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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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194
vllm/model_executor/models/granite.py
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@@ -23,6 +23,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only IBM Granite model compatible with HuggingFace weights."""
from collections.abc import Iterable
from itertools import islice
from typing import Any, Optional, Union
@@ -37,25 +38,36 @@ from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.linear import (
MergedColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
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, maybe_remap_kv_scale_name)
default_weight_loader,
maybe_remap_kv_scale_name,
)
from vllm.sequence import IntermediateTensors
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
make_layers, maybe_prefix)
from .utils import (
AutoWeightsLoader,
PPMissingLayer,
is_pp_missing_parameter,
make_layers,
maybe_prefix,
)
class GraniteMLP(nn.Module):
def __init__(
self,
hidden_size: int,
@@ -71,15 +83,19 @@ class GraniteMLP(nn.Module):
output_sizes=[intermediate_size] * 2,
bias=bias,
quant_config=quant_config,
prefix=f"{prefix}.gate_up_proj")
self.down_proj = RowParallelLinear(input_size=intermediate_size,
output_size=hidden_size,
bias=bias,
quant_config=quant_config,
prefix=f"{prefix}.down_proj")
prefix=f"{prefix}.gate_up_proj",
)
self.down_proj = RowParallelLinear(
input_size=intermediate_size,
output_size=hidden_size,
bias=bias,
quant_config=quant_config,
prefix=f"{prefix}.down_proj",
)
if hidden_act != "silu":
raise ValueError(f"Unsupported activation: {hidden_act}. "
"Only silu is supported for now.")
raise ValueError(
f"Unsupported activation: {hidden_act}. Only silu is supported for now."
)
self.act_fn = SiluAndMul()
def forward(self, x):
@@ -90,7 +106,6 @@ class GraniteMLP(nn.Module):
class GraniteAttention(nn.Module):
def __init__(
self,
config: GraniteConfig,
@@ -155,13 +170,15 @@ class GraniteAttention(nn.Module):
base=rope_theta,
rope_scaling=rope_scaling,
)
self.attn = Attention(self.num_heads,
self.head_dim,
self.scaling,
num_kv_heads=self.num_kv_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn")
self.attn = Attention(
self.num_heads,
self.head_dim,
self.scaling,
num_kv_heads=self.num_kv_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn",
)
def forward(
self,
@@ -177,7 +194,6 @@ class GraniteAttention(nn.Module):
class GraniteDecoderLayer(nn.Module):
def __init__(
self,
config: GraniteConfig,
@@ -191,21 +207,24 @@ class GraniteDecoderLayer(nn.Module):
rope_theta = getattr(config, "rope_theta", 10000)
rope_scaling = getattr(config, "rope_scaling", None)
if rope_scaling is not None and getattr(
config, "original_max_position_embeddings", None):
config, "original_max_position_embeddings", None
):
rope_scaling["original_max_position_embeddings"] = (
config.original_max_position_embeddings)
max_position_embeddings = getattr(config, "max_position_embeddings",
8192)
config.original_max_position_embeddings
)
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
# Support abacusai/Smaug-72B-v0.1 with attention_bias
# Support internlm/internlm-7b with bias
attention_bias = getattr(config, "attention_bias", False) or getattr(
config, "bias", False)
config, "bias", False
)
self.self_attn = GraniteAttention(
config=config,
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
num_kv_heads=getattr(config, "num_key_value_heads",
config.num_attention_heads),
num_kv_heads=getattr(
config, "num_key_value_heads", config.num_attention_heads
),
rope_theta=rope_theta,
rope_scaling=rope_scaling,
max_position_embeddings=max_position_embeddings,
@@ -223,10 +242,10 @@ class GraniteDecoderLayer(nn.Module):
bias=getattr(config, "mlp_bias", False),
prefix=f"{prefix}.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.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,
@@ -251,7 +270,6 @@ class GraniteDecoderLayer(nn.Module):
@support_torch_compile
class GraniteModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
@@ -262,12 +280,16 @@ class GraniteModel(nn.Module):
self.config = config
self.quant_config = quant_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
if get_pp_group().is_first_rank or (config.tie_word_embeddings
and get_pp_group().is_last_rank):
if get_pp_group().is_first_rank or (
config.tie_word_embeddings and get_pp_group().is_last_rank
):
self.embed_tokens = VocabParallelEmbedding(
self.vocab_size,
config.hidden_size,
@@ -275,18 +297,22 @@ class GraniteModel(nn.Module):
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=quant_config,
)
else:
self.embed_tokens = PPMissingLayer()
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: GraniteDecoderLayer(config=config,
cache_config=cache_config,
quant_config=quant_config,
prefix=prefix),
prefix=f"{prefix}.layers")
lambda prefix: GraniteDecoderLayer(
config=config,
cache_config=cache_config,
quant_config=quant_config,
prefix=prefix,
),
prefix=f"{prefix}.layers",
)
if get_pp_group().is_last_rank:
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
else:
@@ -317,15 +343,16 @@ class GraniteModel(nn.Module):
hidden_states = layer(positions, hidden_states)
if not get_pp_group().is_last_rank:
return IntermediateTensors({
"hidden_states": hidden_states,
})
return IntermediateTensors(
{
"hidden_states": hidden_states,
}
)
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"),
@@ -337,18 +364,19 @@ class GraniteModel(nn.Module):
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if (self.quant_config is not None and
(scale_name := self.quant_config.get_cache_scale(name))):
if self.quant_config is not None and (
scale_name := self.quant_config.get_cache_scale(name)
):
# Loading kv cache quantization scales
param = params_dict[scale_name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
loaded_weight[0])
weight_loader = getattr(param, "weight_loader", default_weight_loader)
loaded_weight = (
loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
)
weight_loader(param, loaded_weight)
loaded_params.add(scale_name)
continue
for (param_name, weight_name, shard_id) in stacked_params_mapping:
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
@@ -377,8 +405,7 @@ class GraniteModel(nn.Module):
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
@@ -414,8 +441,9 @@ class GraniteForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
self.lora_config = lora_config
self.quant_config = quant_config
self.model = GraniteModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.model = GraniteModel(
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
)
if get_pp_group().is_last_rank:
self.unpadded_vocab_size = config.vocab_size
if lora_config:
@@ -427,7 +455,8 @@ class GraniteForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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=quant_config,
prefix=maybe_prefix(prefix, "lm_head"),
)
@@ -438,9 +467,9 @@ class GraniteForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
if hasattr(config, "logits_scaling"):
logit_scale /= config.logits_scaling
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size,
scale=logit_scale)
self.logits_processor = LogitsProcessor(
self.unpadded_vocab_size, config.vocab_size, scale=logit_scale
)
else:
self.lm_head = PPMissingLayer()
@@ -454,32 +483,31 @@ class GraniteForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
model_output = self.model(input_ids, positions, intermediate_tensors,
inputs_embeds)
model_output = self.model(
input_ids, positions, intermediate_tensors, inputs_embeds
)
return model_output
def compute_logits(self,
hidden_states: torch.Tensor) -> Optional[torch.Tensor]:
def compute_logits(self, hidden_states: torch.Tensor) -> Optional[torch.Tensor]:
logits = self.logits_processor(self.lm_head, hidden_states)
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),
})
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
),
}
)
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
# With tie_word_embeddings, we can skip lm_head.weight
# The weight might appear unnecessarily in the files if the model is
# processed with quantization, LoRA, fine-tuning, etc.
skip_prefixes = (["lm_head."]
if self.config.tie_word_embeddings else None)
skip_prefixes = ["lm_head."] if self.config.tie_word_embeddings else None
loader = AutoWeightsLoader(
self,