mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-20 05:25:01 +08:00
709 lines
28 KiB
Python
709 lines
28 KiB
Python
# coding=utf-8
|
|
"""Inference-only Jamba model."""
|
|
from typing import Iterable, List, Optional, Tuple
|
|
|
|
import torch
|
|
from torch import nn
|
|
from transformers import JambaConfig
|
|
|
|
from vllm.attention.backends.abstract import AttentionMetadata
|
|
from vllm.attention.layer import Attention
|
|
from vllm.config import CacheConfig, LoRAConfig, SchedulerConfig
|
|
from vllm.distributed import get_tensor_model_parallel_world_size
|
|
from vllm.model_executor.layers.fused_moe import FusedMoE
|
|
from vllm.model_executor.layers.layernorm import RMSNorm
|
|
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
|
|
MergedColumnParallelLinear,
|
|
QKVParallelLinear,
|
|
ReplicatedLinear,
|
|
RowParallelLinear)
|
|
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
|
from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
|
|
causal_conv1d_fn, causal_conv1d_update)
|
|
from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
|
|
selective_scan_fn, selective_state_update)
|
|
from vllm.model_executor.layers.quantization import QuantizationConfig
|
|
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
|
|
from vllm.model_executor.layers.vocab_parallel_embedding import (
|
|
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
|
|
from vllm.model_executor.model_loader.weight_utils import (
|
|
composed_weight_loader, default_weight_loader, sharded_weight_loader)
|
|
from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
|
|
MambaCacheParams)
|
|
from vllm.model_executor.sampling_metadata import SamplingMetadata
|
|
from vllm.model_executor.utils import set_weight_attrs
|
|
from vllm.sequence import IntermediateTensors
|
|
from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
|
|
_get_graph_batch_size)
|
|
|
|
from .interfaces import HasInnerState, SupportsLoRA
|
|
|
|
KVCache = Tuple[torch.Tensor, torch.Tensor]
|
|
|
|
|
|
# Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
|
|
class JambaMambaMixer(nn.Module):
|
|
"""
|
|
Compute ∆, A, B, C, and D the state space parameters and compute
|
|
the `contextualized_states`. A, D are input independent
|
|
(see Mamba paper [1] Section 3.5.2 "Interpretation of A"
|
|
for why A isn't selective) ∆, B, C are input-dependent
|
|
(this is a key difference between Mamba and the linear time
|
|
invariant S4, and is why Mamba is called
|
|
**selective** state spaces)
|
|
"""
|
|
|
|
def __init__(self, config: JambaConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
self.ssm_state_size = config.mamba_d_state
|
|
self.conv_kernel_size = config.mamba_d_conv
|
|
self.intermediate_size = config.mamba_expand * config.hidden_size
|
|
self.time_step_rank = config.mamba_dt_rank
|
|
self.use_conv_bias = config.mamba_conv_bias
|
|
self.use_bias = config.mamba_proj_bias
|
|
self.conv1d = ColumnParallelLinear(
|
|
input_size=self.conv_kernel_size,
|
|
output_size=self.intermediate_size,
|
|
bias=self.use_conv_bias,
|
|
)
|
|
# unsqueeze to fit conv1d weights shape into the linear weights shape.
|
|
# Can't do this in `weight_loader` since it already exists in
|
|
# `ColumnParallelLinear` and `set_weight_attrs`
|
|
# doesn't allow to override it
|
|
self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
|
|
|
|
self.in_proj = MergedColumnParallelLinear(self.hidden_size,
|
|
[self.intermediate_size] * 2,
|
|
bias=self.use_bias)
|
|
# selective projection used to make dt, B and C input dependent
|
|
self.x_proj = RowParallelLinear(
|
|
self.intermediate_size,
|
|
self.time_step_rank + self.ssm_state_size * 2,
|
|
bias=False,
|
|
)
|
|
# time step projection (discretization) -
|
|
# In the forward we need to apply dt_proj without the bias,
|
|
# as the bias is added in the selective scan kernel.
|
|
self.dt_proj = ColumnParallelLinear(self.time_step_rank,
|
|
self.intermediate_size,
|
|
bias=True,
|
|
skip_bias_add=True)
|
|
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
self.A = nn.Parameter(
|
|
torch.empty(
|
|
self.intermediate_size // tp_size,
|
|
self.ssm_state_size,
|
|
dtype=torch.float32,
|
|
))
|
|
self.D = nn.Parameter(torch.ones(self.intermediate_size // tp_size))
|
|
|
|
set_weight_attrs(self.D, {"weight_loader": sharded_weight_loader(0)})
|
|
a_weight_loader = composed_weight_loader(
|
|
sharded_weight_loader(0), lambda x: -torch.exp(x.float()))
|
|
set_weight_attrs(self.A, {"weight_loader": a_weight_loader})
|
|
|
|
self.out_proj = RowParallelLinear(
|
|
self.intermediate_size,
|
|
self.hidden_size,
|
|
bias=self.use_bias,
|
|
input_is_parallel=True,
|
|
)
|
|
self.activation = config.hidden_act
|
|
|
|
self.dt_layernorm = RMSNorm(self.time_step_rank,
|
|
eps=config.rms_norm_eps)
|
|
self.b_layernorm = RMSNorm(self.ssm_state_size,
|
|
eps=config.rms_norm_eps)
|
|
self.c_layernorm = RMSNorm(self.ssm_state_size,
|
|
eps=config.rms_norm_eps)
|
|
|
|
def forward(self, hidden_states: torch.Tensor,
|
|
attn_metadata: AttentionMetadata,
|
|
mamba_cache_params: MambaCacheParams):
|
|
|
|
# 1. Gated MLP's linear projection
|
|
projected_states = self.in_proj(hidden_states)[0].transpose(-2, -1)
|
|
hidden_states, gate = projected_states.chunk(2, dim=-2)
|
|
|
|
# 2. Convolution sequence transformation
|
|
conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0),
|
|
self.conv1d.weight.size(2))
|
|
|
|
if attn_metadata.query_start_loc is not None \
|
|
and attn_metadata.context_lens_tensor is not None:
|
|
# |---------- N-1 iteration --------|
|
|
# |---------------- N iteration ---------------------|
|
|
# |- tokenA -|......................|-- newTokens ---|
|
|
# |---------- context_len ----------|
|
|
# |-------------------- seq_len ---------------------|
|
|
# |-- query_len ---|
|
|
hidden_states = causal_conv1d_fn(
|
|
hidden_states,
|
|
conv_weights,
|
|
self.conv1d.bias,
|
|
activation=self.activation,
|
|
conv_states=mamba_cache_params.conv_state,
|
|
has_initial_state=attn_metadata.context_lens_tensor > 0,
|
|
cache_indices=mamba_cache_params.state_indices_tensor,
|
|
query_start_loc=attn_metadata.query_start_loc)
|
|
else:
|
|
hidden_states = causal_conv1d_update(
|
|
hidden_states.transpose(0, 1),
|
|
mamba_cache_params.conv_state,
|
|
conv_weights,
|
|
self.conv1d.bias,
|
|
self.activation,
|
|
conv_state_indices=mamba_cache_params.state_indices_tensor)
|
|
hidden_states = hidden_states.transpose(0, 1)
|
|
|
|
# 3. State Space Model sequence transformation
|
|
# 3.a. input varying initialization of time_step, B and C
|
|
ssm_parameters = self.x_proj(hidden_states.transpose(-2, -1))[0]
|
|
|
|
time_step, B, C = torch.split(
|
|
ssm_parameters,
|
|
[self.time_step_rank, self.ssm_state_size, self.ssm_state_size],
|
|
dim=-1,
|
|
)
|
|
time_step = self.dt_layernorm(time_step.contiguous())
|
|
B = self.b_layernorm(B.contiguous())
|
|
C = self.c_layernorm(C.contiguous())
|
|
|
|
discrete_time_step = self.dt_proj(time_step)[0].transpose(-2, -1)
|
|
# 3.c perform the recurrence y ← SSM(A, B, C)(x)
|
|
time_proj_bias = (self.dt_proj.bias.float() if hasattr(
|
|
self.dt_proj, "bias") else None)
|
|
|
|
if attn_metadata.query_start_loc is not None \
|
|
and attn_metadata.context_lens_tensor is not None:
|
|
scan_outputs = selective_scan_fn(
|
|
hidden_states,
|
|
mamba_cache_params.ssm_state,
|
|
discrete_time_step,
|
|
self.A,
|
|
B.transpose(-2, -1),
|
|
C.transpose(-2, -1),
|
|
self.D.float(),
|
|
gate,
|
|
time_proj_bias,
|
|
delta_softplus=True,
|
|
cache_indices=mamba_cache_params.state_indices_tensor,
|
|
has_initial_state=attn_metadata.context_lens_tensor > 0,
|
|
query_start_loc=attn_metadata.query_start_loc)
|
|
else:
|
|
scan_outputs = selective_state_update(
|
|
mamba_cache_params.ssm_state,
|
|
hidden_states.transpose(0, 1),
|
|
discrete_time_step.transpose(0, 1),
|
|
self.A,
|
|
B,
|
|
C,
|
|
self.D,
|
|
gate.transpose(0, 1),
|
|
time_proj_bias,
|
|
dt_softplus=True,
|
|
state_batch_indices=mamba_cache_params.state_indices_tensor)
|
|
scan_outputs = scan_outputs.transpose(0, 1)
|
|
|
|
# 4. Final linear projection
|
|
contextualized_states = self.out_proj(scan_outputs.transpose(-2,
|
|
-1))[0]
|
|
return contextualized_states
|
|
|
|
|
|
class JambaMoE(nn.Module):
|
|
|
|
def __init__(self,
|
|
config: JambaConfig,
|
|
num_experts: Optional[int] = None,
|
|
top_k: Optional[int] = None,
|
|
params_dtype: Optional[torch.dtype] = None,
|
|
tp_size: Optional[int] = None,
|
|
quant_config: Optional[QuantizationConfig] = None):
|
|
super().__init__()
|
|
self.num_total_experts = num_experts or config.num_experts
|
|
self.top_k = top_k or config.num_experts_per_tok
|
|
self.hidden_size = config.hidden_size
|
|
self.intermediate_size = config.intermediate_size
|
|
|
|
if self.num_total_experts > 1:
|
|
self.router = ReplicatedLinear(self.hidden_size,
|
|
self.num_total_experts,
|
|
bias=False,
|
|
quant_config=None,
|
|
params_dtype=params_dtype)
|
|
|
|
self.experts = FusedMoE(self.num_total_experts,
|
|
self.top_k,
|
|
self.hidden_size,
|
|
self.intermediate_size,
|
|
tp_size=tp_size,
|
|
params_dtype=params_dtype,
|
|
reduce_results=True,
|
|
renormalize=False,
|
|
use_grouped_topk=False,
|
|
quant_config=quant_config)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
orig_shape = hidden_states.shape
|
|
hidden_states = hidden_states.view(-1, self.hidden_size)
|
|
# router_logits: (batch * sequence_length, n_experts)
|
|
if self.num_total_experts > 1:
|
|
router_logits, _ = self.router(hidden_states)
|
|
else:
|
|
router_logits = torch.ones((hidden_states.shape[0], 1),
|
|
device=hidden_states.device,
|
|
dtype=hidden_states.dtype)
|
|
hidden_states = self.experts(hidden_states, router_logits)
|
|
return hidden_states.view(orig_shape)
|
|
|
|
|
|
class JambaMLP(JambaMoE):
|
|
|
|
def __init__(self,
|
|
config: JambaConfig,
|
|
params_dtype: Optional[torch.dtype] = None,
|
|
tp_size: Optional[int] = None,
|
|
quant_config: Optional[QuantizationConfig] = None):
|
|
super().__init__(config,
|
|
num_experts=1,
|
|
top_k=1,
|
|
params_dtype=params_dtype,
|
|
tp_size=tp_size,
|
|
quant_config=quant_config)
|
|
|
|
|
|
class JambaMambaDecoderLayer(nn.Module):
|
|
|
|
def __init__(self,
|
|
config: JambaConfig,
|
|
layer_idx: int,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None) -> None:
|
|
super().__init__()
|
|
self.layer_idx = layer_idx
|
|
self.config = config
|
|
self.mamba = JambaMambaMixer(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 forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attn_metadata: AttentionMetadata,
|
|
residual: Optional[torch.Tensor],
|
|
mamba_cache_params: MambaCacheParams,
|
|
**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.mamba(hidden_states, attn_metadata,
|
|
mamba_cache_params)
|
|
# Fully Connected
|
|
hidden_states, residual = self.pre_ff_layernorm(
|
|
hidden_states, residual)
|
|
hidden_states = self.feed_forward(hidden_states)
|
|
return hidden_states, residual
|
|
|
|
|
|
class JambaAttentionDecoderLayer(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: JambaConfig,
|
|
layer_idx: int,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
self.total_num_heads = config.num_attention_heads
|
|
assert self.total_num_heads % tp_size == 0
|
|
self.num_heads = self.total_num_heads // tp_size
|
|
self.total_num_kv_heads = config.num_key_value_heads
|
|
if self.total_num_kv_heads >= tp_size:
|
|
# Number of KV heads is greater than TP size, so we partition
|
|
# the KV heads across multiple tensor parallel GPUs.
|
|
assert self.total_num_kv_heads % tp_size == 0
|
|
else:
|
|
# Number of KV heads is less than TP size, so we replicate
|
|
# the KV heads across multiple tensor parallel GPUs.
|
|
assert tp_size % self.total_num_kv_heads == 0
|
|
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
|
|
self.head_dim = config.hidden_size // self.total_num_heads
|
|
self.q_size = self.num_heads * self.head_dim
|
|
self.kv_size = self.num_kv_heads * self.head_dim
|
|
self.scaling = self.head_dim**-0.5
|
|
|
|
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,
|
|
mamba_cache_params: MambaCacheParams,
|
|
) -> 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
|
|
layer_mamba_cache_params = 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)
|
|
layer_mamba_cache_params = mamba_cache_params.at_layer_idx(
|
|
current_state_layer)
|
|
|
|
hidden_states, residual = layer(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
kv_cache=kv_cache,
|
|
attn_metadata=attn_metadata,
|
|
residual=residual,
|
|
mamba_cache_params=layer_mamba_cache_params)
|
|
hidden_states, _ = self.final_layernorm(hidden_states, residual)
|
|
return hidden_states
|
|
|
|
|
|
class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA):
|
|
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,
|
|
scheduler_config: Optional[SchedulerConfig] = None,
|
|
) -> None:
|
|
assert not cache_config.enable_prefix_caching, \
|
|
"Jamba currently does not support prefix caching"
|
|
|
|
super().__init__()
|
|
self.config = config
|
|
self.scheduler_config = scheduler_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,
|
|
)
|
|
# Used to track and store by the Mamba cache between steps.
|
|
self.mamba_cache: Optional[MambaCacheManager] = None
|
|
|
|
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
|
config.vocab_size)
|
|
self.sampler = Sampler()
|
|
|
|
def forward(self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[KVCache],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
**kwargs):
|
|
if self.mamba_cache is None:
|
|
max_batch_size = (_get_graph_batch_size(
|
|
self.scheduler_config.max_num_seqs) if self.scheduler_config
|
|
else max(_BATCH_SIZES_TO_CAPTURE) + 2)
|
|
|
|
layers_type = self.config.layers_block_type
|
|
num_mamba_layers = sum(
|
|
[layer_type == "mamba" for layer_type in layers_type])
|
|
|
|
self.mamba_cache = MambaCacheManager(
|
|
self.lm_head.weight.dtype, num_mamba_layers, max_batch_size,
|
|
*self._get_mamba_cache_shape())
|
|
(
|
|
mamba_cache_tensors,
|
|
state_indices_tensor,
|
|
) = self.mamba_cache.current_run_tensors(input_ids, attn_metadata,
|
|
**kwargs)
|
|
mamba_cache_params = MambaCacheParams(mamba_cache_tensors[0],
|
|
mamba_cache_tensors[1],
|
|
state_indices_tensor)
|
|
hidden_states = self.model(input_ids, positions, kv_caches,
|
|
attn_metadata, mamba_cache_params)
|
|
return hidden_states
|
|
|
|
def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
|
|
return self.mamba_cache.copy_inputs_before_cuda_graphs(
|
|
input_buffers, **kwargs)
|
|
|
|
def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
|
|
return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
|
|
|
|
def _get_mamba_cache_shape(
|
|
self) -> Tuple[Tuple[int, int], Tuple[int, int]]:
|
|
world_size = get_tensor_model_parallel_world_size()
|
|
hidden_size = self.config.hidden_size
|
|
conv_state_shape = (
|
|
self.config.mamba_expand * hidden_size // world_size,
|
|
self.config.mamba_d_conv - 1,
|
|
)
|
|
temporal_state_shape = (
|
|
self.config.mamba_expand * hidden_size // world_size,
|
|
self.config.mamba_d_state,
|
|
)
|
|
return conv_state_shape, temporal_state_shape
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def sample(
|
|
self,
|
|
logits: Optional[torch.Tensor],
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
next_tokens = self.sampler(logits, sampling_metadata)
|
|
return next_tokens
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
]
|
|
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.num_experts)
|
|
|
|
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", "")
|
|
|
|
if "feed_forward" in name and not _is_moe_layer(name):
|
|
## map MLP layers to expert with ID=0
|
|
name = name.replace("feed_forward", "feed_forward.experts.0")
|
|
|
|
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,
|
|
shard_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,
|
|
name,
|
|
shard_id=shard_id,
|
|
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)
|
|
|
|
|
|
def _is_moe_layer(name: str):
|
|
return any(
|
|
[experts_name in name for experts_name in [
|
|
"experts",
|
|
"router",
|
|
]])
|