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542 lines
21 KiB
Python
542 lines
21 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Inference-only Bamba model."""
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# Added by the IBM Team, 2024
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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 BambaConfig
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from vllm.attention.layer import Attention
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import divide, 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.forward_context import get_forward_context
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.mamba.mamba2_metadata import (
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Mamba2Metadata, prepare_mamba2_metadata)
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from vllm.model_executor.layers.mamba.mamba_mixer2 import (
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MambaMixer2, extra_groups_for_head_shards)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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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.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 .interfaces import (HasInnerState, IsHybrid, SupportsLoRA, SupportsPP,
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SupportsQuant, SupportsV0Only)
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from .utils import (AutoWeightsLoader, 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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class BambaMLP(nn.Module):
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def __init__(
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self,
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config: BambaConfig,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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input_size=config.hidden_size,
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output_sizes=[config.intermediate_size] * 2,
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bias=bias,
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quant_config=quant_config,
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)
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self.down_proj = RowParallelLinear(
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input_size=config.intermediate_size,
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output_size=config.hidden_size,
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bias=bias,
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quant_config=quant_config,
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)
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if config.hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {config.hidden_act}. "
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"Only silu is supported for now.")
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self.act_fn = SiluAndMul()
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def forward(self, x):
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x, _ = self.gate_up_proj(x)
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x = self.act_fn(x)
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x, _ = self.down_proj(x)
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return x
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class BambaMixerDecoderLayer(nn.Module):
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def __init__(self,
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config: BambaConfig,
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layer_idx: int,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "") -> None:
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super().__init__()
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self.config = config
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self.mamba = MambaMixer2(hidden_size= config.hidden_size,
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ssm_state_size = config.mamba_d_state,
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conv_kernel_size = config.mamba_d_conv,
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intermediate_size = config.mamba_expand *\
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config.hidden_size,
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use_conv_bias = config.mamba_conv_bias,
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use_bias = config.mamba_proj_bias,
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n_groups=config.mamba_n_groups,
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num_heads=config.mamba_n_heads,
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head_dim=config.mamba_d_head,
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rms_norm_eps=config.rms_norm_eps,
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activation=config.hidden_act,
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quant_config=quant_config)
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self.feed_forward = BambaMLP(config, quant_config=quant_config)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.pre_ff_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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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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mamba2_metadata: Mamba2Metadata,
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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.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.mamba(hidden_states, mamba_cache_params,
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mamba2_metadata)
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# Fully Connected
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hidden_states, residual = self.pre_ff_layernorm(
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hidden_states, residual)
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hidden_states = self.feed_forward(hidden_states)
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return hidden_states, residual
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class BambaAttentionDecoderLayer(nn.Module):
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def __init__(
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self,
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config: BambaConfig,
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layer_idx: int,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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8192)
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = config.num_key_value_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = config.hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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if hasattr(config, "partial_rotary_factor"):
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rotary_dim = self.head_dim * config.partial_rotary_factor
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elif hasattr(config, "attn_rotary_emb"):
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rotary_dim = config.attn_rotary_emb # for backward compatibility
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else:
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rotary_dim = self.head_dim # default
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self.rotary_emb = get_rope(
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head_size=self.head_dim,
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rotary_dim=rotary_dim,
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max_position=max_position_embeddings,
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rope_scaling=rope_scaling,
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base=rope_theta,
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is_neox_style=True,
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dtype=torch.get_default_dtype(), # see impl of get_rope
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)
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self.qkv_proj = QKVParallelLinear(
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config.hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=False,
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quant_config=quant_config,
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)
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self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
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config.hidden_size,
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bias=False,
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quant_config=quant_config)
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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prefix=f"{prefix}.attn",
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)
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self.feed_forward = BambaMLP(config, quant_config=quant_config)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.pre_ff_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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def self_attention(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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**kwargs,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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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.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.self_attention(
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positions=positions,
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hidden_states=hidden_states,
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)
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# Fully Connected
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hidden_states, residual = self.pre_ff_layernorm(
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hidden_states, residual)
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hidden_states = self.feed_forward(hidden_states)
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return hidden_states, residual
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ALL_DECODER_LAYER_TYPES = {
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"attention": BambaAttentionDecoderLayer,
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"mamba": BambaMixerDecoderLayer
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}
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class BambaModel(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: BambaConfig = 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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self.config = config
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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.embed_tokens = 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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def get_layer(prefix: str):
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layer_idx = int(prefix.rsplit(".", 1)[1])
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layer_class = ALL_DECODER_LAYER_TYPES[
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config.layers_block_type[layer_idx]]
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return layer_class(
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config,
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layer_idx,
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cache_config,
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quant_config=quant_config,
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prefix=prefix,
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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, get_layer, prefix=f"{prefix}.layers")
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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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self.final_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(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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attn_metadata = get_forward_context().attn_metadata
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mamba2_metadata = prepare_mamba2_metadata(
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chunk_size=self.config.mamba_chunk_size,
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attn_metadata=attn_metadata,
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)
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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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residual = None
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num_attn = 0
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for i in range(len(self.layers)):
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layer = self.layers[i]
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if isinstance(layer, BambaAttentionDecoderLayer):
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num_attn += 1
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layer_mamba_cache_params = None
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if isinstance(layer, BambaMixerDecoderLayer):
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layer_mamba_cache_params = mamba_cache_params.at_layer_idx(
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i - num_attn)
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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=layer_mamba_cache_params,
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mamba2_metadata=mamba2_metadata,
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)
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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.final_layernorm(hidden_states, residual)
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return hidden_states
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def load_weights(self, weights: Iterable[Tuple[str,
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torch.Tensor]]) -> Set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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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 "rotary_emb.inv_freq" in name:
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continue
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if "A_log" in name:
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name = name.replace("A_log", "A")
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if ".self_attn." in name:
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name = name.replace(".self_attn", "")
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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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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# Skip layers on other devices.
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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 = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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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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class BambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
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IsHybrid, SupportsV0Only, SupportsQuant):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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"gate_up_proj": ["up_proj", "down_proj"]
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}
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# LoRA specific attributes
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embedding_modules = {
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"embed_tokens": "input_embeddings",
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"lm_head": "output_embeddings",
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}
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embedding_padding_modules = ["lm_head"]
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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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self.vllm_config = vllm_config
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self.model_config = vllm_config.model_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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scheduler_config = vllm_config.scheduler_config
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assert not cache_config.enable_prefix_caching, \
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"Bamba currently does not support prefix caching"
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self.quant_config = vllm_config.quant_config
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super().__init__()
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self.config = config
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self.scheduler_config = scheduler_config
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self.model = BambaModel(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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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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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.make_empty_intermediate_tensors = (
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self.model.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.model.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:
|
|
|
|
num_mamba_layers = self.model_config.get_num_layers_by_block_type(
|
|
self.vllm_config.parallel_config, LayerBlockType.mamba)
|
|
|
|
self.mamba_cache = MambaCacheManager(
|
|
self.vllm_config, self.lm_head.weight.dtype, num_mamba_layers,
|
|
*self._get_mamba_cache_shape())
|
|
mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
|
|
hidden_states = self.model(input_ids, positions, mamba_cache_params,
|
|
intermediate_tensors, inputs_embeds)
|
|
|
|
return hidden_states
|
|
|
|
def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
|
|
return self.mamba_cache.copy_inputs_before_cuda_graphs(
|
|
input_buffers, **kwargs)
|
|
|
|
def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
|
|
return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
|
|
|
|
def _get_mamba_cache_shape(
|
|
self) -> Tuple[Tuple[int, int], Tuple[int, int]]:
|
|
world_size = get_tensor_model_parallel_world_size()
|
|
hidden_size = self.config.hidden_size
|
|
|
|
conv_state_shape, temporal_state_shape = None, None
|
|
|
|
intermediate_size = self.config.mamba_expand * hidden_size
|
|
|
|
# if n_groups is not divisible by world_size, need to extend the shards
|
|
# to ensure all groups needed by a head is sharded along with it
|
|
n_groups = (self.config.mamba_n_groups + extra_groups_for_head_shards(
|
|
self.config.mamba_n_groups, world_size))
|
|
|
|
# - heads and n_groups are TP-ed
|
|
conv_dim = (intermediate_size +
|
|
2 * n_groups * self.config.mamba_d_state)
|
|
conv_state_shape = (
|
|
divide(conv_dim, world_size),
|
|
self.config.mamba_d_conv - 1,
|
|
)
|
|
|
|
# These are not TP-ed as they depend on A, dt_bias, D
|
|
# - they are typically small
|
|
# e.g., (h_heads, d_head, d_state) = (128, 64, 128)
|
|
temporal_state_shape = (
|
|
divide(self.config.mamba_n_heads, world_size),
|
|
self.config.mamba_d_head,
|
|
self.config.mamba_d_state,
|
|
)
|
|
return conv_state_shape, temporal_state_shape
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|