9d6a8daa87
Signed-off-by: Muralidhar Andoorveedu <muralidhar.andoorveedu@centml.ai> Co-authored-by: Erez Schwartz <erezs@ai21.com> Co-authored-by: Mor Zusman <morz@ai21.com> Co-authored-by: tomeras91 <57313761+tomeras91@users.noreply.github.com> Co-authored-by: Tomer Asida <tomera@ai21.com> Co-authored-by: Zhuohan Li <zhuohan123@gmail.com> Co-authored-by: Muralidhar Andoorveedu <muralidhar.andoorveedu@centml.ai>
956 lines
38 KiB
Python
956 lines
38 KiB
Python
# coding=utf-8
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"""Inference-only Jurassic model."""
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from dataclasses import dataclass
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from typing import Dict, Iterable, List, Optional, Tuple
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import torch
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from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
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from mamba_ssm.ops.selective_scan_interface import selective_scan_fn
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from mamba_ssm.ops.triton.selective_state_update import selective_state_update
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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 JambaConfig
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from vllm.attention.backends.abstract import AttentionMetadata
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from vllm.attention.layer import Attention
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from vllm.config import CacheConfig, LoRAConfig
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from vllm.distributed import (get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import fused_moe
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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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QKVParallelLinear,
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ReplicatedLinear,
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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.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.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, SamplerOutput
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from vllm.worker.model_runner import _BATCH_SIZES_TO_CAPTURE
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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 JambaMambaMixer(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: JambaConfig, 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.mamba_d_state
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self.conv_kernel_size = config.mamba_d_conv
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self.intermediate_size = config.mamba_expand * config.hidden_size
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self.time_step_rank = config.mamba_dt_rank
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self.use_conv_bias = config.mamba_conv_bias
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self.use_bias = config.mamba_proj_bias
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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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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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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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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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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": weight_loader})
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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=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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self.dt_layernorm = RMSNorm(self.time_step_rank,
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eps=config.rms_norm_eps)
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self.b_layernorm = RMSNorm(self.ssm_state_size,
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eps=config.rms_norm_eps)
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self.c_layernorm = RMSNorm(self.ssm_state_size,
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eps=config.rms_norm_eps)
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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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# 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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# 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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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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# 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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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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time_step = self.dt_layernorm(time_step.contiguous())
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B = self.b_layernorm(B.contiguous())
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C = self.c_layernorm(C.contiguous())
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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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# 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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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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return hidden_states
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class JambaMLP(nn.Module):
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def __init__(
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self,
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config: JambaConfig,
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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 JambaMoE(nn.Module):
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"""A tensor-parallel MoE implementation for Mixtral that shards each expert
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across all ranks.
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Each expert's weights are sharded across all ranks and a fused MoE
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kernel is used for the forward pass, and finally we reduce the outputs
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across ranks.
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"""
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def __init__(
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self,
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config: JambaConfig,
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params_dtype: Optional[torch.dtype] = None,
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tp_size: Optional[int] = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.tp_size = tp_size or get_tensor_model_parallel_world_size()
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self.num_total_experts = config.num_experts
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self.top_k = config.num_experts_per_tok
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size // self.tp_size
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if params_dtype is None:
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params_dtype = torch.get_default_dtype()
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self.params_dtype = params_dtype
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self.router = ReplicatedLinear(self.hidden_size,
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self.num_total_experts,
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bias=False,
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params_dtype=self.params_dtype)
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self.ws = nn.Parameter(
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torch.empty(
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self.num_total_experts,
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2 * self.intermediate_size,
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self.hidden_size,
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device="cuda",
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dtype=self.params_dtype,
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))
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self.w2s = nn.Parameter(
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torch.empty(
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self.num_total_experts,
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self.hidden_size,
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self.intermediate_size,
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device="cuda",
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dtype=self.params_dtype,
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))
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set_weight_attrs(
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self.ws,
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{
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"weight_loader": self.weight_loader,
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},
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)
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set_weight_attrs(
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self.w2s,
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{
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"weight_loader": self.weight_loader,
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},
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)
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def weight_loader(
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self,
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param: nn.Parameter,
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loaded_weight: torch.Tensor,
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weight_name: str,
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expert_id: int,
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):
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tp_rank = get_tensor_model_parallel_rank()
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param_data = param.data
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shard_size = self.intermediate_size
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shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
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if weight_name.endswith("gate_proj.weight"):
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param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :]
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if weight_name.endswith("up_proj.weight"):
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param_data[expert_id,
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shard_size:2 * shard_size, :] = loaded_weight[shard, :]
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if weight_name.endswith("down_proj.weight"):
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param_data[expert_id, :, :] = loaded_weight[:, shard]
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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num_tokens, hidden_size = hidden_states.shape
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hidden_states = hidden_states.view(-1, self.hidden_size)
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# router_logits: (batch * sequence_length, n_experts)
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router_logits, _ = self.router(hidden_states)
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final_hidden_states = fused_moe(
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hidden_states,
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self.ws,
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self.w2s,
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router_logits,
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self.top_k,
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renormalize=
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False, # Mixtral normalize the expert probs to 1. We don't!
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inplace=True,
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)
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if self.tp_size > 1:
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final_hidden_states = tensor_model_parallel_all_reduce(
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final_hidden_states)
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return final_hidden_states.view(num_tokens, hidden_size)
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class JambaMambaDecoderLayer(nn.Module):
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def __init__(self,
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config: JambaConfig,
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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.mamba = JambaMambaMixer(config, layer_idx)
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num_experts = config.layers_num_experts[layer_idx]
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ffn_layer_class = JambaMoE if num_experts > 1 else JambaMLP
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self.feed_forward = ffn_layer_class(config, quant_config=quant_config)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.pre_ff_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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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.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.mamba(hidden_states, 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 JambaAttentionDecoderLayer(nn.Module):
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def __init__(
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self,
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config: JambaConfig,
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layer_idx: int,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = config.num_key_value_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = config.hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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|
|
|
self.qkv_proj = QKVParallelLinear(
|
|
config.hidden_size,
|
|
self.head_dim,
|
|
self.total_num_heads,
|
|
self.total_num_kv_heads,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
|
|
config.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config)
|
|
|
|
self.attn = Attention(
|
|
self.num_heads,
|
|
self.head_dim,
|
|
self.scaling,
|
|
num_kv_heads=self.num_kv_heads,
|
|
cache_config=cache_config,
|
|
)
|
|
|
|
num_experts = config.layers_num_experts[layer_idx]
|
|
ffn_layer_class = JambaMoE if num_experts > 1 else JambaMLP
|
|
self.feed_forward = ffn_layer_class(config, quant_config=quant_config)
|
|
self.input_layernorm = RMSNorm(config.hidden_size,
|
|
eps=config.rms_norm_eps)
|
|
self.pre_ff_layernorm = RMSNorm(config.hidden_size,
|
|
eps=config.rms_norm_eps)
|
|
|
|
def self_attention(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: AttentionMetadata,
|
|
**kwargs,
|
|
) -> torch.Tensor:
|
|
qkv, _ = self.qkv_proj(hidden_states)
|
|
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
|
attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
|
|
output, _ = self.o_proj(attn_output)
|
|
return output
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: AttentionMetadata,
|
|
residual: Optional[torch.Tensor],
|
|
**kwargs,
|
|
):
|
|
if residual is None:
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
else:
|
|
hidden_states, residual = self.input_layernorm(
|
|
hidden_states, residual)
|
|
|
|
hidden_states = self.self_attention(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
kv_cache=kv_cache,
|
|
attn_metadata=attn_metadata,
|
|
)
|
|
# Fully Connected
|
|
hidden_states, residual = self.pre_ff_layernorm(
|
|
hidden_states, residual)
|
|
hidden_states = self.feed_forward(hidden_states)
|
|
return hidden_states, residual
|
|
|
|
|
|
ALL_DECODER_LAYER_TYPES = {
|
|
"attention": JambaAttentionDecoderLayer,
|
|
"mamba": JambaMambaDecoderLayer
|
|
}
|
|
|
|
|
|
class JambaModel(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: JambaConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
lora_config: Optional[LoRAConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.padding_idx = config.pad_token_id
|
|
lora_vocab = ((lora_config.lora_extra_vocab_size *
|
|
(lora_config.max_loras or 1)) if lora_config else 0)
|
|
self.vocab_size = config.vocab_size + lora_vocab
|
|
self.org_vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
self.vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=config.vocab_size,
|
|
)
|
|
|
|
decoder_layers = []
|
|
for i in range(config.num_hidden_layers):
|
|
layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[i]]
|
|
decoder_layers.append(
|
|
layer_class(config,
|
|
layer_idx=i,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config))
|
|
self.layers = nn.ModuleList(decoder_layers)
|
|
self.final_layernorm = RMSNorm(config.hidden_size,
|
|
eps=config.rms_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
conv_state: torch.Tensor,
|
|
ssm_state: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
residual = None
|
|
|
|
for i in range(len(self.layers)):
|
|
layer = self.layers[i]
|
|
kv_cache = None
|
|
current_ssm_state = None
|
|
current_conv_state = None
|
|
if isinstance(layer, JambaAttentionDecoderLayer):
|
|
kv_cache = kv_caches[(i - self.config.attn_layer_offset) //
|
|
self.config.attn_layer_period]
|
|
if isinstance(layer, JambaMambaDecoderLayer):
|
|
current_state_layer = i - (1 +
|
|
(i - self.config.attn_layer_offset)
|
|
// self.config.attn_layer_period)
|
|
current_ssm_state = ssm_state[current_state_layer]
|
|
current_conv_state = conv_state[current_state_layer]
|
|
|
|
hidden_states, residual = layer(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
kv_cache=kv_cache,
|
|
attn_metadata=attn_metadata,
|
|
residual=residual,
|
|
conv_state=current_conv_state,
|
|
ssm_state=current_ssm_state,
|
|
)
|
|
hidden_states, _ = self.final_layernorm(hidden_states, residual)
|
|
return hidden_states
|
|
|
|
|
|
class JambaForCausalLM(nn.Module):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
}
|
|
|
|
# LoRA specific attributes
|
|
supported_lora_modules = [
|
|
"qkv_proj",
|
|
"o_proj",
|
|
"embed_tokens",
|
|
"lm_head",
|
|
]
|
|
embedding_modules = {
|
|
"embed_tokens": "input_embeddings",
|
|
"lm_head": "output_embeddings",
|
|
}
|
|
embedding_padding_modules = ["lm_head"]
|
|
|
|
def __init__(
|
|
self,
|
|
config: JambaConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
lora_config: Optional[LoRAConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.model = JambaModel(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 = ParallelLMHead(
|
|
self.unpadded_vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=config.vocab_size,
|
|
padding_size=DEFAULT_VOCAB_PADDING_SIZE
|
|
# We need bigger padding if using lora for kernel
|
|
# compatibility
|
|
if not lora_config else lora_config.lora_vocab_padding_size,
|
|
)
|
|
# Current step used indices
|
|
self.current_indices: List[int] = []
|
|
# Used to track and store by the Mamba cache between steps.
|
|
self.mamba_cache: Tuple[torch.Tensor, torch.Tensor] = tuple()
|
|
# Used as an input_buffer for the CUDA graph runs.
|
|
self.mamba_gc_cache_buffer: Tuple[torch.Tensor, torch.Tensor] = tuple()
|
|
# Maps between the request id and a dict that maps between the seq_id
|
|
# and its index inside the self.mamba_cache
|
|
self.mamba_cache_indices_mapping: Dict[str, Dict[int, int]] = {}
|
|
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 not self.mamba_cache:
|
|
self._prepare_mamba_cache()
|
|
|
|
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"]
|
|
batch_size = input_ids.shape[0]
|
|
if attn_metadata.prefill_metadata:
|
|
batch_size = len(request_ids_to_seq_ids)
|
|
(
|
|
current_seqlen_agnostic_cache,
|
|
indices,
|
|
) = self._prepare_current_run_mamba_cache(request_ids_to_seq_ids,
|
|
batch_size)
|
|
finished_requests_ids = kwargs["finished_requests_ids"]
|
|
self._release_mamba_cache(finished_requests_ids)
|
|
else:
|
|
# CUDA graph capturing runs
|
|
current_seqlen_agnostic_cache, indices = (
|
|
kwargs["seqlen_agnostic_capture_inputs"],
|
|
[],
|
|
)
|
|
self.current_indices = indices
|
|
|
|
hidden_states = self.model(input_ids, positions, kv_caches,
|
|
attn_metadata,
|
|
current_seqlen_agnostic_cache[0],
|
|
current_seqlen_agnostic_cache[1])
|
|
|
|
if "seqlen_agnostic_capture_inputs" not in kwargs:
|
|
self._copy_mamba_cache_by_indices(self.current_indices,
|
|
current_seqlen_agnostic_cache)
|
|
|
|
return hidden_states
|
|
|
|
def _copy_mamba_cache_by_indices(
|
|
self, indices: List[int],
|
|
current_seqlen_agnostic_cache: Tuple[torch.Tensor, torch.Tensor]):
|
|
for i, offset in enumerate(indices):
|
|
self._copy_mamba_cache(offset, i, current_seqlen_agnostic_cache)
|
|
|
|
def _copy_mamba_cache(self, index_to: int, index_from: int,
|
|
from_buffer: Tuple[torch.Tensor, torch.Tensor]):
|
|
assert len(self.mamba_cache) > 0
|
|
for (cache_t, from_buffer_t) in zip(self.mamba_cache, from_buffer):
|
|
cache_t[:, index_to].copy_(from_buffer_t[:, index_from],
|
|
non_blocking=True)
|
|
|
|
def _assign_seq_id_to_mamba_cache(self, cur_rid: str,
|
|
seqs_id: List[int]) -> List[int]:
|
|
indices_for_current_run = []
|
|
for seq_id in seqs_id:
|
|
if cur_rid not in self.mamba_cache_indices_mapping:
|
|
self.mamba_cache_indices_mapping[cur_rid] = {}
|
|
first_free_index = self._first_free_index_in_mamba_cache()
|
|
self.mamba_cache_indices_mapping[cur_rid][
|
|
seq_id] = first_free_index
|
|
index_for_current_run = first_free_index
|
|
## case of decoding n>1, copy prefill cache to decoding indices
|
|
elif seq_id not in (seq_ids2indices :=
|
|
self.mamba_cache_indices_mapping[cur_rid]):
|
|
first_free_index = self._first_free_index_in_mamba_cache()
|
|
index_exist = list(seq_ids2indices.values())[0]
|
|
self._copy_mamba_cache(index_from=index_exist,
|
|
index_to=first_free_index,
|
|
from_buffer=self.mamba_cache)
|
|
self.mamba_cache_indices_mapping[cur_rid][
|
|
seq_id] = first_free_index
|
|
index_for_current_run = first_free_index
|
|
else:
|
|
index_for_current_run = self.mamba_cache_indices_mapping[
|
|
cur_rid][seq_id]
|
|
|
|
indices_for_current_run.append(index_for_current_run)
|
|
return indices_for_current_run
|
|
|
|
def _prepare_current_run_mamba_cache(
|
|
self, request_ids_to_seq_ids: Dict[str, list[int]], batch_size: int
|
|
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], List[int]]:
|
|
indices_for_current_run = []
|
|
for request_id, seqs_id in request_ids_to_seq_ids.items():
|
|
indices_for_current_run += self._assign_seq_id_to_mamba_cache(
|
|
request_id, seqs_id)
|
|
## Pad the batch in case of running batch that was not captured via CG
|
|
padded_indices = indices_for_current_run.copy()
|
|
pad_index = self._first_free_index_in_mamba_cache()
|
|
|
|
for _ in range(batch_size - len(indices_for_current_run)):
|
|
padded_indices.append(pad_index)
|
|
|
|
conv_state = self.mamba_cache[0][:, padded_indices]
|
|
temporal_state = self.mamba_cache[1][:, padded_indices]
|
|
|
|
return (conv_state, temporal_state), indices_for_current_run
|
|
|
|
def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
|
|
"""
|
|
Copy the relevant Mamba cache into the CUDA graph input buffer
|
|
that was provided during the capture runs
|
|
(JambaForCausalLM.mamba_gc_cache_buffer).
|
|
"""
|
|
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"]
|
|
batch_size = len(request_ids_to_seq_ids)
|
|
(
|
|
current_mamba_cache,
|
|
indices,
|
|
) = self._prepare_current_run_mamba_cache(request_ids_to_seq_ids,
|
|
batch_size)
|
|
self.current_indices = indices
|
|
finished_requests_ids = kwargs["finished_requests_ids"]
|
|
self._release_mamba_cache(finished_requests_ids)
|
|
|
|
for input_buffer, current_cache_buffer in zip(
|
|
input_buffers["seqlen_agnostic_capture_inputs"],
|
|
current_mamba_cache):
|
|
input_buffer.copy_(current_cache_buffer, non_blocking=True)
|
|
|
|
def copy_outputs_after_cuda_graphs(self, input_buffers, **kwargs):
|
|
"""
|
|
Copy the relevant Mamba cache from the CUDA graph input_buffers
|
|
back to the JambaForCausalLM.mamba_cache after CUDA
|
|
graph replay run is done.
|
|
"""
|
|
self._copy_mamba_cache_by_indices(
|
|
self.current_indices,
|
|
input_buffers["seqlen_agnostic_capture_inputs"])
|
|
|
|
def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
|
|
"""
|
|
Provide the CUDA graph capture runs with a buffer in adjusted size.
|
|
The buffer is used to maintain the Mamba Cache during the CUDA graph
|
|
replay runs.
|
|
"""
|
|
return tuple(buffer[:, :batch_size]
|
|
for buffer in self.mamba_gc_cache_buffer)
|
|
|
|
def _release_mamba_cache(self, finished_seq_groups_req_ids: List[str]):
|
|
for req_id in finished_seq_groups_req_ids:
|
|
if req_id in self.mamba_cache_indices_mapping:
|
|
self.mamba_cache_indices_mapping.pop(req_id)
|
|
|
|
def _first_free_index_in_mamba_cache(self) -> int:
|
|
if self.mamba_cache:
|
|
max_possible_batch_size = self.mamba_cache[0].shape[1]
|
|
occupied = [
|
|
id for seq_ids in self.mamba_cache_indices_mapping.values()
|
|
for id in seq_ids.values()
|
|
]
|
|
first_free_index = [
|
|
i not in occupied for i in range(max_possible_batch_size)
|
|
].index(True)
|
|
return first_free_index
|
|
return 0
|
|
|
|
def _get_mamba_cache_shape(
|
|
self
|
|
) -> Tuple[Optional[Tuple[int, int]], Optional[Tuple[int, int]]]:
|
|
world_size = get_tensor_model_parallel_world_size()
|
|
hidden_size = self.config.hidden_size
|
|
conv_state_shape = (
|
|
self.config.mamba_expand * hidden_size // world_size,
|
|
self.config.mamba_d_conv,
|
|
)
|
|
temporal_state_shape = (
|
|
self.config.mamba_expand * self.config.hidden_size // world_size,
|
|
self.config.mamba_d_state,
|
|
)
|
|
return conv_state_shape, temporal_state_shape
|
|
|
|
def _prepare_mamba_cache(self):
|
|
dtype = self.lm_head.weight.dtype
|
|
layers_type = self.config.layers_block_type
|
|
mamba_layers = sum(
|
|
[layer_type == "mamba" for layer_type in layers_type])
|
|
max_batch_size = _BATCH_SIZES_TO_CAPTURE[-1] + 10
|
|
conv_state_shape, temporal_state_shape = self._get_mamba_cache_shape()
|
|
assert conv_state_shape is not None and temporal_state_shape is not None
|
|
for buffername in ["mamba_cache", "mamba_gc_cache_buffer"]:
|
|
buffer = (torch.empty(size=(mamba_layers, max_batch_size) +
|
|
conv_state_shape,
|
|
dtype=dtype,
|
|
device="cuda"),
|
|
torch.empty(size=(mamba_layers, max_batch_size) +
|
|
temporal_state_shape,
|
|
dtype=dtype,
|
|
device="cuda"))
|
|
setattr(self, buffername, buffer)
|
|
|
|
def compute_logits(self, hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata) -> torch.Tensor:
|
|
logits = self.logits_processor(self.lm_head.weight, 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),
|
|
]
|
|
|
|
expert_params_mapping = [
|
|
# (param_name, weight_name, expert_id)
|
|
(
|
|
"ws" if weight_name in ["gate_proj", "up_proj"] else "w2s",
|
|
f"experts.{expert_id}.{weight_name}.weight",
|
|
expert_id,
|
|
) for expert_id in range(self.config.num_experts)
|
|
for weight_name in ["down_proj", "up_proj", "gate_proj"]
|
|
]
|
|
|
|
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
|
|
if 'experts' 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:
|
|
for param_name, weight_name, expert_id in expert_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param,
|
|
loaded_weight,
|
|
weight_name,
|
|
expert_id=expert_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)
|