[Model] Support Mamba (#6484)
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Tyler Michael Smith
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vllm/model_executor/models/mamba.py
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499
vllm/model_executor/models/mamba.py
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# coding=utf-8
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"""PyTorch MAMBA model."""
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from dataclasses import dataclass
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from typing import Iterable, List, Optional, 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.attention.backends.abstract import AttentionMetadata
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from vllm.config import CacheConfig, LoRAConfig, SchedulerConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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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 (ColumnParallelLinear,
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MergedColumnParallelLinear,
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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.ops.causal_conv1d import (
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causal_conv1d_fn, causal_conv1d_update)
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from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
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selective_scan_fn, selective_state_update)
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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 Sampler, SamplerOutput
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import (
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composed_weight_loader, default_weight_loader, sharded_weight_loader)
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from vllm.model_executor.models.interfaces import (HasInnerState,
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IsAttentionFree)
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from vllm.model_executor.models.mamba_cache import MambaCacheManager
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.sequence import IntermediateTensors
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from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
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_get_graph_batch_size)
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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@dataclass
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class MambaCacheParams:
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is_prompt: bool = False
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conv_state: torch.Tensor = torch.Tensor()
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ssm_state: torch.Tensor = torch.Tensor()
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# Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
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class MambaMixer(nn.Module):
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"""
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Compute ∆, A, B, C, and D the state space parameters and compute
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the `contextualized_states`. A, D are input independent
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(see Mamba paper [1] Section 3.5.2 "Interpretation of A"
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for why A isn't selective) ∆, B, C are input-dependent
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(this is a key difference between Mamba and the linear time
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invariant S4, and is why Mamba is called
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**selective** state spaces)
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"""
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def __init__(self, config: MambaConfig, layer_idx):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.hidden_size = config.hidden_size
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self.ssm_state_size = config.state_size
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self.conv_kernel_size = config.conv_kernel
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self.intermediate_size = config.intermediate_size
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self.time_step_rank = int(config.time_step_rank)
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self.conv1d = ColumnParallelLinear(
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input_size=self.conv_kernel_size,
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output_size=self.intermediate_size,
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bias=config.use_conv_bias,
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)
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# unsqueeze to fit conv1d weights shape into the linear weights shape.
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# Can't do this in `weight_loader` since it already exists in
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# `ColumnParallelLinear` and `set_weight_attrs`
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# doesn't allow to override it
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self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
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self.in_proj = MergedColumnParallelLinear(self.hidden_size,
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[self.intermediate_size] * 2,
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bias=config.use_bias)
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# selective projection used to make dt, B and C input dependent
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self.x_proj = RowParallelLinear(
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self.intermediate_size,
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self.time_step_rank + self.ssm_state_size * 2,
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bias=False,
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)
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# time step projection (discretization) -
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# In the forward we need to apply dt_proj without the bias,
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# as the bias is added in the selective scan kernel.
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self.dt_proj = ColumnParallelLinear(self.time_step_rank,
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self.intermediate_size,
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bias=True,
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skip_bias_add=True)
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tp_size = get_tensor_model_parallel_world_size()
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self.A = nn.Parameter(
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torch.empty(
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self.intermediate_size // tp_size,
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self.ssm_state_size,
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dtype=torch.float32,
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))
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self.D = nn.Parameter(torch.ones(self.intermediate_size // tp_size))
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set_weight_attrs(self.D, {"weight_loader": sharded_weight_loader(0)})
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a_weight_loader = composed_weight_loader(
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sharded_weight_loader(0), lambda x: -torch.exp(x.float()))
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set_weight_attrs(self.A, {"weight_loader": a_weight_loader})
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self.out_proj = RowParallelLinear(
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self.intermediate_size,
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self.hidden_size,
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bias=config.use_bias,
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input_is_parallel=True,
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)
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self.activation = config.hidden_act
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def forward(self, hidden_states: torch.Tensor,
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attn_metadata: AttentionMetadata, conv_state: torch.Tensor,
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ssm_state: torch.Tensor):
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# 1. Gated MLP's linear projection
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projected_states = self.in_proj(hidden_states)[0].transpose(-2, -1)
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hidden_states, gate = projected_states.chunk(2, dim=-2)
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# 2. Convolution sequence transformation
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conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0),
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self.conv1d.weight.size(2))
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if attn_metadata.query_start_loc is not None \
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and attn_metadata.context_lens_tensor is not None:
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# |---------- N-1 iteration --------|
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# |---------------- N iteration ---------------------|
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# |- tokenA -|......................|-- newTokens ---|
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# |---------- context_len ----------|
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# |-------------------- seq_len ---------------------|
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# |-- query_len ---|
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hidden_states = causal_conv1d_fn(
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hidden_states,
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conv_weights,
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self.conv1d.bias,
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activation=self.activation,
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conv_states=conv_state,
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has_initial_state=attn_metadata.context_lens_tensor > 0,
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query_start_loc=attn_metadata.query_start_loc)
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else:
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hidden_states = causal_conv1d_update(
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hidden_states.transpose(0, 1),
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conv_state,
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conv_weights,
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self.conv1d.bias,
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self.activation,
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)
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hidden_states = hidden_states.transpose(0, 1)
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# 3. State Space Model sequence transformation
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# 3.a. input varying initialization of time_step, B and C
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ssm_parameters = self.x_proj(hidden_states.transpose(-2, -1))[0]
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time_step, B, C = torch.split(
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ssm_parameters,
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[self.time_step_rank, self.ssm_state_size, self.ssm_state_size],
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dim=-1,
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)
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# Note that Jamba normalizes B, C, and time_step here but Mamba doesn't.
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discrete_time_step = self.dt_proj(time_step)[0].transpose(-2, -1)
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# 3.c perform the recurrence y ← SSM(A, B, C)(x)
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time_proj_bias = (self.dt_proj.bias.float() if hasattr(
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self.dt_proj, "bias") else None)
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if attn_metadata.query_start_loc is not None \
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and attn_metadata.context_lens_tensor is not None:
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scan_outputs = selective_scan_fn(
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hidden_states,
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ssm_state,
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discrete_time_step,
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self.A,
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B.transpose(-2, -1),
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C.transpose(-2, -1),
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self.D.float(),
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gate,
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time_proj_bias,
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delta_softplus=True,
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has_initial_state=attn_metadata.context_lens_tensor > 0,
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query_start_loc=attn_metadata.query_start_loc)
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else:
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scan_outputs = selective_state_update(
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ssm_state,
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hidden_states.transpose(0, 1),
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discrete_time_step.transpose(0, 1),
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self.A,
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B,
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C,
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self.D,
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gate.transpose(0, 1),
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time_proj_bias,
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dt_softplus=True,
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)
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scan_outputs = scan_outputs.transpose(0, 1)
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# 4. Final linear projection
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contextualized_states = self.out_proj(scan_outputs.transpose(-2,
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-1))[0]
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return contextualized_states
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class MambaMLP(nn.Module):
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def __init__(
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self,
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config: MambaConfig,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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hidden_size = config.hidden_size
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intermediate_size = config.intermediate_size
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hidden_act = config.hidden_act
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size, [intermediate_size] * 2,
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bias=False,
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quant_config=quant_config)
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self.down_proj = RowParallelLinear(intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config)
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if hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {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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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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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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layer_idx: int,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None) -> None:
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super().__init__()
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self.layer_idx = layer_idx
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self.config = config
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self.mixer = MambaMixer(config, layer_idx)
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self.feed_forward = MambaMLP(config, quant_config=quant_config)
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self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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self.pre_ff_layernorm = RMSNorm(config.hidden_size,
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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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attn_metadata: AttentionMetadata,
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residual: Optional[torch.Tensor],
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conv_state: torch.Tensor,
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ssm_state: 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.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, attn_metadata, conv_state,
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ssm_state)
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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 MambaModel(nn.Module):
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def __init__(
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self,
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config: MambaConfig,
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quant_config: Optional[QuantizationConfig] = None,
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cache_config: Optional[CacheConfig] = None,
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lora_config: Optional[LoRAConfig] = None,
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) -> None:
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super().__init__()
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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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decoder_layers = []
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for i in range(config.num_hidden_layers):
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decoder_layers.append(
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MambaDecoderLayer(config,
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layer_idx=i,
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cache_config=cache_config,
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quant_config=quant_config))
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self.layers = nn.ModuleList(decoder_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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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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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conv_state: torch.Tensor,
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ssm_state: torch.Tensor,
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) -> torch.Tensor:
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hidden_states = self.embeddings(input_ids)
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residual = None
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for i in range(len(self.layers)):
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layer = self.layers[i]
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current_ssm_state = ssm_state[i]
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current_conv_state = conv_state[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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attn_metadata=attn_metadata,
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residual=residual,
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conv_state=current_conv_state,
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ssm_state=current_ssm_state,
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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):
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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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}
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# LoRA specific attributes
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supported_lora_modules = [
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"qkv_proj",
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"o_proj",
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"embed_tokens",
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"lm_head",
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]
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embedding_modules = {
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"embeddings": "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__(
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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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lora_config: Optional[LoRAConfig] = None,
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scheduler_config: Optional[SchedulerConfig] = None,
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) -> None:
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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.scheduler_config = scheduler_config
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self.backbone = MambaModel(config,
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cache_config=cache_config,
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quant_config=quant_config,
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lora_config=lora_config)
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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 = self.backbone.embeddings
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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 = Sampler()
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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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kv_caches: List[KVCache],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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**kwargs):
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if self.mamba_cache is None:
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max_batch_size = (_get_graph_batch_size(
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self.scheduler_config.max_num_seqs) if self.scheduler_config
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else max(_BATCH_SIZES_TO_CAPTURE) + 2)
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self.mamba_cache = MambaCacheManager(
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self.lm_head.weight.dtype, self.config.num_hidden_layers,
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max_batch_size, *self._get_mamba_cache_shape())
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mamba_cache_tensors = self.mamba_cache.current_run_tensors(
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input_ids, attn_metadata, **kwargs)
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hidden_states = self.backbone(input_ids, positions, kv_caches,
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attn_metadata, mamba_cache_tensors[0],
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mamba_cache_tensors[1])
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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,
|
||||
sampling_metadata)
|
||||
return logits
|
||||
|
||||
def sample(
|
||||
self,
|
||||
logits: Optional[torch.Tensor],
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> Optional[SamplerOutput]:
|
||||
next_tokens = self.sampler(logits, sampling_metadata)
|
||||
return next_tokens
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
if "A_log" in name:
|
||||
name = name.replace("A_log", "A")
|
||||
|
||||
if ".self_attn." in name:
|
||||
name = name.replace(".self_attn", "")
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
Reference in New Issue
Block a user