276 lines
11 KiB
Python
276 lines
11 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""PyTorch MAMBA model."""
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from typing import Iterable, Optional, Set, Tuple
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import torch
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from torch import nn
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from transformers import MambaConfig
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.distributed.parallel_state import get_pp_group
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.interfaces import (HasInnerState,
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IsAttentionFree, SupportsPP)
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from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
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MambaCacheParams)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from vllm.utils import LayerBlockType
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from .utils import (is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class MambaDecoderLayer(nn.Module):
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def __init__(self,
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config: MambaConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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is_lora_enabled: Optional[bool] = False) -> None:
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super().__init__()
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self.config = config
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self.is_falcon_mamba = config.model_type == "falcon_mamba"
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self.is_lora_enabled = is_lora_enabled
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mixer_rms_eps = config.mixer_rms_eps if self.is_falcon_mamba else None
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self.mixer = MambaMixer(hidden_size=config.hidden_size,
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ssm_state_size=config.state_size,
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conv_kernel_size=config.conv_kernel,
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intermediate_size=config.intermediate_size,
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time_step_rank=config.time_step_rank,
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use_conv_bias=config.use_conv_bias,
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use_bias=config.use_bias,
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use_rms_norm=self.is_falcon_mamba,
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rms_norm_has_weight=not self.is_falcon_mamba,
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rms_norm_eps=mixer_rms_eps,
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activation=config.hidden_act,
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is_lora_enabled=self.is_lora_enabled)
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self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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def forward(
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self,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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mamba_cache_params: MambaCacheParams,
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**kwargs,
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):
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if residual is None:
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residual = hidden_states
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hidden_states = self.norm(hidden_states)
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else:
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hidden_states, residual = self.norm(hidden_states, residual)
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hidden_states = self.mixer(hidden_states, mamba_cache_params)
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return hidden_states, residual
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class MambaModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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lora_config = vllm_config.lora_config
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is_lora_enabled = bool(lora_config)
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self.config = config
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self.padding_idx = config.pad_token_id
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lora_vocab = ((lora_config.lora_extra_vocab_size *
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(lora_config.max_loras or 1)) if lora_config else 0)
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self.vocab_size = config.vocab_size + lora_vocab
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self.org_vocab_size = config.vocab_size
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self.embeddings = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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)
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: MambaDecoderLayer(config,
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cache_config=cache_config,
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quant_config=quant_config,
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is_lora_enabled=is_lora_enabled),
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prefix=f"{prefix}.layers")
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self.norm_f = RMSNorm(config.hidden_size,
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eps=config.layer_norm_epsilon)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size))
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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mamba_cache_params: MambaCacheParams,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions=positions,
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hidden_states=hidden_states,
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residual=residual,
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mamba_cache_params=mamba_cache_params.at_layer_idx(
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i - self.start_layer))
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({
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"hidden_states": hidden_states,
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"residual": residual
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})
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hidden_states, _ = self.norm_f(hidden_states, residual)
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return hidden_states
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class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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lora_config = vllm_config.lora_config
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self.scheduler_config = vllm_config.scheduler_config
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assert not cache_config.enable_prefix_caching, \
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"Mamba does not support prefix caching"
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super().__init__()
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self.config = config
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self.vllm_config = vllm_config
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self.model_config = vllm_config.model_config
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self.backbone = MambaModel(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "backbone"))
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self.unpadded_vocab_size = config.vocab_size
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if lora_config:
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self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
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if config.tie_word_embeddings:
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self.lm_head = self.backbone.embeddings
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else:
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self.lm_head = ParallelLMHead(
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self.unpadded_vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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padding_size=DEFAULT_VOCAB_PADDING_SIZE
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# We need bigger padding if using lora for kernel
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# compatibility
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if not lora_config else lora_config.lora_vocab_padding_size,
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)
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# Used to track and store by the Mamba cache between steps.
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self.mamba_cache: Optional[MambaCacheManager] = None
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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config.vocab_size)
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self.sampler = get_sampler()
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self.make_empty_intermediate_tensors = (
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self.backbone.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.backbone.get_input_embeddings(input_ids)
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def forward(self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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**kwargs):
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if self.mamba_cache is None:
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num_mamba_layers = self.model_config.get_num_layers_by_block_type(
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self.vllm_config.parallel_config, LayerBlockType.mamba)
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self.mamba_cache = MambaCacheManager(
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self.vllm_config, self.lm_head.weight.dtype, num_mamba_layers,
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*self._get_mamba_cache_shape())
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mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
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hidden_states = self.backbone(input_ids, positions, mamba_cache_params,
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intermediate_tensors, inputs_embeds)
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return hidden_states
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def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
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return self.mamba_cache.copy_inputs_before_cuda_graphs(
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input_buffers, **kwargs)
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def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
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return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
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def _get_mamba_cache_shape(
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self) -> Tuple[Tuple[int, int], Tuple[int, int]]:
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world_size = get_tensor_model_parallel_world_size()
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conv_state_shape = (
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self.config.intermediate_size // world_size,
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self.config.conv_kernel - 1,
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)
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temporal_state_shape = (
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self.config.intermediate_size // world_size,
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self.config.state_size,
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)
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return conv_state_shape, temporal_state_shape
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def compute_logits(self, hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> torch.Tensor:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: Optional[torch.Tensor],
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str,
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torch.Tensor]]) -> Set[str]:
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params_dict = dict(self.named_parameters())
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loaded_params: Set[str] = set()
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for name, loaded_weight in weights:
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if "A_log" in name:
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name = name.replace("A_log", "A")
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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