Sync from v0.13
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675
vllm/model_executor/models/falcon_h1.py
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675
vllm/model_executor/models/falcon_h1.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Inference-only FalconH1 model."""
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from collections.abc import Iterable
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from itertools import islice
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import torch
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from torch import nn
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from transformers import FalconH1Config
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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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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 vllm.transformers_utils.config import set_default_rope_theta
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from .interfaces import (
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HasInnerState,
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IsHybrid,
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SupportsLoRA,
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SupportsMambaPrefixCaching,
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SupportsPP,
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)
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from .utils import (
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PPMissingLayer,
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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 FalconH1MLP(nn.Module):
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def __init__(
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self,
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config: FalconH1Config,
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quant_config: QuantizationConfig | None = None,
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bias: bool = False,
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prefix: str = "",
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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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prefix=f"{prefix}.gate_up_proj",
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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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prefix=f"{prefix}.down_proj",
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)
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self.tp_size = get_tensor_model_parallel_world_size()
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self.intermediate_size = config.intermediate_size
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self.gate_multiplier, self.down_multiplier = config.mlp_multipliers
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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.intermediate_size // self.tp_size] *= self.gate_multiplier
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x = self.act_fn(x)
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x, _ = self.down_proj(x)
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x = x * self.down_multiplier
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return x
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class FalconH1SSMDecoderLayer(nn.Module):
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def __init__(
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self,
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config: FalconH1Config,
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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.tp_size = get_tensor_model_parallel_world_size()
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self.d_ssm = (
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int(config.mamba_expand * config.hidden_size)
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if config.mamba_d_ssm is None
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else config.mamba_d_ssm
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)
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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=self.d_ssm,
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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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use_rms_norm=config.mamba_rms_norm,
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prefix=f"{prefix}.mixer",
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)
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# n_groups is overridden later by `MambaMixer2`
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self.groups_time_state_size = self.mamba.n_groups * config.mamba_d_state
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self.zxbcdt_multipliers = config.ssm_multipliers
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self._init_mup_vector()
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def _init_mup_vector(self):
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"""
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Non learnable per-block scaling vector composed of element-wise
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multipliersapplied to each separate contiguous block of the output
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of the linear projection (in_proj) before further processing
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(gating, convolution, SSM):
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- Z block: [0 : d_ssm] → zxbcdt_multipliers[0]
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- X block: [d_ssm : 2 * d_ssm] → zxbcdt_multipliers[1]
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- B block: [2 * d_ssm : 2 * d_ssm + G * S] → zxbcdt_multipliers[2]
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- C block: [2 * d_ssm + G * S : 2 * d_ssm + 2 * G * S]
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→ zxbcdt_multipliers[3]
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- dt block: [2 * d_ssm + 2 * G * S : end] → zxbcdt_multipliers[4]
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where:
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- d_ssm: Dimension of state-space model latent
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- G: Number of groups (n_groups)
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- S: SSM state size per group
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- All indices are divided by tp_size to support tensor parallelism
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"""
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vector_shape = (
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2 * self.d_ssm + 2 * self.groups_time_state_size + self.config.mamba_n_heads
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) // self.tp_size
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mup_vector = torch.ones(1, vector_shape)
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# Z vector 0 -> d_ssm
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mup_vector[:, : self.d_ssm // self.tp_size] *= self.zxbcdt_multipliers[0]
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# X vector d_ssm -> 2 * d_ssm
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mup_vector[
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:, (self.d_ssm // self.tp_size) : (2 * self.d_ssm // self.tp_size)
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] *= self.zxbcdt_multipliers[1]
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# B vector 2 * d_ssm -> 2 * d_ssm + (n_group * d_state)
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mup_vector[
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:,
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(2 * self.d_ssm) // self.tp_size : (
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2 * self.d_ssm + self.groups_time_state_size
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)
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// self.tp_size,
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] *= self.zxbcdt_multipliers[2]
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# C vector 2 * d_ssm + (n_group * d_state)
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# -> 2 * d_ssm + 2 * (n_group * d_state)
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mup_vector[
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:,
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(2 * self.d_ssm + self.groups_time_state_size) // self.tp_size : (
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2 * self.d_ssm + 2 * self.groups_time_state_size
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)
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// self.tp_size,
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] *= self.zxbcdt_multipliers[3]
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# dt vector 2 * d_ssm + 2 * (n_group * d_state)
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# -> 2 * d_ssm + 2 * (n_group * d_state) + n_heads
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mup_vector[
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:,
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(2 * self.d_ssm + 2 * self.groups_time_state_size) // self.tp_size :,
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] *= self.zxbcdt_multipliers[4]
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self.register_buffer("mup_vector", mup_vector, persistent=False)
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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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output = self.mamba(
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hidden_states,
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mup_vector=self.mup_vector,
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)
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return output, residual
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class FalconH1AttentionDecoderLayer(nn.Module):
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def __init__(
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self,
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config: FalconH1Config,
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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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set_default_rope_theta(config, default_theta=1e11)
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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 = (
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config.hidden_size // self.total_num_heads
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if getattr(config, "head_dim", None) is None
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else config.head_dim
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)
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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.max_position_embeddings = max_position_embeddings
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rotary_dim = getattr(config, "attn_rotary_emb", self.head_dim)
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config.rope_parameters["partial_rotary_factor"] = rotary_dim / self.head_dim
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self.rotary_emb = get_rope(
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head_size=self.head_dim,
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max_position=max_position_embeddings,
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rope_parameters=config.rope_parameters,
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is_neox_style=True,
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dtype=None, # 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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prefix=f"{prefix}.qkv_proj",
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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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prefix=f"{prefix}.o_proj",
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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.key_multiplier = config.key_multiplier
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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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k = k * self.key_multiplier
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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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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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return hidden_states, residual
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class FalconH1ParallelHybrid(nn.Module):
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"""
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A hybrid decoder layer for FalconH1 where the input is processed
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in parallel through both the self-attention branch and the SSM (Mamba)
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branch. Their outputs are then summed to produce the final hidden state.
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This layer uses:
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- FalconH1AttentionDecoderLayer for the multi-head self-attention branch.
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- FalconH1SSMDecoderLayer for the state-space (Mamba) branch.
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"""
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def __init__(
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self,
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config: FalconH1Config,
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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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# Instantiate the attention branch
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self.self_attn = FalconH1AttentionDecoderLayer(
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config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix,
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)
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# In V1 all attention/ssm layers must have
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# different index in prefix
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ssm_layer_idx = config.num_hidden_layers + layer_idx
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ssm_prefix = prefix.split(".")[0] + f".{ssm_layer_idx}"
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# Instantiate the SSM branch
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self.mamba = FalconH1SSMDecoderLayer(
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config=config,
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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=ssm_prefix,
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)
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self.ssm_out_multiplier = config.ssm_out_multiplier
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self.ssm_in_multiplier = config.ssm_in_multiplier
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self.attention_in_multiplier = config.attention_in_multiplier
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self.attn_out_multiplier = config.attention_out_multiplier
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self.feed_forward = FalconH1MLP(config, prefix=f"{prefix}.feed_forward")
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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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positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
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**kwargs,
|
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):
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
|
||||
# Process input through the attention branch.
|
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# FalconH1AttentionDecoderLayer expects positions, hidden_states,
|
||||
# kv_cache, attn_metadata, and residual.
|
||||
attn_hidden, _ = self.self_attn(
|
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positions=positions,
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||||
hidden_states=hidden_states * self.attention_in_multiplier,
|
||||
residual=residual,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Process input through the SSM branch.
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||||
# FalconH1SSMDecoderLayer expects hidden_states, attn_metadata,
|
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# residual, and sequence_idx.
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ssm_hidden, _ = self.mamba(
|
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hidden_states=hidden_states * self.ssm_in_multiplier,
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residual=residual,
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**kwargs,
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)
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# Sum the outputs from both branches.
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# We assume both branches produce outputs of the same
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||||
# dimensionality (config.hidden_size).
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hidden_states = (attn_hidden * self.attn_out_multiplier) + (
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ssm_hidden * self.ssm_out_multiplier
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)
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hidden_states = hidden_states + residual
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# feed-forward
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residual = hidden_states
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hidden_states = self.pre_ff_layernorm(hidden_states)
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hidden_states = self.feed_forward(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
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||||
return hidden_states
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||||
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||||
@support_torch_compile
|
||||
class FalconH1Model(nn.Module):
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
config: FalconH1Config = vllm_config.model_config.hf_config
|
||||
model_config = vllm_config.model_config
|
||||
cache_config = vllm_config.cache_config
|
||||
quant_config = vllm_config.quant_config
|
||||
|
||||
self.config = config
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
if get_pp_group().is_first_rank:
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
self.vocab_size,
|
||||
config.hidden_size,
|
||||
)
|
||||
self.embedding_multiplier = config.embedding_multiplier
|
||||
else:
|
||||
self.embed_tokens = PPMissingLayer()
|
||||
self.embedding_multiplier = 1.0
|
||||
|
||||
def get_layer(prefix: str):
|
||||
layer_idx = int(prefix.rsplit(".", 1)[1])
|
||||
layer_class = FalconH1ParallelHybrid
|
||||
return layer_class(
|
||||
config,
|
||||
layer_idx,
|
||||
model_config,
|
||||
cache_config,
|
||||
quant_config=quant_config,
|
||||
prefix=prefix,
|
||||
)
|
||||
|
||||
self.start_layer, self.end_layer, self.layers = make_layers(
|
||||
config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers"
|
||||
)
|
||||
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
|
||||
["hidden_states", "residual"], config.hidden_size
|
||||
)
|
||||
if get_pp_group().is_last_rank:
|
||||
self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
else:
|
||||
self.final_layernorm = PPMissingLayer()
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.embed_tokens(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
) -> torch.Tensor:
|
||||
if get_pp_group().is_first_rank:
|
||||
if inputs_embeds is not None:
|
||||
hidden_states = inputs_embeds * self.embedding_multiplier
|
||||
else:
|
||||
hidden_states = (
|
||||
self.embed_input_ids(input_ids) * self.embedding_multiplier
|
||||
)
|
||||
else:
|
||||
assert intermediate_tensors is not None
|
||||
hidden_states = intermediate_tensors["hidden_states"]
|
||||
|
||||
for layer in islice(self.layers, self.start_layer, self.end_layer):
|
||||
hidden_states = layer(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
)
|
||||
if not get_pp_group().is_last_rank:
|
||||
return IntermediateTensors(
|
||||
{
|
||||
"hidden_states": hidden_states,
|
||||
}
|
||||
)
|
||||
hidden_states = self.final_layernorm(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class FalconH1ForCausalLM(
|
||||
nn.Module,
|
||||
HasInnerState,
|
||||
SupportsLoRA,
|
||||
SupportsPP,
|
||||
IsHybrid,
|
||||
SupportsMambaPrefixCaching,
|
||||
):
|
||||
packed_modules_mapping = {
|
||||
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
|
||||
"gate_up_proj": ["gate_proj", "up_proj"],
|
||||
}
|
||||
|
||||
embedding_modules = {
|
||||
"embed_tokens": "input_embeddings",
|
||||
"lm_head": "output_embeddings",
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def get_mamba_state_dtype_from_config(
|
||||
cls,
|
||||
vllm_config: "VllmConfig",
|
||||
) -> tuple[torch.dtype, torch.dtype]:
|
||||
return MambaStateDtypeCalculator.mamba2_state_dtype(
|
||||
vllm_config.model_config.dtype,
|
||||
vllm_config.cache_config.mamba_cache_dtype,
|
||||
vllm_config.cache_config.mamba_ssm_cache_dtype,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_mamba_state_shape_from_config(
|
||||
cls,
|
||||
vllm_config: "VllmConfig",
|
||||
) -> tuple[tuple[int, int], tuple[int, int, int]]:
|
||||
"""Calculate shapes for Mamba's convolutional and state caches.
|
||||
|
||||
Args:
|
||||
vllm_config: vLLM config
|
||||
|
||||
Returns:
|
||||
Tuple containing:
|
||||
- conv_state_shape: Shape for convolutional state cache
|
||||
- temporal_state_shape: Shape for state space model cache
|
||||
"""
|
||||
parallel_config = vllm_config.parallel_config
|
||||
hf_config = vllm_config.model_config.hf_config
|
||||
|
||||
intermediate_size = (
|
||||
int(hf_config.mamba_expand * hf_config.hidden_size)
|
||||
if hf_config.mamba_d_ssm is None
|
||||
else hf_config.mamba_d_ssm
|
||||
)
|
||||
|
||||
return MambaStateShapeCalculator.mamba2_state_shape(
|
||||
intermediate_size=intermediate_size,
|
||||
tp_world_size=parallel_config.tensor_parallel_size,
|
||||
n_groups=hf_config.mamba_n_groups,
|
||||
num_heads=hf_config.mamba_n_heads,
|
||||
head_dim=hf_config.mamba_d_head,
|
||||
state_size=hf_config.mamba_d_state,
|
||||
conv_kernel=hf_config.mamba_d_conv,
|
||||
)
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
config = vllm_config.model_config.hf_config
|
||||
self.vllm_config = vllm_config
|
||||
self.model_config = vllm_config.model_config
|
||||
|
||||
scheduler_config = vllm_config.scheduler_config
|
||||
|
||||
self.quant_config = vllm_config.quant_config
|
||||
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.scheduler_config = scheduler_config
|
||||
self.model = FalconH1Model(
|
||||
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
||||
)
|
||||
self.tie_word_embeddings = config.tie_word_embeddings
|
||||
|
||||
if get_pp_group().is_last_rank:
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
prefix=maybe_prefix(prefix, "lm_head"),
|
||||
)
|
||||
self.lm_head_multiplier = config.lm_head_multiplier
|
||||
if self.tie_word_embeddings:
|
||||
self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
|
||||
# Used to track and store by the Mamba cache between steps.
|
||||
|
||||
self.logits_processor = LogitsProcessor(
|
||||
config.vocab_size,
|
||||
config.vocab_size,
|
||||
scale=config.lm_head_multiplier,
|
||||
)
|
||||
else:
|
||||
self.lm_head = PPMissingLayer()
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.model.make_empty_intermediate_tensors
|
||||
)
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.embed_input_ids(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]:
|
||||
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())
|
||||
loaded_params: set[str] = set()
|
||||
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 "mamba" in name:
|
||||
name = name.replace("mamba", "mamba.mamba")
|
||||
|
||||
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
|
||||
# Skip layers on other devices.
|
||||
if is_pp_missing_parameter(name, self):
|
||||
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
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
if self.tie_word_embeddings and "lm_head" in name:
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
|
||||
if self.tie_word_embeddings:
|
||||
loaded_params.add("lm_head.weight")
|
||||
return loaded_params
|
||||
Reference in New Issue
Block a user