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519 lines
18 KiB
Python
519 lines
18 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Inference-only Bamba model."""
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# Added by the IBM Team, 2024
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from collections.abc import Iterable
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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.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, ModelConfig, 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.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 (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2
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from vllm.model_executor.layers.mamba.mamba_utils import (
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MambaStateDtypeCalculator,
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MambaStateShapeCalculator,
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)
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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,
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.sequence import IntermediateTensors
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from .interfaces import HasInnerState, IsHybrid, SupportsLoRA, SupportsPP, SupportsQuant
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from .utils import (
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AutoWeightsLoader,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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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: QuantizationConfig | None = 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(
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f"Unsupported activation: {config.hidden_act}. "
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"Only silu is supported for now."
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)
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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__(
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self,
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config: BambaConfig,
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layer_idx: int,
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model_config: ModelConfig | None = None,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.mamba = MambaMixer2(
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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 * 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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model_config=model_config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.mixer",
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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, eps=config.rms_norm_eps)
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self.pre_ff_layernorm = RMSNorm(config.hidden_size, 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: torch.Tensor | None,
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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(hidden_states, residual)
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output = torch.empty_like(hidden_states)
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self.mamba(hidden_states, output)
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# Fully Connected
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hidden_states, residual = self.pre_ff_layernorm(output, 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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model_config: ModelConfig | None = None,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = 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", 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 = int(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(
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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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)
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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, eps=config.rms_norm_eps)
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self.pre_ff_layernorm = RMSNorm(config.hidden_size, 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: torch.Tensor | None,
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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(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(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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@support_torch_compile
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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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model_config = vllm_config.model_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 = (
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(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
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if lora_config
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else 0
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)
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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[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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model_config,
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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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)
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size
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)
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self.final_layernorm = RMSNorm(config.hidden_size, 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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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = 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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residual = None
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for i, layer in enumerate(self.layers):
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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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)
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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, "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, 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", 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(
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nn.Module, HasInnerState, SupportsLoRA, SupportsPP, IsHybrid, SupportsQuant
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):
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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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@classmethod
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def get_mamba_state_dtype_from_config(
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cls,
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vllm_config: "VllmConfig",
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) -> tuple[torch.dtype, torch.dtype]:
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return MambaStateDtypeCalculator.mamba2_state_dtype(
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vllm_config.model_config.dtype,
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vllm_config.cache_config.mamba_cache_dtype,
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vllm_config.cache_config.mamba_ssm_cache_dtype,
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)
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@classmethod
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def get_mamba_state_shape_from_config(
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cls,
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vllm_config: "VllmConfig",
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) -> tuple[tuple[int, int], tuple[int, int, int]]:
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"""Calculate shapes for Mamba's convolutional and state caches.
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Args:
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vllm_config: vLLM config
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Returns:
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Tuple containing:
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- conv_state_shape: Shape for convolutional state cache
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- temporal_state_shape: Shape for state space model cache
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"""
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parallel_config = vllm_config.parallel_config
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hf_config = vllm_config.model_config.hf_config
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intermediate_size = hf_config.mamba_expand * hf_config.hidden_size
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return MambaStateShapeCalculator.mamba2_state_shape(
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intermediate_size=intermediate_size,
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tp_world_size=parallel_config.tensor_parallel_size,
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n_groups=hf_config.mamba_n_groups,
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num_heads=hf_config.mamba_n_heads,
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head_dim=hf_config.mamba_d_head,
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state_size=hf_config.mamba_d_state,
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conv_kernel=hf_config.mamba_d_conv,
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)
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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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lora_config = vllm_config.lora_config
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scheduler_config = vllm_config.scheduler_config
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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(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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)
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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,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
|
|
self.logits_processor = LogitsProcessor(
|
|
self.unpadded_vocab_size, config.vocab_size
|
|
)
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors
|
|
)
|
|
|
|
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.get_input_embeddings(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
**kwargs,
|
|
):
|
|
hidden_states = self.model(
|
|
input_ids, positions, intermediate_tensors, inputs_embeds
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
logits = self.logits_processor(self.lm_head, hidden_states)
|
|
return logits
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|