. However, based on actual debugging, it was found that the number
of tensor parallelisms (tp) of the Eagle model is consistent with that
of the target model. The setting of tp for the draft model did not take
effect as expected.
**Note:** This feature has not been superimposed and tested with `sp`
and `dp`. It will be adapted later
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
```python
from vllm import LLM, SamplingParams
def main():
prompts = [
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Create an LLM.
llm = LLM(
model="meta-llama/Llama-3.1-8B-Instruct",
tensor_parallel_size=4,
gpu_memory_utilization=0.9,
enforce_eager=True,
speculative_config={
"method": "eagle3",
"model": "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
"draft_tensor_parallel_size": 1,
"num_speculative_tokens": 3,
},
)
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
print(f"Outputs: {outputs}")
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
Fixes vllm-project/vllm#31345
Signed-off-by: zhaomingyu <zhaomingyu13@h-partners.com>
Co-authored-by: drslark <slarksblood@qq.com>
932 lines
44 KiB
Python
932 lines
44 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
||
from contextlib import nullcontext
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||
from typing import Optional
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||
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||
import numpy as np
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import torch
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import torch.nn as nn
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||
import torch.nn.functional as F
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from vllm.config import (CompilationMode, CUDAGraphMode, VllmConfig,
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get_layers_from_vllm_config)
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from vllm.distributed.parallel_state import (get_pp_group, get_world_group,
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init_model_parallel_group,
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patch_tensor_parallel_group)
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from vllm.forward_context import get_forward_context
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from vllm.logger import logger
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||
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
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||
from vllm.model_executor.model_loader import get_model
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from vllm.model_executor.models import supports_multimodal
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from vllm.model_executor.models.deepseek_v2 import DeepseekV32IndexerCache
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from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
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from vllm.triton_utils import HAS_TRITON, triton
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from vllm.utils.math_utils import cdiv
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from vllm.utils.platform_utils import is_pin_memory_available
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from vllm.v1.attention.backends.utils import CommonAttentionMetadata
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.spec_decode.eagle import PADDING_SLOT_ID
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from vllm.v1.spec_decode.eagle import EagleProposer as VllmEagleProposer
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm_ascend.ascend_forward_context import set_ascend_forward_context
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from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
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from vllm_ascend.attention.attention_v1 import AscendAttentionState
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from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
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from vllm_ascend.compilation.acl_graph import (ACLGraphWrapper,
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update_attn_dcp_pcp_params,
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update_attn_params,
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update_mla_attn_dcp_pcp_params,
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update_mla_attn_params)
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from vllm_ascend.ops.rotary_embedding import update_cos_sin
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from vllm_ascend.ops.triton.spec_decode.utils import \
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prepare_inputs_padded_kernel
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from vllm_ascend.ops.triton.triton_utils import get_vectorcore_num
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from vllm_ascend.utils import shared_expert_dp_enabled
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# Currently we will fix block size to a small one since `num_reqs` can't be too large
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_PREPARE_INPUTS_BLOCK_SIZE = 4
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class EagleProposer(VllmEagleProposer):
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def __init__(self,
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vllm_config: VllmConfig,
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device: torch.device,
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runner=None):
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super().__init__(vllm_config, device, runner)
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self.use_async_scheduling = self.vllm_config.scheduler_config.async_scheduling
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# there is synchronization between mtp steps when enabling aclgraph,
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# disable aclgraph when use async scheduling to avoid the
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# synchronization overhead.
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# NOTE: we need to set aclgraph_runtime_mode to None in both dummy_run
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# and _propose.
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self.use_cuda_graph = (
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self.vllm_config.compilation_config.mode
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== CompilationMode.VLLM_COMPILE
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and not self.vllm_config.model_config.enforce_eager
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and not self.use_async_scheduling
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and not self.vllm_config.speculative_config.enforce_eager)
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self.cudagraph_batch_sizes = list(
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sorted(
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self.vllm_config.compilation_config.cudagraph_capture_sizes))
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self.pcp_size = self.runner.pcp_size
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self.decode_threshold = 1 + self.num_speculative_tokens
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self.query_start_loc = self.runner._make_buffer(
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self.runner.max_num_reqs + 1, dtype=torch.int32)
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self.arange_cpu = torch.arange(self.arange.shape[0],
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device="cpu",
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dtype=torch.int32)
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self.attn_mask_builder = AttentionMaskBuilder(self.device)
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self.enable_shared_expert_dp = shared_expert_dp_enabled()
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self.dcp_size = self.runner.dcp_size
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self.pcp_rank = self.runner.pcp_rank
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||
self.dcp_rank = self.runner.dcp_rank
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self.use_aclgraph = self.runner._use_aclgraph()
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self.full_indices = range(
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self.runner.max_num_tokens * self.pcp_size * self.dcp_size +
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self.pcp_size * self.dcp_size * self.runner.max_num_reqs)
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self.use_sparse = hasattr(vllm_config.model_config.hf_text_config,
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"index_topk")
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# NOTE:
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# `draft_tensor_parallel_size` does not take effect for Eagle:
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# the draft model uses the same TP size as the target model in practice.
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# so we applied this patch to set tp=1 of draft model separately.
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# Due to verification of `_verify_and_get_draft_tp` in vllm,
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# the value of `draft_tensor_parallel_size` here will either be 1 separately
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# or the same as target model.
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# TODO(zhaomingyu13): If we want to adapt to the case where draft model tp
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# is not 1 and differs from target model, this part should be rewritten.
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if (vllm_config.parallel_config.tensor_parallel_size
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!= self.speculative_config.draft_tensor_parallel_size):
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tp_group = init_model_parallel_group(
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[[get_world_group().rank]],
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get_world_group().rank,
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torch.distributed.get_backend(get_world_group().device_group),
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use_message_queue_broadcaster=True,
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group_name="tp",
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)
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self.tp_group_context = patch_tensor_parallel_group(tp_group)
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else:
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self.tp_group_context = nullcontext()
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def load_model(self, model: nn.Module) -> None:
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target_attn_layer_names = set(
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get_layers_from_vllm_config(self.vllm_config,
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AttentionLayerBase).keys())
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target_indexer_layer_names = set(
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get_layers_from_vllm_config(self.vllm_config,
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DeepseekV32IndexerCache).keys())
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self.model = get_model(vllm_config=self.vllm_config,
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model_config=self.vllm_config.
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speculative_config.draft_model_config)
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indexer_layers = get_layers_from_vllm_config(
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self.vllm_config, DeepseekV32IndexerCache).keys()
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draft_attn_layer = get_layers_from_vllm_config(
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self.vllm_config, AttentionLayerBase).keys()
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draft_attn_layer_names = draft_attn_layer - target_attn_layer_names
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draft_indexer_layer_names = indexer_layers - target_indexer_layer_names
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draft_attn_layer_names = draft_attn_layer_names - draft_indexer_layer_names
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assert len(draft_attn_layer_names) == 1
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self.attn_layer_name = list(draft_attn_layer_names)
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self.attn_layer_names = self.attn_layer_name
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# share embed_tokens with the target model if needed
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if get_pp_group().world_size == 1:
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if self.method == "mtp":
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if self.vllm_config.model_config.is_deepseek_mla and \
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torch.equal(self.model.model.embed_tokens.weight,
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model.model.embed_tokens.weight):
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# If pp>1, the weights of mtp and the main model's embedding are not on the same device.
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# check if mtp model use main model's embedding and LMhead
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logger.info(
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"The MTP head shares the same vocab embedding" \
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" with the target model."
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)
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self.model.model.embed_tokens = model.model.embed_tokens
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else:
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logger.info(
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" The MTP head loaded its own vocab embedding" \
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" weights instead of sharing them with the target model."
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)
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else:
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logger.info(
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"The EAGLE head shares the same vocab embedding" \
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" with the target model."
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)
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self.model.model.embed_tokens = model.model.embed_tokens
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else:
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logger.info(
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"Since PP > 1 or other reasons the model head loaded its own vocab embedding" \
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" weights instead of sharing them with the target model."
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)
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# share lm_head with the target model if needed
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# some model definition do not define lm_head explicitly
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# and reuse embed_tokens for lm_head, e.g., CohereForCausalLM
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if self.method == "eagle" and hasattr(model, "lm_head"):
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logger.info("Loading EAGLE LM head weights from the target model.")
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if supports_multimodal(model):
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self.model.lm_head = model.get_language_model().lm_head
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else:
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self.model.lm_head = model.lm_head
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if self.method == "mtp" and \
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self.vllm_config.model_config.is_deepseek_mla:
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for _, layer_module in self.model.model.layers.items():
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if torch.equal(layer_module.shared_head.head.weight,
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model.lm_head.weight):
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layer_module.shared_head.head = model.lm_head
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if self.vllm_config.compilation_config.cudagraph_mode.has_full_cudagraphs(
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) and self.use_cuda_graph:
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self.update_stream = torch.npu.Stream()
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self.model = ACLGraphWrapper(self.model,
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self.vllm_config,
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runtime_mode=CUDAGraphMode.FULL)
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def get_model(self) -> nn.Module:
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# get raw model out of the aclgraph wrapper.
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if isinstance(self.model, ACLGraphWrapper):
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return self.model.unwrap()
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return self.model
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@torch.inference_mode()
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def dummy_run(self,
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num_tokens: int,
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with_prefill: bool = False,
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in_graph_capturing: bool = False,
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num_reqs: int = 0,
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num_tokens_across_dp: Optional[torch.Tensor] = None,
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aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
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batch_descriptor=None,
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dummy_compute_logits=lambda hidden_states: None,
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is_profile=False):
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# update global cos, sin
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update_cos_sin(self.positions[:num_tokens])
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attn_metadata = None
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if not self.use_cuda_graph:
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aclgraph_runtime_mode = CUDAGraphMode.NONE
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if aclgraph_runtime_mode == CUDAGraphMode.FULL and len(
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self.runner.attn_groups) > 0:
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num_computed_tokens_cpu = (
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self.runner.input_batch.
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num_computed_tokens_cpu_tensor[:num_reqs])
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self.query_start_loc.cpu[:num_reqs + 1] = torch.tensor(
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[0] + self.runner.actual_seq_lengths_q[:num_reqs],
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device="cpu",
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dtype=torch.int32)
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self.query_start_loc.copy_to_gpu()
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common_attn_metadata = AscendCommonAttentionMetadata(
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query_start_loc=self.query_start_loc.gpu[:num_reqs + 1],
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query_start_loc_cpu=self.query_start_loc.cpu[:num_reqs + 1],
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seq_lens_cpu=self.runner.seq_lens.cpu,
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seq_lens=self.runner.seq_lens.gpu[:num_reqs],
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num_reqs=num_reqs,
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num_actual_tokens=num_tokens,
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num_input_tokens=num_tokens,
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max_query_len=self.num_speculative_tokens + 1,
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num_computed_tokens_cpu=num_computed_tokens_cpu,
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actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
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block_table_tensor=self.runner.input_batch.block_table[0].
|
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get_device_tensor()[:num_reqs],
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slot_mapping=self.runner.input_batch.block_table[0].
|
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slot_mapping.gpu,
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||
positions=self.runner.positions.gpu,
|
||
attn_state=self.runner.attn_state,
|
||
decode_token_per_req=self.runner.decode_token_per_req,
|
||
max_seq_len=0,
|
||
)
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||
|
||
builder = self.runner.attn_groups[0][0].get_metadata_builder()
|
||
attn_metadata_eagle = builder.build_for_graph_capture(
|
||
common_attn_metadata, AscendAttentionState.ChunkedPrefill)
|
||
attn_metadata = {}
|
||
for layer_name in self.attn_layer_name:
|
||
attn_metadata[layer_name] = attn_metadata_eagle
|
||
|
||
model_input_ids = self.input_ids[:num_tokens]
|
||
model_positions = self.positions[:num_tokens]
|
||
model_previous_hidden_states = self.hidden_states[:num_tokens]
|
||
for i in range(self.num_speculative_tokens):
|
||
if i > 0 and in_graph_capturing and aclgraph_runtime_mode == CUDAGraphMode.FULL:
|
||
aclgraph_runtime_mode = CUDAGraphMode.NONE
|
||
with set_ascend_forward_context(
|
||
attn_metadata,
|
||
self.vllm_config,
|
||
num_tokens=num_tokens,
|
||
num_actual_tokens=0,
|
||
in_profile_run=is_profile,
|
||
batch_descriptor=batch_descriptor,
|
||
aclgraph_runtime_mode=aclgraph_runtime_mode,
|
||
is_draft_model=True):
|
||
|
||
if self.enable_shared_expert_dp:
|
||
model_previous_hidden_states = torch.ops.vllm.maybe_pad_and_reduce(
|
||
model_previous_hidden_states)
|
||
|
||
self.model(
|
||
input_ids=model_input_ids,
|
||
positions=model_positions,
|
||
hidden_states=model_previous_hidden_states,
|
||
)
|
||
forward_context = get_forward_context()
|
||
if (forward_context.cudagraph_runtime_mode
|
||
== CUDAGraphMode.FULL
|
||
and not forward_context.capturing):
|
||
update_attn_params(
|
||
self.update_stream,
|
||
forward_context,
|
||
num_tokens,
|
||
self.vllm_config,
|
||
)
|
||
|
||
if self.enable_shared_expert_dp:
|
||
model_previous_hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
||
model_previous_hidden_states, True)
|
||
|
||
dummy_compute_logits(self.hidden_states)
|
||
|
||
def _propose(
|
||
self,
|
||
# [num_tokens]
|
||
target_token_ids: torch.Tensor,
|
||
# [num_tokens] or [3, num_tokens] when M-RoPE is enabled
|
||
target_positions: torch.Tensor,
|
||
# [num_tokens, hidden_size]
|
||
target_hidden_states: torch.Tensor,
|
||
# [batch_size]
|
||
next_token_ids: torch.Tensor,
|
||
last_token_indices: Optional[torch.Tensor],
|
||
common_attn_metadata: CommonAttentionMetadata,
|
||
sampling_metadata: SamplingMetadata,
|
||
mm_embed_inputs: Optional[tuple[list[torch.Tensor],
|
||
torch.Tensor]] = None,
|
||
req_scheduled_tokens=None,
|
||
long_seq_metadata=None,
|
||
num_prefill_reqs=0,
|
||
num_decode_reqs=0,
|
||
scheduler_output: SchedulerOutput = None,
|
||
num_scheduled_tokens: int = 0,
|
||
) -> torch.Tensor:
|
||
|
||
num_tokens = target_token_ids.shape[0]
|
||
batch_size = next_token_ids.shape[0]
|
||
|
||
if last_token_indices is None:
|
||
last_token_indices = common_attn_metadata.query_start_loc[1:] - 1
|
||
|
||
if self.method == "eagle3":
|
||
assert isinstance(self.get_model(), Eagle3LlamaForCausalLM)
|
||
target_hidden_states = self.model.combine_hidden_states(
|
||
target_hidden_states)
|
||
assert target_hidden_states.shape[-1] == self.hidden_size
|
||
|
||
# Shift the input ids by one token.
|
||
# E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
|
||
self.input_ids[:num_tokens - 1] = target_token_ids[1:]
|
||
# Replace the last token with the next token.
|
||
# E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
|
||
self.input_ids[last_token_indices] = next_token_ids
|
||
|
||
if self.use_cuda_graph and \
|
||
num_tokens <= self.cudagraph_batch_sizes[-1]:
|
||
num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
|
||
else:
|
||
num_input_tokens = num_tokens
|
||
|
||
has_lora = len(self.runner.input_batch.lora_id_to_lora_request) > 0
|
||
if self.use_cuda_graph:
|
||
aclgraph_runtime_mode, batch_descriptor = \
|
||
self.runner.cudagraph_dispatcher.dispatch(num_tokens=num_input_tokens, uniform_decode=True, has_lora=has_lora)
|
||
else:
|
||
aclgraph_runtime_mode = CUDAGraphMode.NONE
|
||
batch_descriptor = None
|
||
|
||
# copy inputs to buffer for cudagraph
|
||
self.positions[:num_tokens] = target_positions
|
||
self.hidden_states[:num_tokens] = target_hidden_states
|
||
|
||
# FIXME(woosuk): The below two ops cause synchronization. Optimize.
|
||
builder = self.runner.attn_groups[0][0].get_metadata_builder()
|
||
attn_metadata = builder.build(0, common_attn_metadata,
|
||
self.runner.get_model())
|
||
# update global cos, sin
|
||
update_cos_sin(self.positions[:num_input_tokens])
|
||
per_layer_attn_metadata = {}
|
||
for layer_name in self.attn_layer_name:
|
||
per_layer_attn_metadata[layer_name] = attn_metadata
|
||
with set_ascend_forward_context(
|
||
per_layer_attn_metadata,
|
||
self.vllm_config,
|
||
num_tokens=num_input_tokens,
|
||
num_actual_tokens=num_tokens,
|
||
batch_descriptor=batch_descriptor,
|
||
aclgraph_runtime_mode=aclgraph_runtime_mode,
|
||
is_draft_model=True):
|
||
|
||
# The lifecycle of `input_ids`, `positions`, `hidden_states` runs through all speculative tokens' proposings.
|
||
# `model_input_ids`, `model_positions` and `model_hidden_states` are used to represent the inputs of speculative model.
|
||
model_input_ids = self.input_ids[:num_input_tokens]
|
||
model_positions = self.positions[:num_input_tokens]
|
||
model_hidden_states = self.hidden_states[:num_input_tokens]
|
||
|
||
if self.enable_shared_expert_dp:
|
||
# split hidden states along sequence dimension
|
||
# positions should not be split?
|
||
model_hidden_states = torch.ops.vllm.maybe_pad_and_reduce(
|
||
model_hidden_states)
|
||
# in acl-graph, `model_hidden_states` should be copy back to `self.hidden_states`?
|
||
|
||
last_hidden_states, hidden_states = self.model(
|
||
input_ids=model_input_ids,
|
||
positions=model_positions,
|
||
hidden_states=model_hidden_states,
|
||
)
|
||
forward_context = get_forward_context()
|
||
if forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL:
|
||
# TODO: support mla in future.
|
||
update_attn_params(
|
||
self.update_stream,
|
||
forward_context,
|
||
num_input_tokens,
|
||
self.vllm_config,
|
||
)
|
||
|
||
if self.enable_shared_expert_dp:
|
||
# merge hidden states along sequence dimension
|
||
last_hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
||
last_hidden_states.contiguous(), True)
|
||
hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
||
hidden_states.contiguous(), True)
|
||
|
||
sample_hidden_states = last_hidden_states[last_token_indices]
|
||
logits = self.model.compute_logits(sample_hidden_states)
|
||
draft_token_ids = logits.argmax(dim=-1)
|
||
|
||
# Early exit if there is only one draft token to be generated.
|
||
if self.num_speculative_tokens == 1:
|
||
# [batch_size, 1]
|
||
return draft_token_ids.view(-1, 1)
|
||
|
||
# Generate the remaining draft tokens.
|
||
draft_token_ids_tensor = torch.zeros(
|
||
(self.num_speculative_tokens, *draft_token_ids.shape),
|
||
dtype=draft_token_ids.dtype,
|
||
device=self.device)
|
||
draft_token_ids_tensor[0] = draft_token_ids
|
||
|
||
positions = target_positions[last_token_indices]
|
||
hidden_states = hidden_states[last_token_indices]
|
||
last_token_indices = self.arange[:batch_size]
|
||
|
||
if self.use_cuda_graph and \
|
||
batch_size <= self.cudagraph_batch_sizes[-1]:
|
||
input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
|
||
else:
|
||
input_batch_size = batch_size
|
||
|
||
attn_metadata.num_actual_tokens = batch_size
|
||
attn_metadata.max_query_len = 1
|
||
attn_metadata.query_start_loc = self.arange_cpu[:batch_size + 1]
|
||
attn_metadata.num_decodes, attn_metadata.num_prefills, attn_metadata.num_decode_tokens, attn_metadata.num_prefill_tokens = 0, batch_size, 0, batch_size
|
||
attn_metadata.num_actual_tokens_pcp_padded = attn_metadata.num_decode_tokens + attn_metadata.num_prefill_tokens
|
||
|
||
attn_metadata.actual_seq_lengths_q = attn_metadata.query_start_loc[
|
||
1:].tolist()
|
||
attn_metadata.seq_lens_list = attn_metadata.seq_lens.tolist()
|
||
attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
|
||
if self.use_cuda_graph:
|
||
aclgraph_runtime_mode, batch_descriptor = \
|
||
self.runner.cudagraph_dispatcher.dispatch(num_tokens=input_batch_size, uniform_decode=True, has_lora=has_lora)
|
||
else:
|
||
aclgraph_runtime_mode = CUDAGraphMode.NONE
|
||
batch_descriptor = None
|
||
for now_speculative in range(self.num_speculative_tokens - 1):
|
||
# Update the inputs.
|
||
# cast to int32 is crucial when eagle model is compiled.
|
||
# tensor.argmax() returns int64 by default.
|
||
input_ids = draft_token_ids_tensor[now_speculative]
|
||
positions += 1
|
||
|
||
# NOTE(woosuk): We should handle the case where the draft model
|
||
# generates tokens beyond the max model length. Since it is complex
|
||
# to remove such requests from the batch, we keep them in the batch
|
||
# but adjust the position ids and slot mappings to avoid the
|
||
# out-of-range access during the model execution. The draft tokens
|
||
# generated with this adjustment should be ignored.
|
||
exceeds_max_model_len = positions >= self.vllm_config.model_config.max_model_len
|
||
# Mask out the position ids that exceed the max model length.
|
||
# Otherwise, we may get out-of-range error in RoPE.
|
||
clamped_positions = torch.where(exceeds_max_model_len, 0,
|
||
positions)
|
||
|
||
# TODO: Increment the sequence lengths.
|
||
|
||
attn_metadata.seq_lens = attn_metadata.seq_lens + 1
|
||
attn_metadata.seq_lens_list = [
|
||
_ + 1 for _ in attn_metadata.seq_lens_list
|
||
]
|
||
# TODO: Consider max model length.
|
||
# attn_metadata.max_seq_len = min(attn_metadata.max_seq_len,
|
||
# self.max_model_len)
|
||
# For the requests that exceed the max model length, we set the
|
||
# TODO: sequence length to 1 to minimize their overheads in attention.
|
||
|
||
if self.attn_metadata_builder is None:
|
||
attn_metadata_builder = self._get_attention_metadata_builder()
|
||
else:
|
||
attn_metadata_builder = self.attn_metadata_builder
|
||
block_size = attn_metadata_builder.kv_cache_spec.block_size
|
||
|
||
# Compute the slot mapping.
|
||
block_numbers = (clamped_positions // block_size)
|
||
block_ids = attn_metadata.block_tables.gather(
|
||
dim=1, index=block_numbers.view(-1, 1))
|
||
block_ids = block_ids.view(-1)
|
||
slot_mapping_tmp = (block_ids * block_size +
|
||
clamped_positions % block_size)
|
||
|
||
# Mask out the slot mappings that exceed the max model length.
|
||
# Otherwise, the KV cache will be inadvertently updated with the
|
||
# padding tokens.
|
||
slot_mapping_tmp.masked_fill_(exceeds_max_model_len,
|
||
PADDING_SLOT_ID)
|
||
# NOTE: ASCEND slot_mapping must on cpu
|
||
attn_metadata.slot_mapping[:slot_mapping_tmp.shape[0]].copy_(
|
||
slot_mapping_tmp.to(torch.int32))
|
||
attn_metadata.slot_mapping[slot_mapping_tmp.shape[0]:].fill_(
|
||
PADDING_SLOT_ID)
|
||
# copy inputs to buffer for cudagraph
|
||
self.input_ids[:batch_size] = input_ids
|
||
self.positions[:batch_size] = clamped_positions
|
||
self.hidden_states[:batch_size] = hidden_states
|
||
attn_mask = self.attn_mask_builder.get_splitfuse_attn_mask()
|
||
|
||
attn_metadata.attn_mask = attn_mask
|
||
|
||
# update global cos, sin
|
||
update_cos_sin(self.positions[:input_batch_size])
|
||
|
||
# Run the model.
|
||
with set_ascend_forward_context(
|
||
per_layer_attn_metadata,
|
||
self.vllm_config,
|
||
num_tokens=input_batch_size,
|
||
num_actual_tokens=batch_size,
|
||
batch_descriptor=batch_descriptor,
|
||
aclgraph_runtime_mode=aclgraph_runtime_mode,
|
||
is_draft_model=True):
|
||
|
||
# The lifecycle of `input_ids`, `positions`, `hidden_states` runs through all speculative tokens' proposings.
|
||
# `model_input_ids`, `model_positions` and `model_hidden_states` are used to represent the inputs of speculative model.
|
||
model_input_ids = self.input_ids[:input_batch_size]
|
||
model_positions = self.positions[:input_batch_size]
|
||
model_hidden_states = self.hidden_states[:input_batch_size]
|
||
|
||
if self.enable_shared_expert_dp:
|
||
# split hidden states along sequence dimension
|
||
# positions should not be split？
|
||
model_hidden_states = torch.ops.vllm.maybe_pad_and_reduce(
|
||
model_hidden_states)
|
||
# in acl-graph, `model_hidden_states` should be copy back to `self.hidden_states`?
|
||
|
||
last_hidden_states, hidden_states = self.model(
|
||
input_ids=model_input_ids,
|
||
positions=model_positions,
|
||
hidden_states=model_hidden_states,
|
||
)
|
||
forward_context = get_forward_context()
|
||
if forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL:
|
||
update_attn_params(
|
||
self.update_stream,
|
||
forward_context,
|
||
input_batch_size,
|
||
self.vllm_config,
|
||
)
|
||
|
||
if self.enable_shared_expert_dp:
|
||
# merge hidden states along sequence dimension
|
||
last_hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
||
last_hidden_states.contiguous(), True)
|
||
hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
|
||
hidden_states.contiguous(), True)
|
||
|
||
hidden_states = hidden_states[:batch_size]
|
||
logits = self.model.compute_logits(last_hidden_states[:batch_size])
|
||
|
||
# TODO(wenlong): get more than one token for tree attention
|
||
draft_token_ids = logits.argmax(dim=-1)
|
||
draft_token_ids_tensor[now_speculative + 1] = draft_token_ids
|
||
|
||
# [batch_size, num_speculative_tokens]
|
||
draft_token_ids = draft_token_ids_tensor.swapaxes(0, 1)
|
||
return draft_token_ids
|
||
|
||
def prepare_next_token_ids_padded(
|
||
self,
|
||
common_attn_metadata: CommonAttentionMetadata,
|
||
sampled_token_ids: torch.Tensor,
|
||
requests: dict[str, CachedRequestState],
|
||
gpu_input_batch: InputBatch,
|
||
discard_request_indices: torch.Tensor,
|
||
num_discarded_requests: int,
|
||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||
"""
|
||
This function is used to prepare the inputs for speculative decoding.
|
||
It calculates the next token ids and the number of valid sampled tokens
|
||
for each request, considering the "discarded" requests whose next token
|
||
is not sampled and comes from `request.get_token_id()` instead.
|
||
It also accounts for the rejected tokens in `sampled_token_ids`.
|
||
This function must use device functions to operate on the inputs, and
|
||
should not introduce any blocking CPU-GPU synchronization.
|
||
"""
|
||
# TODO(Ben): Combine this into a custom fused kernel
|
||
|
||
# Precompute get_token_id for when there is no valid next token
|
||
num_reqs = gpu_input_batch.num_reqs
|
||
self.backup_next_token_ids.np[:num_reqs] = np.array([
|
||
requests[gpu_input_batch.req_ids[i]].get_token_id(
|
||
common_attn_metadata.seq_lens_cpu[i].item())
|
||
for i in range(num_reqs)
|
||
])
|
||
self.backup_next_token_ids.copy_to_gpu(num_reqs)
|
||
|
||
# Mask out the sampled tokens indices that should not be sampled.
|
||
discard_sampled_tokens_req_indices = discard_request_indices[:
|
||
num_discarded_requests]
|
||
|
||
valid_sampled_token_ids_gpu = sampled_token_ids.clone()
|
||
valid_sampled_token_ids_gpu.index_fill_(
|
||
0, discard_sampled_tokens_req_indices, -1)
|
||
|
||
# Generate a mask for all valid tokens within those requests
|
||
valid_mask = (valid_sampled_token_ids_gpu != -1) & (
|
||
valid_sampled_token_ids_gpu < gpu_input_batch.vocab_size)
|
||
|
||
# Count the number of valid tokens in each request
|
||
valid_sampled_tokens_count = valid_mask.sum(dim=1)
|
||
|
||
# Get the rightmost valid index per row
|
||
last_valid_indices = valid_sampled_tokens_count - 1
|
||
last_valid_indices_safe = torch.clamp(last_valid_indices, min=0)
|
||
|
||
# Get last valid token from each row
|
||
# (assume undefined state where there is no valid token)
|
||
selected_tokens = torch.gather(
|
||
valid_sampled_token_ids_gpu, 1,
|
||
last_valid_indices_safe.unsqueeze(1)).squeeze(1)
|
||
|
||
# Use last token if valid, pre-computed backup if not
|
||
batch_size = valid_sampled_token_ids_gpu.shape[0]
|
||
next_token_ids = torch.where(
|
||
last_valid_indices != -1,
|
||
selected_tokens,
|
||
self.backup_next_token_ids.gpu[:batch_size],
|
||
)
|
||
|
||
return next_token_ids, valid_sampled_tokens_count
|
||
|
||
def prepare_inputs(
|
||
self,
|
||
common_attn_metadata: CommonAttentionMetadata,
|
||
sampled_token_ids: list[list[int]],
|
||
num_draft_tokens: list[int],
|
||
) -> tuple[CommonAttentionMetadata, torch.Tensor]:
|
||
"""
|
||
This function is used to prepare the inputs for speculative decoding.
|
||
It updates to the common_attn_metadata to account for the rejected
|
||
tokens (and newly sampled tokens). It also returns the token indices
|
||
of the tokens that should be fed to the speculator.
|
||
"""
|
||
# E.g.
|
||
# common_attn_metadata.query_start_loc{_cpu}:
|
||
# [0, q1, q1 + q2, q1 + q2 + q3]
|
||
# common_attn_metadata.seq_lens{_cpu}: [s1, s2, s3]
|
||
# num_rejected_tokens: [n1, n2, n3]
|
||
# This function computes the intermediate values:
|
||
# num_tokens_per_req: [q1 - n1, q2 - n2, q3 - n3]
|
||
# And returns:
|
||
# common_attn_metadata.query_start_loc{_cpu}:
|
||
# [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
|
||
# common_attn_metadata.seq_lens{_cpu}:
|
||
# [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
|
||
# token_indices: [0, 1, ..., q1 - n1 - 1,
|
||
# q1, q1 + 1, ..., q1 + q2 - n2 - 1,
|
||
# q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
|
||
|
||
num_actual_reqs = len(num_draft_tokens)
|
||
num_rejected_tokens = [
|
||
n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
|
||
for i, n in enumerate(num_draft_tokens)
|
||
]
|
||
num_rejected_tokens = torch.tensor(num_rejected_tokens,
|
||
dtype=torch.int32)
|
||
|
||
device = common_attn_metadata.query_start_loc.device
|
||
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu[:
|
||
num_actual_reqs
|
||
+ 1]
|
||
seq_lens_cpu = common_attn_metadata.seq_lens_cpu[:num_actual_reqs]
|
||
new_seq_lens_cpu = seq_lens_cpu - num_rejected_tokens
|
||
|
||
# [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
|
||
new_query_len_per_req = query_start_loc_cpu[
|
||
1:] - query_start_loc_cpu[:-1]
|
||
# [q1, q2, q3] -> [q1 - n1, q2 - n2, q3 - n3]
|
||
new_num_tokens_per_req = new_query_len_per_req - num_rejected_tokens
|
||
new_num_tokens_per_req_np = new_num_tokens_per_req.numpy()
|
||
|
||
# [q1 - n1, q2 - n2, q3 - n3] ->
|
||
# [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
|
||
new_query_start_loc_cpu = torch.zeros(
|
||
query_start_loc_cpu.shape,
|
||
dtype=torch.int32,
|
||
pin_memory=is_pin_memory_available(),
|
||
)
|
||
new_query_start_loc_np = new_query_start_loc_cpu.numpy()
|
||
np.cumsum(new_num_tokens_per_req_np, out=new_query_start_loc_np[1:])
|
||
|
||
total_num_tokens = new_query_start_loc_np[-1]
|
||
# Example assuming num_tokens_per_req_np = [2, 4, 3]
|
||
# this implies that `new_query_start_locs` is:
|
||
# [0, 2, 6, 9] ->
|
||
# [0, 0, 2, 2, 2, 2, 6, 6, 6]
|
||
# _r1_ ____r2____ ___r3__
|
||
new_query_start_locs_expanded = np.repeat(new_query_start_loc_np[:-1],
|
||
new_num_tokens_per_req_np)
|
||
# [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
|
||
# [0, 1, 0, 1, 2, 3, 0, 1, 2]
|
||
# _r1_ ____r2____ ___r3__
|
||
token_offests = (self.token_arange_np[:total_num_tokens] -
|
||
new_query_start_locs_expanded)
|
||
|
||
# Expand starting positions to match token pattern
|
||
# [0, q1, q1 + q2] ->
|
||
# [0, 0, q1, q1, q1, q1, q1 + q2, q1 + q2, q1 + q2]
|
||
# _r1_ _____r2_______ ___________r3____________
|
||
old_query_start_locs_expanded = np.repeat(
|
||
query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np)
|
||
# Final token indices are:
|
||
# [0, 1, // req 1
|
||
# q1 + 0, q1 + 1, q1 + 2, q1 + 3, // req 2
|
||
# q1 + q2 + 0, q1 + q2 + 1, q1 + q2 + 2] // req 3
|
||
token_indices_np = token_offests + old_query_start_locs_expanded
|
||
token_indices = torch.from_numpy(token_indices_np).to(
|
||
device, non_blocking=True)
|
||
|
||
common_attn_metadata.slot_mapping[:token_indices.shape[0]].copy_(
|
||
common_attn_metadata.slot_mapping[token_indices])
|
||
common_attn_metadata.slot_mapping[token_indices.shape[0]:].fill_(-1)
|
||
|
||
# NOTE: Currently positions and seq_lens are not used in attn forward
|
||
# so we do not need to fixed them. But if they are used in the future,
|
||
# we should fixed them.
|
||
spec_common_attn_metadata = AscendCommonAttentionMetadata(
|
||
query_start_loc=new_query_start_loc_cpu.to(device,
|
||
non_blocking=True),
|
||
query_start_loc_cpu=new_query_start_loc_cpu,
|
||
seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
|
||
seq_lens_cpu=new_seq_lens_cpu,
|
||
num_computed_tokens_cpu=common_attn_metadata.
|
||
num_computed_tokens_cpu,
|
||
num_reqs=common_attn_metadata.num_reqs,
|
||
num_actual_tokens=total_num_tokens,
|
||
num_input_tokens=common_attn_metadata.num_input_tokens,
|
||
max_query_len=new_query_len_per_req.max().item(),
|
||
block_table_tensor=common_attn_metadata.block_table_tensor,
|
||
slot_mapping=common_attn_metadata.slot_mapping,
|
||
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
|
||
positions=common_attn_metadata.positions[token_indices],
|
||
attn_state=self.runner.attn_state,
|
||
decode_token_per_req=self.runner.decode_token_per_req,
|
||
max_seq_len=0)
|
||
return spec_common_attn_metadata, token_indices
|
||
|
||
def prepare_inputs_padded(
|
||
self,
|
||
common_attn_metadata: CommonAttentionMetadata,
|
||
spec_decode_metadata: SpecDecodeMetadata,
|
||
valid_sampled_tokens_count: torch.Tensor,
|
||
) -> tuple[CommonAttentionMetadata, torch.Tensor, torch.Tensor]:
|
||
"""
|
||
This function is used to prepare the inputs for speculative decoding
|
||
It updates the common_attn_metadata for speculative decoding,
|
||
but does not consider the rejected tokens. Instead, all tokens
|
||
are included as inputs to the speculator, with the rejected tokens
|
||
used as padding and filtered out later by `token_indices_to_sample`.
|
||
No blocking CPU operations should be introduced in this function.
|
||
"""
|
||
if HAS_TRITON:
|
||
num_reqs = common_attn_metadata.num_reqs
|
||
device = valid_sampled_tokens_count.device
|
||
|
||
token_indices_to_sample = torch.empty((num_reqs, ),
|
||
dtype=torch.int32,
|
||
device=device)
|
||
|
||
num_blocks_needed = triton.cdiv(num_reqs,
|
||
_PREPARE_INPUTS_BLOCK_SIZE)
|
||
num_vector_core = get_vectorcore_num()
|
||
grid_size = min(num_blocks_needed, num_vector_core)
|
||
grid = (grid_size, )
|
||
|
||
prepare_inputs_padded_kernel[grid](
|
||
spec_decode_metadata.cu_num_draft_tokens,
|
||
valid_sampled_tokens_count,
|
||
common_attn_metadata.query_start_loc,
|
||
token_indices_to_sample,
|
||
num_reqs,
|
||
BLOCK_SIZE=_PREPARE_INPUTS_BLOCK_SIZE,
|
||
)
|
||
else:
|
||
num_draft_tokens_gpu = torch.cat([
|
||
spec_decode_metadata.cu_num_draft_tokens[0:1],
|
||
spec_decode_metadata.cu_num_draft_tokens[1:] -
|
||
spec_decode_metadata.cu_num_draft_tokens[:-1],
|
||
])
|
||
|
||
num_rejected_tokens_gpu = torch.where(
|
||
num_draft_tokens_gpu > 0,
|
||
num_draft_tokens_gpu + 1 - valid_sampled_tokens_count,
|
||
torch.zeros_like(num_draft_tokens_gpu),
|
||
)
|
||
|
||
token_indices_to_sample = (
|
||
common_attn_metadata.query_start_loc[1:] - 1 -
|
||
num_rejected_tokens_gpu)
|
||
|
||
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
|
||
|
||
new_query_len_per_req = query_start_loc_cpu[
|
||
1:] - query_start_loc_cpu[:-1]
|
||
|
||
total_num_tokens = query_start_loc_cpu[-1].item()
|
||
token_indices = self.arange[:total_num_tokens]
|
||
|
||
# NOTE: Currently positions and seq_lens are not used in attn forward
|
||
# so we do not need to fixed them. But if they are used in the future,
|
||
# we should fixed them.
|
||
spec_common_attn_metadata = AscendCommonAttentionMetadata(
|
||
query_start_loc=common_attn_metadata.query_start_loc,
|
||
query_start_loc_cpu=query_start_loc_cpu,
|
||
seq_lens_cpu=common_attn_metadata.seq_lens_cpu,
|
||
num_reqs=common_attn_metadata.num_reqs,
|
||
num_actual_tokens=common_attn_metadata.num_actual_tokens
|
||
if self.pcp_size > 1 else total_num_tokens,
|
||
num_input_tokens=common_attn_metadata.num_input_tokens,
|
||
max_query_len=new_query_len_per_req.max().item(),
|
||
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
|
||
block_table_tensor=common_attn_metadata.block_table_tensor,
|
||
slot_mapping=common_attn_metadata.slot_mapping,
|
||
positions=common_attn_metadata.positions,
|
||
attn_state=self.runner.attn_state,
|
||
decode_token_per_req=self.runner.decode_token_per_req,
|
||
num_computed_tokens_cpu=common_attn_metadata.
|
||
num_computed_tokens_cpu,
|
||
seq_lens=common_attn_metadata.seq_lens,
|
||
max_seq_len=0)
|
||
|
||
return spec_common_attn_metadata, token_indices, token_indices_to_sample
|
||
|
||
def _split_pcp_input(self, req_scheduled_tokens, input_ids,
|
||
target_hidden_states):
|
||
"""
|
||
Split prefill input_ids and target_hidden_states in pcp group.
|
||
1. input_ids padding: [t0, t1, t2, t3, t4, t5] -> [t0, t1, t2, t3, t4, t5, pad, pad]
|
||
2. split input_ids: pcp0 [t0, t1, pad, pad], pcp1 [t2, t3, t4, t5]
|
||
3. split target_hidden_states (already include pcp padding):
|
||
[h0, h1, h2, h3, h4, h5, pad, pad] -> pcp0 [h0, h1, pad, pad], pcp1 [h2, h3, h4, h5]
|
||
4. also update max_query_len, seq_lens, cu_num_tokens according to pcp split.
|
||
"""
|
||
if len(req_scheduled_tokens) == 0:
|
||
# no prefill inputs to split, return empty result
|
||
return (
|
||
0,
|
||
torch.zeros([0], device='npu'),
|
||
torch.zeros([0, target_hidden_states.size(1)], device='npu'),
|
||
0,
|
||
torch.zeros([0]),
|
||
torch.tensor([0], dtype=torch.int32),
|
||
)
|
||
|
||
def _pcp_pad_and_split(num_tokens):
|
||
num_pcp_padded_scheduled_tokens = cdiv(
|
||
num_tokens, 2 * self.pcp_size) * 2 * self.pcp_size
|
||
pcp_pad = num_pcp_padded_scheduled_tokens - num_tokens
|
||
chunk_size = num_pcp_padded_scheduled_tokens // (2 * self.pcp_size)
|
||
|
||
# split position_ids (and use split position_ids to split input_ids afterwards)
|
||
req_position_cp: list[int] = []
|
||
req_position_cp.extend(
|
||
self.full_indices[self.pcp_rank *
|
||
chunk_size:(self.pcp_rank + 1) * chunk_size])
|
||
req_position_cp.extend(
|
||
self.full_indices[num_pcp_padded_scheduled_tokens -
|
||
(self.pcp_rank + 1) *
|
||
chunk_size:num_pcp_padded_scheduled_tokens -
|
||
self.pcp_rank * chunk_size])
|
||
|
||
return req_position_cp, num_pcp_padded_scheduled_tokens, pcp_pad
|
||
|
||
num_pcp_scheduled_tokens = []
|
||
ori_start_index = 0
|
||
pad_start_index = 0
|
||
pcp_split_input_ids_list = []
|
||
pcp_split_hidden_states_list = []
|
||
for ori_num_tokens in req_scheduled_tokens.values():
|
||
req_position_pcp, num_pcp_padded_scheduled_tokens, num_pcp_pad = \
|
||
_pcp_pad_and_split(ori_num_tokens)
|
||
actual_num_tokens = len(req_position_pcp)
|
||
num_pcp_scheduled_tokens.append(actual_num_tokens)
|
||
pad_input_ids = F.pad(
|
||
input_ids[ori_start_index:ori_start_index + ori_num_tokens],
|
||
(0, num_pcp_pad))
|
||
ori_start_index += ori_num_tokens
|
||
pcp_chunk_indices = [
|
||
pad_start_index + pos for pos in req_position_pcp
|
||
]
|
||
pcp_split_input_ids = pad_input_ids[req_position_pcp]
|
||
pcp_split_hidden_states = target_hidden_states[pcp_chunk_indices]
|
||
pcp_split_input_ids_list.append(pcp_split_input_ids)
|
||
pcp_split_hidden_states_list.append(pcp_split_hidden_states)
|
||
pad_start_index += num_pcp_padded_scheduled_tokens
|
||
num_tokens = sum(num_pcp_scheduled_tokens)
|
||
input_ids = torch.cat(pcp_split_input_ids_list)
|
||
target_hidden_states = torch.cat(pcp_split_hidden_states_list, dim=0)
|
||
max_query_len = max(num_pcp_scheduled_tokens)
|
||
seq_lens = torch.tensor(num_pcp_scheduled_tokens, dtype=torch.int32)
|
||
cu_num_tokens = torch.tensor(
|
||
np.insert(np.cumsum(np.array(num_pcp_scheduled_tokens)), 0, 0))
|
||
return num_tokens, input_ids, target_hidden_states, max_query_len, seq_lens, cu_num_tokens
|
||
|
||
# update full-graph params for one spec token
|
||
def _update_full_graph_params(self, forward_context, num_tokens):
|
||
if self.vllm_config.model_config.use_mla:
|
||
if self.pcp_size * self.dcp_size > 1:
|
||
update_mla_attn_dcp_pcp_params(self.update_stream,
|
||
forward_context, num_tokens)
|
||
else:
|
||
update_mla_attn_params(self.update_stream, forward_context,
|
||
num_tokens,
|
||
self.vllm_config.speculative_config)
|
||
else:
|
||
if self.pcp_size * self.dcp_size > 1:
|
||
update_attn_dcp_pcp_params(self.update_stream, forward_context,
|
||
num_tokens)
|
||
else:
|
||
update_attn_params(self.update_stream, forward_context,
|
||
num_tokens, self.vllm_config)
|