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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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+
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+import torch
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+from torch import nn
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+from torch.nn.parameter import Parameter
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+from transformers import MambaConfig
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+
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+from aphrodite.attention.backends.abstract import AttentionMetadata
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+from aphrodite.common.config import CacheConfig, LoRAConfig, SchedulerConfig
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+from aphrodite.common.sequence import IntermediateTensors, SamplerOutput
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+from aphrodite.distributed import (get_tensor_model_parallel_rank,
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+ get_tensor_model_parallel_world_size)
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+from aphrodite.modeling.layers.activation import SiluAndMul
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+from aphrodite.modeling.layers.layernorm import RMSNorm
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+from aphrodite.modeling.layers.linear import (ColumnParallelLinear,
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+ MergedColumnParallelLinear,
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+ RowParallelLinear)
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+from aphrodite.modeling.layers.logits_processor import LogitsProcessor
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+from aphrodite.modeling.layers.mamba.ops.causal_conv1d import (
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+ causal_conv1d_fn, causal_conv1d_update)
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+from aphrodite.modeling.layers.mamba.ops.mamba_ssm import (
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+ selective_scan_fn, selective_state_update)
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+from aphrodite.modeling.layers.sampler import Sampler
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+from aphrodite.modeling.layers.vocab_parallel_embedding import (
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+ VocabParallelEmbedding)
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+from aphrodite.modeling.model_loader.weight_utils import default_weight_loader
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+from aphrodite.modeling.models.interfaces import HasInnerState
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+from aphrodite.modeling.models.mamba_cache import MambaCacheManager
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+from aphrodite.modeling.sampling_metadata import SamplingMetadata
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+from aphrodite.modeling.utils import set_weight_attrs
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+from aphrodite.quantization.base_config import QuantizationConfig
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+from aphrodite.task_handler.model_runner import (_BATCH_SIZES_TO_CAPTURE,
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+ _get_graph_batch_size)
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+
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+KVCache = Tuple[torch.Tensor, torch.Tensor]
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+
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+
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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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+
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+
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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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+
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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.use_conv_bias = config.use_conv_bias
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+
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+ # TODO: ??
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+ #self.use_bias = config.mamba_proj_bias
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+ self.use_bias = False
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+
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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=self.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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+
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+ self.in_proj = MergedColumnParallelLinear(self.hidden_size,
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+ [self.intermediate_size] * 2,
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+ bias=self.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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+
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+ def weight_loader(param: Parameter, loaded_weight: torch.Tensor):
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+ tp_rank = get_tensor_model_parallel_rank()
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+ tp_size = get_tensor_model_parallel_world_size()
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+ param.data.copy_(
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+ loaded_weight.data.split(loaded_weight.shape[0] // tp_size,
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+ dim=0)[tp_rank])
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+
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+ def A_weight_loader(param: Parameter, loaded_weight: torch.Tensor):
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+ weight_loader(param, -torch.exp(loaded_weight.float()))
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+
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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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+
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+ set_weight_attrs(self.D, {"weight_loader": weight_loader})
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+ set_weight_attrs(self.A, {"weight_loader": A_weight_loader})
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+
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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=self.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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+
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+ def mamba_forward(self,
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+ hidden_states: torch.Tensor,
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+ cache_params: MambaCacheParams = None):
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+
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+ # 1. Gated MLP's linear projection
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+ projected_states = self.in_proj(hidden_states)[0].transpose(1, 2)
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+ hidden_states, gate = projected_states.chunk(2, dim=1)
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+
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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 cache_params is not None and not cache_params.is_prompt:
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+ hidden_states = causal_conv1d_update(
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+ hidden_states.squeeze(-1),
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+ cache_params.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.unsqueeze(-1)
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+ else:
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+ if cache_params is not None:
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+ conv_states = nn.functional.pad(
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+ hidden_states,
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+ (self.conv_kernel_size - hidden_states.shape[-1], 0))
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+ cache_params.conv_state.copy_(conv_states)
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+
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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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+ )
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+
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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(1, 2))[0]
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+
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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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+
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+ # Note that Jamba normalizes B, C, and time_step here but Mamba doesn't.
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+
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+ discrete_time_step = self.dt_proj(time_step)[0].transpose(1, 2)
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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 cache_params is not None and not cache_params.is_prompt:
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+ scan_outputs = selective_state_update(
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+ cache_params.ssm_state,
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+ hidden_states[..., 0],
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+ discrete_time_step[..., 0],
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+ self.A,
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+ B[:, 0],
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+ C[:, 0],
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+ self.D,
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+ gate[..., 0],
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+ time_proj_bias,
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+ dt_softplus=True,
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+ ).unsqueeze(-1)
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+ else:
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+ scan_outputs, ssm_state = selective_scan_fn(
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+ hidden_states,
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+ discrete_time_step,
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+ self.A,
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+ B.transpose(1, 2),
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+ C.transpose(1, 2),
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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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+ return_last_state=True,
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+ )
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+ if ssm_state is not None and cache_params is not None:
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+ cache_params.ssm_state.copy_(ssm_state)
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+
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+ # 4. Final linear projection
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+ contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))[0]
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+ return contextualized_states
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+
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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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+ conv_state: torch.Tensor,
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+ ssm_state: torch.Tensor,
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+ ):
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+ if attn_metadata.prefill_metadata is not None:
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+ offset = 0
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+ for i, prompt_len in enumerate(
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+ attn_metadata.prefill_metadata.seq_lens):
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+ cache = MambaCacheParams(True,
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+ conv_state=conv_state[i].unsqueeze(0),
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+ ssm_state=ssm_state[i].unsqueeze(0))
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+ hidden_states[offset:offset + prompt_len].copy_(
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+ self.mamba_forward(hidden_states[offset:offset +
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+ prompt_len].unsqueeze(0),
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+ cache_params=cache)[0])
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+ offset += prompt_len
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+ else:
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+ cache = MambaCacheParams(False,
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+ conv_state=conv_state,
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+ ssm_state=ssm_state)
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+ hidden_states = self.mamba_forward(hidden_states.unsqueeze(1),
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+ cache_params=cache)
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+ hidden_states = hidden_states.squeeze(1)
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+
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+ return hidden_states
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+
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+
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+class MambaMLP(nn.Module):
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+
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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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+
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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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+
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+
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+class MambaDecoderLayer(nn.Module):
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+
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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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+
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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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+
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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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+
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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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+
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+
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+class MambaModel(nn.Module):
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ return hidden_states
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+
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+
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+class MambaForCausalLM(nn.Module, HasInnerState):
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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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+ ],
|
|
|
+ }
|
|
|
+
|
|
|
+ # LoRA specific attributes
|
|
|
+ supported_lora_modules = [
|
|
|
+ "qkv_proj",
|
|
|
+ "o_proj",
|
|
|
+ "embed_tokens",
|
|
|
+ "lm_head",
|
|
|
+ ]
|
|
|
+ embedding_modules = {
|
|
|
+ "embeddings": "input_embeddings",
|
|
|
+ "lm_head": "output_embeddings",
|
|
|
+ }
|
|
|
+ embedding_padding_modules = ["lm_head"]
|
|
|
+
|
|
|
+ def __init__(
|
|
|
+ self,
|
|
|
+ config: MambaConfig,
|
|
|
+ cache_config: Optional[CacheConfig] = None,
|
|
|
+ quant_config: Optional[QuantizationConfig] = None,
|
|
|
+ lora_config: Optional[LoRAConfig] = None,
|
|
|
+ scheduler_config: Optional[SchedulerConfig] = None,
|
|
|
+ ) -> None:
|
|
|
+ super().__init__()
|
|
|
+ self.config = config
|
|
|
+ self.scheduler_config = scheduler_config
|
|
|
+ self.backbone = MambaModel(config,
|
|
|
+ cache_config=cache_config,
|
|
|
+ quant_config=quant_config,
|
|
|
+ lora_config=lora_config)
|
|
|
+ self.unpadded_vocab_size = config.vocab_size
|
|
|
+ if lora_config:
|
|
|
+ self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
|
|
+
|
|
|
+ self.lm_head = self.backbone.embeddings
|
|
|
+
|
|
|
+ # Used to track and store by the Mamba cache between steps.
|
|
|
+ self.mamba_cache: Optional[MambaCacheManager] = None
|
|
|
+
|
|
|
+ self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
|
|
+ config.vocab_size)
|
|
|
+ self.sampler = Sampler()
|
|
|
+
|
|
|
+ def forward(self,
|
|
|
+ input_ids: torch.Tensor,
|
|
|
+ positions: torch.Tensor,
|
|
|
+ kv_caches: List[KVCache],
|
|
|
+ attn_metadata: AttentionMetadata,
|
|
|
+ intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
|
+ **kwargs):
|
|
|
+ if self.mamba_cache is None:
|
|
|
+ max_batch_size = (_get_graph_batch_size(
|
|
|
+ self.scheduler_config.max_num_seqs) if self.scheduler_config
|
|
|
+ else max(_BATCH_SIZES_TO_CAPTURE) + 2)
|
|
|
+ self.mamba_cache = MambaCacheManager(
|
|
|
+ self.lm_head.weight.dtype, self.config.num_hidden_layers,
|
|
|
+ max_batch_size, *self._get_mamba_cache_shape())
|
|
|
+
|
|
|
+ if "seqlen_agnostic_capture_inputs" not in kwargs:
|
|
|
+ # We get here only on Prefill/Eager mode runs
|
|
|
+ assert all(
|
|
|
+ key in kwargs
|
|
|
+ for key in ["request_ids_to_seq_ids", "finished_requests_ids"])
|
|
|
+
|
|
|
+ request_ids_to_seq_ids = kwargs["request_ids_to_seq_ids"]
|
|
|
+ finished_requests_ids = kwargs["finished_requests_ids"]
|
|
|
+ self.mamba_cache.release_finished_requests(finished_requests_ids)
|
|
|
+
|
|
|
+ batch_size = input_ids.shape[0]
|
|
|
+ if attn_metadata.prefill_metadata:
|
|
|
+ batch_size = len(request_ids_to_seq_ids)
|
|
|
+ mamba_cache_tensors = self.mamba_cache.prepare_current_run_state(
|
|
|
+ request_ids_to_seq_ids, batch_size, finished_requests_ids)
|
|
|
+
|
|
|
+ else:
|
|
|
+ # CUDA graph capturing runs
|
|
|
+ mamba_cache_tensors = kwargs["seqlen_agnostic_capture_inputs"]
|
|
|
+
|
|
|
+ hidden_states = self.backbone(input_ids, positions, kv_caches,
|
|
|
+ attn_metadata, mamba_cache_tensors[0],
|
|
|
+ mamba_cache_tensors[1])
|
|
|
+
|
|
|
+ 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()
|
|
|
+ conv_state_shape = (
|
|
|
+ self.config.intermediate_size // world_size,
|
|
|
+ self.config.conv_kernel,
|
|
|
+ )
|
|
|
+ temporal_state_shape = (
|
|
|
+ self.config.intermediate_size // world_size,
|
|
|
+ self.config.state_size,
|
|
|
+ )
|
|
|
+ return conv_state_shape, temporal_state_shape
|
|
|
+
|
|
|
+ def compute_logits(self, hidden_states: torch.Tensor,
|
|
|
+ sampling_metadata: SamplingMetadata) -> torch.Tensor:
|
|
|
+ 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)
|
AlpinDale