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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Inference-only GraniteMoeShared model.
The architecture is the same as granitemoe but with the addition of shared
experts.
"""
from collections.abc import Iterable
from typing import Optional
import torch
from torch import nn
from transformers.models.granitemoeshared import GraniteMoeSharedConfig
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_pp_group
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from . import mixtral
from .granitemoe import GraniteMoeAttention, GraniteMoeMoE
from .interfaces import SupportsLoRA, SupportsPP
from .utils import AutoWeightsLoader, make_layers, maybe_prefix
class GraniteMoeSharedMLP(nn.Module):
def __init__(
self,
config: GraniteMoeSharedConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.input_size = config.hidden_size
self.hidden_size = config.shared_intermediate_size
self.input_linear = MergedColumnParallelLinear(
input_size=self.input_size,
output_sizes=[self.hidden_size] * 2,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.input_linear")
self.output_linear = RowParallelLinear(
self.hidden_size,
self.input_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.output_linear")
if config.hidden_act != "silu":
raise ValueError(f"Unsupported activation: {config.hidden_act}. "
"Only silu is supported for now.")
self.act_fn = SiluAndMul()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states, _ = self.input_linear(hidden_states)
hidden_states = self.act_fn(hidden_states)
hidden_states, _ = self.output_linear(hidden_states)
return hidden_states
class GraniteMoeSharedDecoderLayer(nn.Module):
def __init__(
self,
config: GraniteMoeSharedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.hidden_size = config.hidden_size
# Requires transformers > 4.32.0
rope_theta = getattr(config, "rope_theta", 10000)
self.self_attn = GraniteMoeAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
max_position=config.max_position_embeddings,
num_kv_heads=config.num_key_value_heads,
rope_theta=rope_theta,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
attention_multiplier=config.attention_multiplier)
self.block_sparse_moe = GraniteMoeMoE(
num_experts=config.num_local_experts,
top_k=config.num_experts_per_tok,
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
quant_config=quant_config,
prefix=f"{prefix}.block_sparse_moe")
self.shared_mlp = None if \
getattr(config, 'shared_intermediate_size', 0) == 0 \
else GraniteMoeSharedMLP(
config,
quant_config=quant_config,
prefix=f"{prefix}.shared_mlp"
)
self.input_layernorm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.residual_multiplier = config.residual_multiplier
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
) -> torch.Tensor:
# Self Attention
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = self.self_attn(
positions=positions,
hidden_states=hidden_states,
)
hidden_states = residual + hidden_states * self.residual_multiplier
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
if self.shared_mlp is None:
hidden_states = self.block_sparse_moe(hidden_states)
else:
# create a copy since block_sparse_moe modifies in-place
moe_hidden_states = hidden_states.clone()
moe_hidden_states = self.block_sparse_moe(moe_hidden_states)
hidden_states = moe_hidden_states + self.shared_mlp(hidden_states)
del moe_hidden_states
hidden_states = residual + hidden_states * self.residual_multiplier
return hidden_states
@support_torch_compile
class GraniteMoeSharedModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.config = config
self.quant_config = quant_config # Required by MixtralModel
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,
quant_config=quant_config,
)
self.embedding_multiplier = config.embedding_multiplier
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: GraniteMoeSharedDecoderLayer(
config, cache_config, quant_config=quant_config, prefix=prefix
),
prefix=f"{prefix}.layers")
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def get_input_embeddings(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: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if get_pp_group().is_first_rank:
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
hidden_states *= self.embedding_multiplier
residual = None
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
residual = intermediate_tensors["residual"]
for i in range(self.start_layer, self.end_layer):
layer = self.layers[i]
hidden_states = layer(positions, hidden_states)
if not get_pp_group().is_last_rank:
return IntermediateTensors({
"hidden_states": hidden_states,
"residual": residual
})
hidden_states = self.norm(hidden_states)
return hidden_states
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
new_weights = {}
for n, p in weights:
if n.endswith('.block_sparse_moe.input_linear.weight'):
for e in range(p.size(0)):
w1_name = n.replace(
'.block_sparse_moe.input_linear.weight',
f".block_sparse_moe.experts.{e}.w1.weight")
w3_name = n.replace(
'.block_sparse_moe.input_linear.weight',
f".block_sparse_moe.experts.{e}.w3.weight")
w1_param, w3_param = p[e].chunk(2, dim=0)
assert w1_name not in new_weights
assert w3_name not in new_weights
new_weights[w1_name] = w1_param
new_weights[w3_name] = w3_param
elif n.endswith('.block_sparse_moe.output_linear.weight'):
for e in range(p.size(0)):
w2_name = n.replace(
'.block_sparse_moe.output_linear.weight',
f".block_sparse_moe.experts.{e}.w2.weight")
w2_param = p[e]
assert w2_name not in new_weights
new_weights[w2_name] = w2_param
elif n.endswith('.block_sparse_moe.router.layer.weight'):
gate_name = n.replace('.block_sparse_moe.router.layer.weight',
".block_sparse_moe.gate.weight")
assert gate_name not in new_weights
new_weights[gate_name] = p
else:
new_weights[n] = p
return mixtral.MixtralModel.load_weights(self, new_weights.items())
class GraniteMoeSharedForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
fall_back_to_pt_during_load = False
packed_modules_mapping = {
"qkv_proj": [
"q_proj",
"k_proj",
"v_proj",
],
}
# LoRA specific attributes
embedding_modules = {
"embed_tokens": "input_embeddings",
"lm_head": "output_embeddings",
}
embedding_padding_modules = ["lm_head"]
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.config = config
self.lora_config = lora_config
self.model = GraniteMoeSharedModel(vllm_config=vllm_config,
prefix=maybe_prefix(
prefix, "model"))
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,
quant_config=quant_config,
prefix=maybe_prefix(prefix, "lm_head"))
if config.tie_word_embeddings:
self.lm_head.weight = self.model.embed_tokens.weight
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size,
scale=1 /
self.config.logits_scaling)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, intermediate_tensors,
inputs_embeds)
return hidden_states
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 make_empty_intermediate_tensors(
self, batch_size: int, dtype: torch.dtype,
device: torch.device) -> IntermediateTensors:
return IntermediateTensors({
"hidden_states":
torch.zeros((batch_size, self.config.hidden_size),
dtype=dtype,
device=device),
"residual":
torch.zeros((batch_size, self.config.hidden_size),
dtype=dtype,
device=device),
})
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
loader = AutoWeightsLoader(
self,
skip_prefixes=(["lm_head."]
if self.config.tie_word_embeddings else None),
)
return loader.load_weights(weights)
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