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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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383
vllm/model_executor/models/granitemoehybrid.py
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@@ -1,6 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Inference-only GraniteMoeHybrid model."""
# Added by the IBM Team, 2025
from collections.abc import Iterable
from typing import Optional
@@ -15,58 +16,67 @@ from vllm.config import CacheConfig, ModelConfig, VllmConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.distributed.parallel_state import get_pp_group
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.linear import QKVParallelLinear, RowParallelLinear
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2
from vllm.model_executor.layers.mamba.mamba_utils import (
MambaStateDtypeCalculator, MambaStateShapeCalculator)
MambaStateDtypeCalculator,
MambaStateShapeCalculator,
)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
DEFAULT_VOCAB_PADDING_SIZE,
ParallelLMHead,
VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.sequence import IntermediateTensors
from .granitemoe import GraniteMoeMoE
from .granitemoeshared import GraniteMoeSharedMLP
from .interfaces import (HasInnerState, IsHybrid, SupportsLoRA, SupportsPP,
SupportsQuant)
from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
from .interfaces import HasInnerState, IsHybrid, SupportsLoRA, SupportsPP, SupportsQuant
from .utils import (
AutoWeightsLoader,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory,
make_layers,
maybe_prefix,
)
class GraniteMoeHybridMambaDecoderLayer(nn.Module):
def __init__(self,
config: GraniteMoeHybridConfig,
layer_idx: int,
model_config: Optional[ModelConfig] = None,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "") -> None:
def __init__(
self,
config: GraniteMoeHybridConfig,
layer_idx: int,
model_config: Optional[ModelConfig] = None,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.residual_multiplier = config.residual_multiplier
self.mamba = MambaMixer2(hidden_size= config.hidden_size,
ssm_state_size = config.mamba_d_state,
conv_kernel_size = config.mamba_d_conv,
intermediate_size = config.mamba_expand *\
config.hidden_size,
use_conv_bias = config.mamba_conv_bias,
use_bias = config.mamba_proj_bias,
n_groups=config.mamba_n_groups,
num_heads=config.mamba_n_heads,
head_dim=config.mamba_d_head,
rms_norm_eps=config.rms_norm_eps,
activation=config.hidden_act,
model_config=model_config,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.mixer")
self.mamba = MambaMixer2(
hidden_size=config.hidden_size,
ssm_state_size=config.mamba_d_state,
conv_kernel_size=config.mamba_d_conv,
intermediate_size=config.mamba_expand * config.hidden_size,
use_conv_bias=config.mamba_conv_bias,
use_bias=config.mamba_proj_bias,
n_groups=config.mamba_n_groups,
num_heads=config.mamba_n_heads,
head_dim=config.mamba_d_head,
rms_norm_eps=config.rms_norm_eps,
activation=config.hidden_act,
model_config=model_config,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.mixer",
)
self.block_sparse_moe = None
if getattr(config, "num_local_experts", 0) > 0:
@@ -76,20 +86,21 @@ class GraniteMoeHybridMambaDecoderLayer(nn.Module):
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"
prefix=f"{prefix}.block_sparse_moe",
)
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.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
)
def forward(
self,
@@ -114,8 +125,7 @@ class GraniteMoeHybridMambaDecoderLayer(nn.Module):
if self.block_sparse_moe is not None:
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)
hidden_states = moe_hidden_states + self.shared_mlp(hidden_states)
del moe_hidden_states
else:
hidden_states = self.shared_mlp(hidden_states)
@@ -125,7 +135,6 @@ class GraniteMoeHybridMambaDecoderLayer(nn.Module):
class GraniteMoeHybridAttentionDecoderLayer(nn.Module):
def __init__(
self,
config: GraniteMoeHybridConfig,
@@ -143,7 +152,8 @@ class GraniteMoeHybridAttentionDecoderLayer(nn.Module):
config,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn")
prefix=f"{prefix}.self_attn",
)
self.block_sparse_moe = None
if getattr(config, "num_local_experts", 0) > 0:
@@ -153,20 +163,21 @@ class GraniteMoeHybridAttentionDecoderLayer(nn.Module):
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"
prefix=f"{prefix}.block_sparse_moe",
)
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.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
)
def forward(
self,
@@ -194,8 +205,7 @@ class GraniteMoeHybridAttentionDecoderLayer(nn.Module):
if self.block_sparse_moe is not None:
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)
hidden_states = moe_hidden_states + self.shared_mlp(hidden_states)
del moe_hidden_states
else:
hidden_states = self.shared_mlp(hidden_states)
@@ -205,7 +215,6 @@ class GraniteMoeHybridAttentionDecoderLayer(nn.Module):
class GraniteMoeHybridAttention(nn.Module):
def __init__(
self,
config: GraniteMoeHybridConfig,
@@ -237,19 +246,23 @@ class GraniteMoeHybridAttention(nn.Module):
assert tp_size % self.total_num_kv_heads == 0
self.num_key_value_heads = max(1, self.total_num_kv_heads // tp_size)
self.qkv_proj = QKVParallelLinear(self.hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_kv_heads,
bias=self.attention_bias,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj")
self.qkv_proj = QKVParallelLinear(
self.hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_kv_heads,
bias=self.attention_bias,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj",
)
self.o_proj = RowParallelLinear(self.hidden_size,
self.hidden_size,
bias=self.attention_bias,
quant_config=quant_config,
prefix=f"{prefix}.o_proj")
self.o_proj = RowParallelLinear(
self.hidden_size,
self.hidden_size,
bias=self.attention_bias,
quant_config=quant_config,
prefix=f"{prefix}.o_proj",
)
if config.position_embedding_type == "rope":
self.rotary_emb = get_rope(
@@ -257,34 +270,38 @@ class GraniteMoeHybridAttention(nn.Module):
rotary_dim=self.head_dim,
max_position=config.max_position_embeddings,
base=int(config.rope_theta),
rope_scaling=config.rope_scaling \
if hasattr(config, "rope_scaling") \
and config.rope_scaling is not None else None,
rope_scaling=config.rope_scaling
if hasattr(config, "rope_scaling") and config.rope_scaling is not None
else None,
is_neox_style=True,
)
else:
self.rotary_emb = None
self.attn = Attention(self.num_heads,
self.head_dim,
self.attention_multiplier,
num_kv_heads=self.num_key_value_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn")
self.attn = Attention(
self.num_heads,
self.head_dim,
self.attention_multiplier,
num_kv_heads=self.num_key_value_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn",
)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
) -> torch.Tensor:
qkv, _ = self.qkv_proj(hidden_states)
query, key, value = qkv.split([
self.num_heads * self.head_dim, self.num_key_value_heads *
self.head_dim, self.num_key_value_heads * self.head_dim
],
dim=-1)
query, key, value = qkv.split(
[
self.num_heads * self.head_dim,
self.num_key_value_heads * self.head_dim,
self.num_key_value_heads * self.head_dim,
],
dim=-1,
)
if self.rotary_emb is not None:
query, key = self.rotary_emb(positions, query, key)
@@ -304,7 +321,6 @@ ALL_DECODER_LAYER_TYPES = {
@support_torch_compile
class GraniteMoeHybridModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
@@ -315,8 +331,11 @@ class GraniteMoeHybridModel(nn.Module):
lora_config = vllm_config.lora_config
self.config = config
lora_vocab = ((lora_config.lora_extra_vocab_size *
(lora_config.max_loras or 1)) if lora_config else 0)
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
@@ -329,8 +348,7 @@ class GraniteMoeHybridModel(nn.Module):
def get_layer(prefix: str):
layer_idx = int(prefix.rsplit(".", 1)[1])
layer_class = ALL_DECODER_LAYER_TYPES[
config.layer_types[layer_idx]]
layer_class = ALL_DECODER_LAYER_TYPES[config.layer_types[layer_idx]]
return layer_class(
config,
layer_idx,
@@ -341,10 +359,11 @@ class GraniteMoeHybridModel(nn.Module):
)
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))
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
)
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -358,7 +377,6 @@ class GraniteMoeHybridModel(nn.Module):
intermediate_tensors: Optional[IntermediateTensors] = None,
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
@@ -368,7 +386,7 @@ class GraniteMoeHybridModel(nn.Module):
residual = None
else:
if intermediate_tensors is None:
raise RuntimeError('Intermediate tensors may not be None!')
raise RuntimeError("Intermediate tensors may not be None!")
hidden_states = intermediate_tensors["hidden_states"]
residual = intermediate_tensors["residual"]
@@ -376,21 +394,19 @@ class GraniteMoeHybridModel(nn.Module):
for i, layer in enumerate(self.layers):
if isinstance(layer, GraniteMoeHybridAttentionDecoderLayer):
num_attn += 1
hidden_states, residual = layer(positions=positions,
hidden_states=hidden_states,
residual=residual)
hidden_states, residual = layer(
positions=positions, hidden_states=hidden_states, residual=residual
)
if not get_pp_group().is_last_rank:
return IntermediateTensors({
"hidden_states": hidden_states,
"residual": residual
})
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]:
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"),
@@ -402,8 +418,7 @@ class GraniteMoeHybridModel(nn.Module):
def _load(n, p):
param = params_dict[n]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, p)
loaded_params.add(n)
@@ -411,20 +426,14 @@ class GraniteMoeHybridModel(nn.Module):
# Skip layers on other devices.
if not is_pp_missing_parameter(n, self):
param = params_dict[n]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, p, shard_id)
loaded_params.add(n)
def _load_expert(n, p, name, shard_id, expert_id):
param = params_dict[n]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param,
p,
name,
shard_id=shard_id,
expert_id=expert_id)
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, p, name, shard_id=shard_id, expert_id=expert_id)
loaded_params.add(n)
for n, p in weights:
@@ -437,49 +446,62 @@ class GraniteMoeHybridModel(nn.Module):
# to vLLM (experts_w13({e}.w1, {e}.w2), experts_w3({e}.w3), gate)
# The renaming and parameter loading logic is the same for weight
# and weight_scale tensors so we can reuse them without issues.
if (n.endswith('.block_sparse_moe.input_linear.weight') or
n.endswith('.block_sparse_moe.input_linear.weight_scale')):
if n.endswith(".block_sparse_moe.input_linear.weight") or n.endswith(
".block_sparse_moe.input_linear.weight_scale"
):
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")
".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")
".block_sparse_moe.input_linear.weight",
f".block_sparse_moe.experts.{e}.w3.weight",
)
w1_param, w3_param = p[e].chunk(2, dim=0)
_load_expert(n.replace('.input_linear.', '.experts.w13_'),
w1_param,
w1_name,
shard_id='w1',
expert_id=e)
_load_expert(n.replace('.input_linear.', '.experts.w13_'),
w3_param,
w3_name,
shard_id='w3',
expert_id=e)
elif (n.endswith('.block_sparse_moe.output_linear.weight') or
n.endswith('.block_sparse_moe.output_linear.weight_scale')):
_load_expert(
n.replace(".input_linear.", ".experts.w13_"),
w1_param,
w1_name,
shard_id="w1",
expert_id=e,
)
_load_expert(
n.replace(".input_linear.", ".experts.w13_"),
w3_param,
w3_name,
shard_id="w3",
expert_id=e,
)
elif n.endswith(".block_sparse_moe.output_linear.weight") or n.endswith(
".block_sparse_moe.output_linear.weight_scale"
):
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")
".block_sparse_moe.output_linear.weight",
f".block_sparse_moe.experts.{e}.w2.weight",
)
w2_param = p[e]
_load_expert(n.replace('.output_linear.', '.experts.w2_'),
w2_param,
w2_name,
shard_id='w2',
expert_id=e)
elif n.endswith('.block_sparse_moe.router.layer.weight'):
gate_name = n.replace('.block_sparse_moe.router.layer.weight',
".block_sparse_moe.gate.weight")
_load_expert(
n.replace(".output_linear.", ".experts.w2_"),
w2_param,
w2_name,
shard_id="w2",
expert_id=e,
)
elif n.endswith(".block_sparse_moe.router.layer.weight"):
gate_name = n.replace(
".block_sparse_moe.router.layer.weight",
".block_sparse_moe.gate.weight",
)
_load(gate_name, p)
else:
loaded = False
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name in n:
_load_shard(n.replace(weight_name, param_name),
p,
shard_id=shard_id)
_load_shard(
n.replace(weight_name, param_name), p, shard_id=shard_id
)
loaded = True
if not loaded:
_load(n, p)
@@ -487,8 +509,9 @@ class GraniteMoeHybridModel(nn.Module):
return loaded_params
class GraniteMoeHybridForCausalLM(nn.Module, HasInnerState, SupportsLoRA,
SupportsPP, IsHybrid, SupportsQuant):
class GraniteMoeHybridForCausalLM(
nn.Module, HasInnerState, SupportsLoRA, SupportsPP, IsHybrid, SupportsQuant
):
packed_modules_mapping = {
"qkv_proj": [
"q_proj",
@@ -507,7 +530,6 @@ class GraniteMoeHybridForCausalLM(nn.Module, HasInnerState, SupportsLoRA,
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,
@@ -554,9 +576,9 @@ class GraniteMoeHybridForCausalLM(nn.Module, HasInnerState, SupportsLoRA,
self.quant_config = vllm_config.quant_config
self.config = config
self.scheduler_config = scheduler_config
self.model = GraniteMoeHybridModel(vllm_config=vllm_config,
prefix=maybe_prefix(
prefix, "model"))
self.model = GraniteMoeHybridModel(
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
@@ -568,31 +590,37 @@ class GraniteMoeHybridForCausalLM(nn.Module, HasInnerState, SupportsLoRA,
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,
if not lora_config
else lora_config.lora_vocab_padding_size,
quant_config=self.quant_config,
prefix=maybe_prefix(prefix, "lm_head"))
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)
self.logits_processor = LogitsProcessor(
self.unpadded_vocab_size,
config.vocab_size,
scale=1 / self.config.logits_scaling,
)
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
self.model.make_empty_intermediate_tensors
)
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,
**kwargs):
hidden_states = self.model(input_ids, positions, intermediate_tensors,
inputs_embeds)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
**kwargs,
):
hidden_states = self.model(
input_ids, positions, intermediate_tensors, inputs_embeds
)
return hidden_states
@@ -603,7 +631,6 @@ class GraniteMoeHybridForCausalLM(nn.Module, HasInnerState, SupportsLoRA,
logits = self.logits_processor(self.lm_head, hidden_states)
return logits
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
loader = AutoWeightsLoader(self)
return loader.load_weights(weights)