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Commit 4b4eeb26 authored by zhuwenwen's avatar zhuwenwen
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Merge remote-tracking branch 'mirror/main'

parents 2216a4e5 4fdc581f
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Showing with 709 additions and 322 deletions
vllm/engine/llm_engine.py
View file @ 4b4eeb26
......@@ -254,7 +254,7 @@ class LLMEngine:
"num_scheduler_steps=%d, chunked_prefill_enabled=%s "
"multi_step_stream_outputs=%s, enable_prefix_caching=%s, "
"use_async_output_proc=%s, use_cached_outputs=%s, "
"mm_processor_kwargs=%s)",
"chat_template_text_format=%s, mm_processor_kwargs=%s)",
VLLM_VERSION,
model_config.model,
speculative_config,
......@@ -289,6 +289,7 @@ class LLMEngine:
cache_config.enable_prefix_caching,
model_config.use_async_output_proc,
use_cached_outputs,
model_config.chat_template_text_format,
model_config.mm_processor_kwargs,
)
# TODO(woosuk): Print more configs in debug mode.
......@@ -646,10 +647,24 @@ class LLMEngine:
prompt_adapter_request: Optional[PromptAdapterRequest],
trace_headers: Optional[Mapping[str, str]] = None,
priority: int = 0,
) -> SequenceGroup:
) -> Optional[SequenceGroup]:
"""Add a processed request to the engine's request pool.
return the created sequence group.
"""
if isinstance(params, SamplingParams) and params.n > 1:
ParallelSampleSequenceGroup.add_request(
request_id,
self,
params,
processed_inputs=processed_inputs,
arrival_time=arrival_time,
lora_request=lora_request,
trace_headers=trace_headers,
prompt_adapter_request=prompt_adapter_request,
priority=priority,
)
return None
self._validate_model_inputs(processed_inputs)
# Create the sequences.
block_size = self.cache_config.block_size
......@@ -720,7 +735,7 @@ class LLMEngine:
trace_headers: Optional[Mapping[str, str]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
priority: int = 0,
) -> Optional[SequenceGroup]:
) -> None:
...
@overload
......@@ -734,7 +749,7 @@ class LLMEngine:
trace_headers: Optional[Mapping[str, str]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
priority: int = 0,
) -> Optional[SequenceGroup]:
) -> None:
...
@deprecate_kwargs(
......@@ -753,7 +768,7 @@ class LLMEngine:
priority: int = 0,
*,
inputs: Optional[PromptType] = None, # DEPRECATED
) -> Optional[SequenceGroup]:
) -> None:
"""Add a request to the engine's request pool.
The request is added to the request pool and will be processed by the
......@@ -797,22 +812,6 @@ class LLMEngine:
>>> # continue the request processing
>>> ...
"""
if isinstance(params, SamplingParams) and params.n > 1:
ParallelSampleSequenceGroup.add_request(
request_id,
self,
params,
prompt=prompt,
arrival_time=arrival_time,
lora_request=lora_request,
trace_headers=trace_headers,
prompt_adapter_request=prompt_adapter_request,
priority=priority,
inputs=inputs,
)
return None
if inputs is not None:
prompt = inputs
assert prompt is not None and params is not None
......@@ -843,7 +842,7 @@ class LLMEngine:
processed_inputs["mm_processor_kwargs"] = preprocessed_inputs.get(
"mm_processor_kwargs")
return self._add_processed_request(
self._add_processed_request(
request_id=request_id,
processed_inputs=processed_inputs,
params=params,
......@@ -1612,7 +1611,7 @@ class LLMEngine:
# KV Cache Usage in %
num_total_gpu = self.cache_config.num_gpu_blocks
gpu_cache_usage_sys = 0.
if num_total_gpu is not None:
if num_total_gpu: # Guard against both None and 0
num_free_gpu = sum(
scheduler.block_manager.get_num_free_gpu_blocks()
for scheduler in self.scheduler)
......@@ -1620,7 +1619,7 @@ class LLMEngine:
num_total_cpu = self.cache_config.num_cpu_blocks
cpu_cache_usage_sys = 0.
if num_total_cpu is not None and num_total_cpu > 0:
if num_total_cpu: # Guard against both None and 0
num_free_cpu = sum(
scheduler.block_manager.get_num_free_cpu_blocks()
for scheduler in self.scheduler)
......
vllm/engine/output_processor/single_step.py
View file @ 4b4eeb26
from typing import Dict, List, Tuple
from typing import List
from vllm.config import SchedulerConfig
from vllm.core.scheduler import Scheduler
......@@ -6,9 +6,8 @@ from vllm.engine.output_processor.interfaces import (
SequenceGroupOutputProcessor)
from vllm.engine.output_processor.stop_checker import StopChecker
from vllm.logger import init_logger
from vllm.sequence import (CompletionSequenceGroupOutput, Sequence,
SequenceGroup, SequenceGroupOutput, SequenceOutput,
SequenceStatus)
from vllm.sequence import (CompletionSequenceGroupOutput, SequenceGroup,
SequenceGroupOutput)
from vllm.transformers_utils.detokenizer import Detokenizer
from vllm.utils import Counter
......@@ -114,11 +113,9 @@ class SingleStepOutputProcessor(SequenceGroupOutputProcessor):
outputs: SequenceGroupOutput,
is_async: bool) -> None:
sampling_params = seq_group.sampling_params
if sampling_params.n == 1:
# only have one output sample
sample = outputs.samples[0]
# only have one sequence
seq = seq_group.seqs[0]
seq = seq_group.first_seq
if not is_async:
seq.append_token_id(sample.output_token, sample.logprobs)
if sampling_params.detokenize and self.detokenizer:
......@@ -135,83 +132,3 @@ class SingleStepOutputProcessor(SequenceGroupOutputProcessor):
if seq.is_finished():
for scheduler in self.scheduler:
scheduler.free_seq(seq)
return
# TODO: Add support for async for beam search
assert not is_async
# Process samples
samples = outputs.samples
parent_seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING)
parent_child_dict: Dict[int, List[SequenceOutput]] = {
parent_seq.seq_id: []
for parent_seq in parent_seqs
}
for sample in samples:
# Guard against a KeyError which can occur if the request was
# aborted while the output was generated
if (child_list :=
parent_child_dict.get(sample.parent_seq_id)) is not None:
child_list.append(sample)
# List of (child, parent)
child_seqs: List[Tuple[Sequence, Sequence]] = []
# Process the child samples for each parent sequence
for parent in parent_seqs:
child_samples: List[SequenceOutput] = parent_child_dict[
parent.seq_id]
if len(child_samples) == 0:
# This parent sequence has no children samples. Remove
# the parent sequence from the sequence group since it will
# not be used in the future iterations.
parent.status = SequenceStatus.FINISHED_ABORTED
seq_group.remove(parent.seq_id)
for scheduler in self.scheduler:
scheduler.free_seq(parent)
continue
# Fork the parent sequence if there are multiple child samples.
for child_sample in child_samples[:-1]:
new_child_seq_id: int = next(self.seq_counter)
child = parent.fork(new_child_seq_id)
child.append_token_id(child_sample.output_token,
child_sample.logprobs)
child_seqs.append((child, parent))
# Continue the parent sequence for the last child sample.
# We reuse the parent sequence here to reduce redundant memory
# copies, especially when using non-beam search sampling methods.
last_child_sample = child_samples[-1]
parent.append_token_id(last_child_sample.output_token,
last_child_sample.logprobs)
child_seqs.append((parent, parent))
for seq, _ in child_seqs:
if sampling_params.detokenize and self.detokenizer:
new_char_count = self.detokenizer.decode_sequence_inplace(
seq, sampling_params)
else:
new_char_count = 0
self.stop_checker.maybe_stop_sequence(
seq,
new_char_count,
sampling_params,
lora_req=seq_group.lora_request,
)
# For newly created child sequences, add them to the sequence group
# and fork them in block manager if they are not finished.
for seq, parent in child_seqs:
if seq is not parent:
seq_group.add(seq)
if not seq.is_finished():
for scheduler in self.scheduler:
scheduler.fork_seq(parent, seq)
# Free the finished and selected parent sequences' memory in block
# manager. Keep them in the sequence group as candidate output.
# NOTE: we need to fork the new sequences before freeing the
# old sequences.
for seq, parent in child_seqs:
if seq is parent and seq.is_finished():
for scheduler in self.scheduler:
scheduler.free_seq(seq)
return
vllm/entrypoints/chat_utils.py
View file @ 4b4eeb26
......@@ -121,7 +121,7 @@ class ConversationMessage(TypedDict, total=False):
role: Required[str]
"""The role of the message's author."""
content: Optional[str]
content: Union[Optional[str], List[Dict[str, str]]]
"""The contents of the message"""
tool_call_id: Optional[str]
......@@ -196,7 +196,10 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
elif modality == "audio":
if model_type == "ultravox":
return "<|reserved_special_token_0|>"
raise TypeError(f"Unknown {modality} model type: {model_type}")
if model_type == "qwen2_audio":
return (f"Audio {current_count}: "
f"<|audio_bos|><|AUDIO|><|audio_eos|>")
raise TypeError(f"Unknown model type: {model_type}")
elif modality == "video":
if model_type == "qwen2_vl":
return "<|vision_start|><|video_pad|><|vision_end|>"
......@@ -428,7 +431,7 @@ MM_PARSER_MAP: Dict[str, Callable[[ChatCompletionContentPartParam], str]] = {
def _parse_chat_message_content_mm_part(
part: ChatCompletionContentPartParam) -> Tuple[str, str]:
"""
Parses a given multi modal content part based on its type.
Parses a given multi-modal content part based on its type.
Args:
part: A dict containing the content part, with a potential 'type' field.
......@@ -482,54 +485,76 @@ def _parse_chat_message_content_parts(
role: str,
parts: Iterable[ChatCompletionContentPartParam],
mm_tracker: BaseMultiModalItemTracker,
chat_template_text_format: str,
) -> List[ConversationMessage]:
texts: List[str] = []
content: List[Union[str, Dict[str, str]]] = []
mm_parser = mm_tracker.create_parser()
keep_multimodal_content = \
wrap_dicts = \
mm_tracker._model_config.hf_config.model_type in \
MODEL_KEEP_MULTI_MODAL_CONTENT
MODEL_KEEP_MULTI_MODAL_CONTENT or \
(chat_template_text_format == "openai")
has_image = False
for part in parts:
parse_res = _parse_chat_message_content_part(
part,
mm_parser,
wrap_dicts=wrap_dicts,
)
if parse_res:
content.append(parse_res)
if wrap_dicts:
# Parsing wraps images and texts as interleaved dictionaries
return [ConversationMessage(role=role,
content=content)] # type: ignore
texts = cast(List[str], content)
text_prompt = "\n".join(texts)
mm_placeholder_counts = mm_parser.mm_placeholder_counts()
if mm_placeholder_counts:
text_prompt = _get_full_multimodal_text_prompt(mm_placeholder_counts,
text_prompt)
return [ConversationMessage(role=role, content=text_prompt)]
def _parse_chat_message_content_part(
part: ChatCompletionContentPartParam,
mm_parser: BaseMultiModalContentParser,
wrap_dicts: bool) -> Optional[Union[str, Dict[str, str]]]:
"""Parses a single part of a conversation. If wrap_dicts is True,
structured dictionary pieces for texts and images will be
wrapped in dictionaries, i.e., {"type": "text", "text", ...} and
{"type": "image"}, respectively. Otherwise multimodal data will be
handled by mm_parser, and texts will be returned as strings to be joined
with multimodal placeholders.
"""
if isinstance(part, str): # Handle plain text parts
text = _TextParser(part)
texts.append(text)
else: # Handle structured dictionary parts
return text
# Handle structured dictionary parts
part_type, content = _parse_chat_message_content_mm_part(part)
# if part_type is text/refusal/image_url/audio_url but
# content is empty, logg a warning and skip
# content is empty, log a warning and skip
if part_type in VALID_MESSAGE_CONTENT_MM_PART_TYPES and not content:
logger.warning("Skipping multimodal part "
"with empty / unparsable content.")
continue
logger.warning(
"Skipping multimodal part (type: '%s')"
"with empty / unparsable content.", part_type)
return None
if part_type in ("text", "refusal"):
texts.append(content)
elif part_type == "image_url":
return {'type': 'text', 'text': content} if wrap_dicts else content
if part_type == "image_url":
mm_parser.parse_image(content)
has_image = True
elif part_type == "audio_url":
mm_parser.parse_audio(content)
else:
raise NotImplementedError(f"Unknown part type: {part_type}")
return {'type': 'image'} if wrap_dicts else None
text_prompt = "\n".join(texts)
if keep_multimodal_content:
text_prompt = "\n".join(texts)
role_content = [{'type': 'text', 'text': text_prompt}]
if part_type == "audio_url":
mm_parser.parse_audio(content)
return {'type': 'audio'} if wrap_dicts else None
if has_image:
role_content = [{'type': 'image'}] + role_content
return [ConversationMessage(role=role,
content=role_content)] # type: ignore
else:
mm_placeholder_counts = mm_parser.mm_placeholder_counts()
if mm_placeholder_counts:
text_prompt = _get_full_multimodal_text_prompt(
mm_placeholder_counts, text_prompt)
return [ConversationMessage(role=role, content=text_prompt)]
raise NotImplementedError(f"Unknown part type: {part_type}")
# No need to validate using Pydantic again
......@@ -540,6 +565,7 @@ _ToolParser = partial(cast, ChatCompletionToolMessageParam)
def _parse_chat_message_content(
message: ChatCompletionMessageParam,
mm_tracker: BaseMultiModalItemTracker,
chat_template_text_format: str,
) -> List[ConversationMessage]:
role = message["role"]
content = message.get("content")
......@@ -555,6 +581,7 @@ def _parse_chat_message_content(
role,
content, # type: ignore
mm_tracker,
chat_template_text_format,
)
for result_msg in result:
......@@ -598,7 +625,11 @@ def parse_chat_messages(
mm_tracker = MultiModalItemTracker(model_config, tokenizer)
for msg in messages:
sub_messages = _parse_chat_message_content(msg, mm_tracker)
sub_messages = _parse_chat_message_content(
msg,
mm_tracker,
model_config.chat_template_text_format,
)
conversation.extend(sub_messages)
......@@ -616,7 +647,11 @@ def parse_chat_messages_futures(
mm_tracker = AsyncMultiModalItemTracker(model_config, tokenizer)
for msg in messages:
sub_messages = _parse_chat_message_content(msg, mm_tracker)
sub_messages = _parse_chat_message_content(
msg,
mm_tracker,
model_config.chat_template_text_format,
)
conversation.extend(sub_messages)
......
vllm/entrypoints/openai/serving_chat.py
View file @ 4b4eeb26
......@@ -384,7 +384,7 @@ class OpenAIServingChat(OpenAIServing):
# Send response to echo the input portion of the
# last message
if request.echo or request.continue_final_message:
last_msg_content: str = ""
last_msg_content: Union[str, List[Dict[str, str]]] = ""
if conversation and "content" in conversation[
-1] and conversation[-1].get("role") == role:
last_msg_content = conversation[-1]["content"] or ""
......@@ -724,10 +724,13 @@ class OpenAIServingChat(OpenAIServing):
choices.append(choice_data)
if request.echo or request.continue_final_message:
last_msg_content = ""
last_msg_content: Union[str, List[Dict[str, str]]] = ""
if conversation and "content" in conversation[-1] and conversation[
-1].get("role") == role:
last_msg_content = conversation[-1]["content"] or ""
if isinstance(last_msg_content, list):
last_msg_content = "\n".join(msg['text']
for msg in last_msg_content)
for choice in choices:
full_message = last_msg_content + (choice.message.content
......
vllm/executor/ray_utils.py
View file @ 4b4eeb26
......@@ -10,7 +10,7 @@ from vllm.executor.msgspec_utils import decode_hook, encode_hook
from vllm.logger import init_logger
from vllm.platforms import current_platform
from vllm.sequence import ExecuteModelRequest, IntermediateTensors
from vllm.utils import get_ip, is_hip, is_xpu
from vllm.utils import get_ip, is_hip
from vllm.worker.worker_base import WorkerWrapperBase
logger = init_logger(__name__)
......@@ -231,7 +231,7 @@ def initialize_ray_cluster(
assert_ray_available()
# Connect to a ray cluster.
if is_hip() or is_xpu():
if is_hip() or current_platform.is_xpu():
ray.init(address=ray_address,
ignore_reinit_error=True,
num_gpus=parallel_config.world_size)
......
vllm/model_executor/custom_op.py
View file @ 4b4eeb26
......@@ -7,7 +7,7 @@ import vllm.envs as envs
from vllm.compilation.levels import CompilationLevel
from vllm.logger import init_logger
from vllm.platforms import current_platform
from vllm.utils import is_hip, is_xpu, print_warning_once
from vllm.utils import is_hip, print_warning_once
logger = init_logger(__name__)
......@@ -78,7 +78,7 @@ class CustomOp(nn.Module):
return self.forward_cpu
elif current_platform.is_tpu():
return self.forward_tpu
elif is_xpu():
elif current_platform.is_xpu():
return self.forward_xpu
else:
return self.forward_cuda
......
vllm/model_executor/layers/quantization/awq.py
View file @ 4b4eeb26
......@@ -5,7 +5,8 @@ import os
import torch.nn.functional as F
from vllm import _custom_ops as ops
from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
UnquantizedLinearMethod)
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.parameter import (GroupQuantScaleParameter,
......@@ -37,10 +38,12 @@ class AWQConfig(QuantizationConfig):
weight_bits: int,
group_size: int,
zero_point: bool,
modules_to_not_convert: Optional[List[str]] = None,
) -> None:
self.weight_bits = weight_bits
self.group_size = group_size
self.zero_point = zero_point
self.modules_to_not_convert = modules_to_not_convert or []
if self.weight_bits != 4:
raise ValueError(
......@@ -51,7 +54,8 @@ class AWQConfig(QuantizationConfig):
def __repr__(self) -> str:
return (f"AWQConfig(weight_bits={self.weight_bits}, "
f"group_size={self.group_size}, "
f"zero_point={self.zero_point})")
f"zero_point={self.zero_point}, "
f"modules_to_not_convert={self.modules_to_not_convert})")
def get_name(self) -> str:
return "awq"
......@@ -77,11 +81,15 @@ class AWQConfig(QuantizationConfig):
weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
zero_point = cls.get_from_keys(config, ["zero_point"])
return cls(weight_bits, group_size, zero_point)
modules_to_not_convert = cls.get_from_keys_or(
config, ["modules_to_not_convert"], None)
return cls(weight_bits, group_size, zero_point, modules_to_not_convert)
def get_quant_method(self, layer: torch.nn.Module,
prefix: str) -> Optional["AWQLinearMethod"]:
prefix: str) -> Optional["LinearMethodBase"]:
if isinstance(layer, LinearBase):
if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
return UnquantizedLinearMethod()
return AWQLinearMethod(self)
return None
......@@ -89,6 +97,10 @@ class AWQConfig(QuantizationConfig):
return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
def is_layer_skipped_awq(prefix: str, modules_to_not_convert: List[str]):
return any(module_name in prefix for module_name in modules_to_not_convert)
class AWQLinearMethod(LinearMethodBase):
"""Linear method for AWQ.
......
vllm/model_executor/models/baichuan.py
View file @ 4b4eeb26
......@@ -28,6 +28,7 @@ import os
import re
from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, LoRAConfig
from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
......@@ -264,6 +265,7 @@ class BaiChuanDecoderLayer(nn.Module):
return hidden_states, residual
@support_torch_compile
class BaiChuanModel(nn.Module):
def __init__(self,
......@@ -527,7 +529,9 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
qweight.data=torch.cat((qweight.data,qweight_pad),dim=1).contiguous()
class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
"""Baichuan 13B and Baichuan2 7B/13B."""
"""Baichuan 13B and Baichuan2 7B/13B.
NOTE: the class name has a lower case 'c'.
"""
def __init__(
self,
......@@ -545,7 +549,9 @@ class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
"""Baichuan 7B."""
"""Baichuan 7B.
NOTE: the class name has an upper case 'C'.
"""
def __init__(
self,
......
vllm/model_executor/models/blip.py
View file @ 4b4eeb26
......@@ -122,7 +122,7 @@ def input_processor_for_blip(
# Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/blip/modeling_blip.py#L164 # noqa
class BlipVisionEmbeddings(nn.Module):
def __init__(self, config: BlipVisionConfig):
def __init__(self, config: Union[BlipVisionConfig, Blip2VisionConfig]):
super().__init__()
self.config = config
......@@ -167,9 +167,10 @@ class BlipParallelAttention(nn.Module):
def __init__(
self,
config: BlipVisionConfig,
config: Union[BlipVisionConfig, Blip2VisionConfig],
quant_config: Optional[QuantizationConfig] = None,
):
prefix: str = "",
) -> None:
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
......@@ -189,11 +190,13 @@ class BlipParallelAttention(nn.Module):
self.num_heads,
bias=config.qkv_bias,
quant_config=quant_config,
prefix=f"{prefix}.qkv",
)
self.projection = RowParallelLinear(
self.embed_dim,
self.embed_dim,
quant_config=quant_config,
prefix=f"{prefix}.projection",
)
self.tp_size = get_tensor_model_parallel_world_size()
......@@ -235,9 +238,12 @@ class BlipParallelAttention(nn.Module):
class BlipMLP(nn.Module):
def __init__(self,
def __init__(
self,
config: BlipVisionConfig,
quant_config: Optional[QuantizationConfig] = None):
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
......@@ -246,11 +252,13 @@ class BlipMLP(nn.Module):
self.fc1 = ColumnParallelLinear(config.hidden_size,
config.intermediate_size,
bias=True,
quant_config=quant_config)
quant_config=quant_config,
prefix=f"{prefix}.fc1")
self.fc2 = RowParallelLinear(config.intermediate_size,
config.hidden_size,
bias=True,
quant_config=quant_config)
quant_config=quant_config,
prefix=f"{prefix}.fc2")
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states, _ = self.fc1(hidden_states)
......@@ -262,24 +270,32 @@ class BlipMLP(nn.Module):
class BlipEncoderLayer(nn.Module):
def __init__(self,
def __init__(
self,
config: BlipVisionConfig,
quant_config: Optional[QuantizationConfig] = None):
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
# fallback to sdpa attention if tp unavailable
num_heads = config.num_attention_heads
tp_size = get_tensor_model_parallel_world_size()
if USE_XFORMERS_OPS and num_heads % tp_size == 0:
self.self_attn = BlipParallelAttention(config,
quant_config=quant_config)
self.self_attn = BlipParallelAttention(
config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
)
else:
# Blip doesn't have SDPA attention implemented in transformers
# use eager attention instead for cpu backend
self.self_attn = BlipAttention(config)
self.layer_norm1 = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
self.mlp = BlipMLP(config, quant_config=quant_config)
self.mlp = BlipMLP(config,
quant_config=quant_config,
prefix=f"{prefix}.mlp")
self.layer_norm2 = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
......@@ -307,10 +323,13 @@ class BlipEncoder(nn.Module):
config: BlipConfig
"""
def __init__(self,
def __init__(
self,
config: BlipVisionConfig,
quant_config: Optional[QuantizationConfig] = None,
num_hidden_layers_override: Optional[int] = None):
num_hidden_layers_override: Optional[int] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
......@@ -321,8 +340,10 @@ class BlipEncoder(nn.Module):
num_hidden_layers = num_hidden_layers_override
self.layers = nn.ModuleList([
BlipEncoderLayer(config=config, quant_config=quant_config)
for _ in range(num_hidden_layers)
BlipEncoderLayer(config=config,
quant_config=quant_config,
prefix=f"{prefix}.layers.{layer_idx}")
for layer_idx in range(num_hidden_layers)
])
def forward(self, inputs_embeds: torch.Tensor):
......@@ -337,10 +358,15 @@ class BlipVisionModel(nn.Module):
config_class = BlipVisionConfig
main_input_name = "pixel_values"
def __init__(self,
def __init__(
self,
config: BlipVisionConfig,
quant_config: Optional[QuantizationConfig] = None,
num_hidden_layers_override: Optional[int] = None):
*,
num_hidden_layers_override: Optional[int] = None,
require_post_norm: Optional[bool] = None,
prefix: str = "",
) -> None:
super().__init__()
tp_size = get_tensor_model_parallel_world_size()
......@@ -354,19 +380,24 @@ class BlipVisionModel(nn.Module):
config=config,
quant_config=quant_config,
num_hidden_layers_override=num_hidden_layers_override,
prefix=f"{prefix}.encoder",
)
num_hidden_layers = config.num_hidden_layers
if len(self.encoder.layers) > config.num_hidden_layers:
raise ValueError(
f"The original encoder only has {config.num_hidden_layers} "
f"The original encoder only has {num_hidden_layers} "
f"layers, but you requested {len(self.encoder.layers)} layers."
)
elif len(self.encoder.layers) == config.num_hidden_layers:
# If possible, skip post_layernorm to conserve memory
if require_post_norm is None:
require_post_norm = len(self.encoder.layers) == num_hidden_layers
if require_post_norm:
self.post_layernorm = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
else:
# post_layernorm is unused when we extract intermediate features
# In this case, we can skip it to conserve memory
self.post_layernorm = None
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
......
vllm/model_executor/models/blip2.py
View file @ 4b4eeb26
......@@ -490,7 +490,7 @@ class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
self.multimodal_config = multimodal_config
# TODO: Optionally initializes this for supporting embeddings.
self.vision_model = BlipVisionModel(config.vision_config)
self.vision_model = BlipVisionModel(config.vision_config, quant_config)
self.query_tokens = nn.Parameter(
torch.zeros(1, config.num_query_tokens,
......
vllm/model_executor/models/bloom.py
View file @ 4b4eeb26
......@@ -26,6 +26,7 @@ import os
import re
from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig
from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
......@@ -226,6 +227,7 @@ class BloomBlock(nn.Module):
return output
@support_torch_compile
class BloomModel(nn.Module):
def __init__(
......
vllm/model_executor/models/chatglm.py
View file @ 4b4eeb26
......@@ -15,8 +15,9 @@ import re
from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig, LoRAConfig, MultiModalConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.inputs import INPUT_REGISTRY, DecoderOnlyInputs, InputContext
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
token_inputs)
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
......@@ -24,8 +25,7 @@ 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.base_config import (
QuantizationConfig)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.layers.vocab_parallel_embedding import (
......@@ -41,11 +41,13 @@ from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
SequenceData)
from vllm.transformers_utils.configs import ChatGLMConfig
from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
from .utils import (is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers)
from vllm import _custom_ops as ops
from vllm.model_executor.utils import pad_weight, gemm_bank_conf
from .interfaces import SupportsLoRA, SupportsMultiModal
logger = init_logger(__name__)
......@@ -155,6 +157,10 @@ def find_all_positions(input_ids: List[int], target: int) -> List[int]:
def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs):
multi_modal_data = inputs.get("multi_modal_data")
if multi_modal_data is None or "image" not in multi_modal_data:
return inputs
hf_config = ctx.get_hf_config(ChatGLMConfig)
vision_config = getattr(hf_config, 'vision_config', None)
......@@ -166,8 +172,8 @@ def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs):
msg = f"Unsupported vision config: {type(vision_config)}"
raise NotImplementedError(msg)
input_ids = inputs.get("prompt_token_ids")
position_ids = inputs.get("position_ids")
input_ids = inputs["prompt_token_ids"]
tokenizer = cached_get_tokenizer(
ctx.model_config.model,
trust_remote_code=ctx.model_config.trust_remote_code)
......@@ -176,20 +182,19 @@ def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs):
raw_batch_data = tokenizer.apply_chat_template(
conversation=[{
"role": "user",
"image": inputs['multi_modal_data']["image"],
"content": inputs['prompt']
"image": multi_modal_data["image"],
"content": inputs['prompt'],
}],
add_generation_prompt=True,
tokenize=True,
return_tensors="pt",
return_dict=True).data
return_dict=True,
).data
except Exception:
logger.error("Failed to process content (%s)", inputs['prompt'])
raise
input_ids = raw_batch_data['input_ids'][0].tolist()
if position_ids is None:
position_ids = list(range(len(input_ids)))
boi_token_id = hf_config.boi_token_id
eoi_token_id = hf_config.eoi_token_id
boi_positions = find_all_positions(input_ids, boi_token_id)
......@@ -198,7 +203,6 @@ def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs):
assert len(boi_positions) == len(eoi_positions)
new_input_ids = []
new_position_ids = []
final_processed_position = 0
final_processed_position = 0
......@@ -206,29 +210,28 @@ def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs):
assert boi_position < eoi_position
new_input_ids.extend(input_ids[final_processed_position:boi_position +
1])
new_position_ids.extend(
list(range(final_processed_position, boi_position + 1)))
new_input_ids.extend([input_ids[boi_position + 1]] *
image_placeholder_length)
new_position_ids.extend([boi_position + 1] * image_placeholder_length)
final_processed_position = eoi_position
new_input_ids.extend(input_ids[final_processed_position:])
new_position_ids.extend(
list(range(final_processed_position, len(input_ids))))
assert len(new_input_ids) == len(new_position_ids)
prompt = inputs.get("prompt")
if prompt is None:
prompt = tokenizer.decode(new_input_ids)
inputs["prompt_token_ids"] = new_input_ids
inputs["position_ids"] = new_position_ids
return inputs
return token_inputs(
prompt_token_ids=new_input_ids,
prompt=prompt,
multi_modal_data=multi_modal_data,
)
class GLMAttention(nn.Module):
def __init__(
self,
config,
config: ChatGLMConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
):
......@@ -326,7 +329,7 @@ class GLMMLP(nn.Module):
def __init__(
self,
config,
config: ChatGLMConfig,
quant_config: Optional[QuantizationConfig] = None,
):
super().__init__()
......@@ -369,7 +372,7 @@ class GLMBlock(nn.Module):
def __init__(
self,
config,
config: ChatGLMConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
):
......@@ -440,9 +443,10 @@ class GLMTransformer(nn.Module):
def __init__(
self,
config,
config: ChatGLMConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.post_layer_norm = config.post_layer_norm
......@@ -451,10 +455,11 @@ class GLMTransformer(nn.Module):
self.num_layers = config.num_layers
# Transformer layers.
self.layers = nn.ModuleList([
GLMBlock(config, cache_config, quant_config)
for i in range(self.num_layers)
])
self.start_layer, self.end_layer, self.layers = make_layers(
self.num_layers,
lambda prefix: GLMBlock(config, cache_config, quant_config),
prefix=f"{prefix}.layers",
)
if self.post_layer_norm:
layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
......@@ -462,6 +467,10 @@ class GLMTransformer(nn.Module):
self.final_layernorm = layer_norm_func(
config.hidden_size, eps=config.layernorm_epsilon)
self.make_empty_intermediate_tensors = (
make_empty_intermediate_tensors_factory(["hidden_states"],
config.hidden_size))
def forward(
self,
hidden_states: torch.Tensor,
......@@ -469,16 +478,16 @@ class GLMTransformer(nn.Module):
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
) -> torch.Tensor:
for i in range(self.num_layers):
for i in range(self.start_layer, self.end_layer):
layer = self.layers[i]
hidden_states = layer(
hidden_states=hidden_states,
position_ids=position_ids,
kv_cache=kv_caches[i],
kv_cache=kv_caches[i - self.start_layer],
attn_metadata=attn_metadata,
)
# Final layer norm.
if self.post_layer_norm:
if get_pp_group().is_last_rank and self.post_layer_norm:
hidden_states = self.final_layernorm(hidden_states)
return hidden_states
......@@ -488,7 +497,7 @@ class ChatGLMModel(nn.Module):
def __init__(
self,
config,
config: ChatGLMConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
):
......@@ -516,6 +525,9 @@ class ChatGLMModel(nn.Module):
else:
self.vision = None
self.make_empty_intermediate_tensors = (
self.encoder.make_empty_intermediate_tensors)
def _parse_and_validate_image_input(
self, **kwargs: object) -> GLMImagePixelInputs:
......@@ -541,7 +553,7 @@ class ChatGLMModel(nn.Module):
intermediate_tensors: Optional[IntermediateTensors] = None,
**kwargs: object,
) -> torch.Tensor:
if intermediate_tensors is None:
inputs_embeds = self.embedding(input_ids)
image_input = self._parse_and_validate_image_input(**kwargs)
......@@ -559,6 +571,8 @@ class ChatGLMModel(nn.Module):
vision_embeddings=image_embeds,
boi_token_id=boi_token_id,
eoi_token_id=eoi_token_id)
else:
inputs_embeds = intermediate_tensors["hidden_states"]
# Run encoder.
hidden_states = self.encoder(
......@@ -567,6 +581,9 @@ class ChatGLMModel(nn.Module):
kv_caches=kv_caches,
attn_metadata=attn_metadata,
)
if not get_pp_group().is_last_rank:
return IntermediateTensors({"hidden_states": hidden_states})
return hidden_states
......@@ -574,7 +591,8 @@ class ChatGLMModel(nn.Module):
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_glmv_image_tokens)
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_glmv)
@INPUT_REGISTRY.register_input_processor(input_processor_for_glmv)
class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
SupportsMultiModal):
packed_modules_mapping = {
"query_key_value": ["query_key_value"],
"dense_h_to_4h": ["dense_h_to_4h"]
......@@ -631,7 +649,8 @@ class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
intermediate_tensors: Optional[IntermediateTensors] = None,
**kwargs) -> torch.Tensor:
hidden_states = self.transformer(input_ids, positions, kv_caches,
attn_metadata, **kwargs)
attn_metadata, intermediate_tensors,
**kwargs)
return hidden_states
def compute_logits(
......@@ -677,6 +696,8 @@ class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
......
vllm/model_executor/models/clip.py
View file @ 4b4eeb26
......@@ -192,6 +192,7 @@ class CLIPParallelAttention(nn.Module):
self,
config: CLIPVisionConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.config = config
......@@ -211,12 +212,14 @@ class CLIPParallelAttention(nn.Module):
head_size=self.head_dim,
total_num_heads=self.num_heads,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj",
)
self.out_proj = RowParallelLinear(
input_size=self.embed_dim,
output_size=self.embed_dim,
quant_config=quant_config,
prefix=f"{prefix}.out_proj",
)
self.tp_size = get_tensor_model_parallel_world_size()
......@@ -259,20 +262,25 @@ class CLIPParallelAttention(nn.Module):
class CLIPMLP(nn.Module):
def __init__(self,
def __init__(
self,
config: CLIPVisionConfig,
quant_config: Optional[QuantizationConfig] = None):
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
self.activation_fn = get_act_fn(config.hidden_act)
self.fc1 = ColumnParallelLinear(config.hidden_size,
config.intermediate_size,
bias=True,
quant_config=quant_config)
quant_config=quant_config,
prefix=f"{prefix}.fc1")
self.fc2 = RowParallelLinear(config.intermediate_size,
config.hidden_size,
bias=True,
quant_config=quant_config)
quant_config=quant_config,
prefix=f"{prefix}.fc2")
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states, _ = self.fc1(hidden_states)
......@@ -284,21 +292,29 @@ class CLIPMLP(nn.Module):
class CLIPEncoderLayer(nn.Module):
def __init__(self,
def __init__(
self,
config: CLIPVisionConfig,
quant_config: Optional[QuantizationConfig] = None):
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
num_heads = config.num_attention_heads
tp_size = get_tensor_model_parallel_world_size()
if USE_XFORMERS_OPS and num_heads % tp_size == 0:
self.self_attn = CLIPParallelAttention(config,
quant_config=quant_config)
self.self_attn = CLIPParallelAttention(
config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
)
else:
self.self_attn = CLIPSdpaAttention(config)
self.layer_norm1 = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
self.mlp = CLIPMLP(config, quant_config=quant_config)
self.mlp = CLIPMLP(config,
quant_config=quant_config,
prefix=f"{prefix}.mlp")
self.layer_norm2 = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
......@@ -327,11 +343,15 @@ class CLIPEncoder(nn.Module):
config: CLIPConfig
"""
def __init__(self,
def __init__(
self,
config: CLIPVisionConfig,
quant_config: Optional[QuantizationConfig] = None,
num_hidden_layers_override: Optional[int] = None):
num_hidden_layers_override: Optional[int] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
if num_hidden_layers_override is None:
......@@ -339,8 +359,10 @@ class CLIPEncoder(nn.Module):
else:
num_hidden_layers = num_hidden_layers_override
self.layers = nn.ModuleList([
CLIPEncoderLayer(config=config, quant_config=quant_config)
for _ in range(num_hidden_layers)
CLIPEncoderLayer(config=config,
quant_config=quant_config,
prefix=f"{prefix}.layers.{layer_idx}")
for layer_idx in range(num_hidden_layers)
])
def forward(self, inputs_embeds: torch.Tensor):
......@@ -354,11 +376,17 @@ class CLIPEncoder(nn.Module):
class CLIPVisionTransformer(nn.Module):
def __init__(self,
def __init__(
self,
config: CLIPVisionConfig,
quant_config: Optional[QuantizationConfig] = None,
num_hidden_layers_override: Optional[int] = None):
*,
num_hidden_layers_override: Optional[int] = None,
require_post_norm: Optional[bool] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
embed_dim = config.hidden_size
......@@ -370,19 +398,25 @@ class CLIPVisionTransformer(nn.Module):
self.encoder = CLIPEncoder(
config=config,
quant_config=quant_config,
num_hidden_layers_override=num_hidden_layers_override)
num_hidden_layers_override=num_hidden_layers_override,
prefix=f"{prefix}.encoder",
)
num_hidden_layers = config.num_hidden_layers
if len(self.encoder.layers) > config.num_hidden_layers:
raise ValueError(
f"The original encoder only has {config.num_hidden_layers} "
f"The original encoder only has {num_hidden_layers} "
f"layers, but you requested {len(self.encoder.layers)} layers."
)
elif len(self.encoder.layers) == config.num_hidden_layers:
# If possible, skip post_layernorm to conserve memory
if require_post_norm is None:
require_post_norm = len(self.encoder.layers) == num_hidden_layers
if require_post_norm:
self.post_layernorm = nn.LayerNorm(embed_dim,
eps=config.layer_norm_eps)
else:
# post_layernorm is unused when we extract intermediate features
# In this case, we can skip it to conserve memory
self.post_layernorm = None
def forward(
......@@ -405,10 +439,15 @@ class CLIPVisionModel(nn.Module):
config_class = CLIPVisionConfig
main_input_name = "pixel_values"
def __init__(self,
def __init__(
self,
config: CLIPVisionConfig,
quant_config: Optional[QuantizationConfig] = None,
num_hidden_layers_override: Optional[int] = None):
*,
num_hidden_layers_override: Optional[int] = None,
require_post_norm: Optional[bool] = None,
prefix: str = "",
) -> None:
super().__init__()
tp_size = get_tensor_model_parallel_world_size()
......@@ -418,7 +457,10 @@ class CLIPVisionModel(nn.Module):
self.vision_model = CLIPVisionTransformer(
config=config,
quant_config=quant_config,
num_hidden_layers_override=num_hidden_layers_override)
num_hidden_layers_override=num_hidden_layers_override,
require_post_norm=require_post_norm,
prefix=f"{prefix}.vision_model",
)
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
return self.vision_model(pixel_values)
......
vllm/model_executor/models/commandr.py
View file @ 4b4eeb26
......@@ -28,6 +28,7 @@ from torch import nn
from transformers import CohereConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, LoRAConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import SiluAndMul
......@@ -250,6 +251,7 @@ class CohereDecoderLayer(nn.Module):
return hidden_states, residual
@support_torch_compile
class CohereModel(nn.Module):
def __init__(
......
vllm/model_executor/models/exaone.py
View file @ 4b4eeb26
......@@ -29,6 +29,7 @@ import torch
from torch import nn
from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, LoRAConfig
from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
......@@ -311,6 +312,7 @@ class ExaoneDecoderLayer(nn.Module):
return hidden_states, residual
@support_torch_compile
class ExaoneModel(nn.Module):
def __init__(
......
vllm/model_executor/models/florence2.py 0 → 100644
View file @ 4b4eeb26
import math
from typing import Iterable, List, Optional, Tuple
import torch
import torch.nn as nn
from transformers import PretrainedConfig
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.bart import (BartDecoder, BartEncoder,
BartParallelLMHead,
BartScaledWordEmbedding)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from .utils import AutoWeightsLoader
class Florence2LanguageModel(nn.Module):
def __init__(self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None):
super().__init__()
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.shared = BartScaledWordEmbedding(self.vocab_size, config.d_model)
self.encoder = BartEncoder(config,
cache_config=cache_config,
quant_config=quant_config)
self.decoder = BartDecoder(config,
cache_config=cache_config,
quant_config=quant_config)
if self.config.tie_word_embeddings:
self.encoder.embed_tokens.weight = self.shared.weight
self.decoder.embed_tokens.weight = self.shared.weight
def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
encoder_input_ids: torch.Tensor,
encoder_positions: torch.Tensor, kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata) -> torch.Tensor:
r"""
Args:
input_ids
Indices of *decoder* input sequence tokens in the vocabulary.
Padding will be ignored by default should you
provide it.
positions
Positions of *decoder* input sequence tokens.
encoder_input_ids
Indices of *encoder* input sequence tokens in the vocabulary.
encoder_positions:
Positions of *encoder* input sequence tokens.
kv_caches:
Layer-wise list of KV cache tensors
attn_metadata:
vLLM Attention metadata structure
Returns:
Model output torch.Tensor
"""
encoder_hidden_states = None
if encoder_input_ids.numel() > 0:
# Run encoder attention if a non-zero number of encoder tokens
# are provided as input
encoder_hidden_states = self.encoder(input_ids=encoder_input_ids,
positions=encoder_positions,
kv_caches=kv_caches,
attn_metadata=attn_metadata)
# decoder outputs consists of
# (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
decoder_input_ids=input_ids,
decoder_positions=positions,
encoder_hidden_states=encoder_hidden_states,
kv_caches=kv_caches,
attn_metadata=attn_metadata)
return decoder_outputs
class Florence2LanguageForConditionalGeneration(nn.Module):
def __init__(self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None):
super().__init__()
self.config = config
self.model = Florence2LanguageModel(config,
cache_config=cache_config,
quant_config=quant_config)
embed_scale = math.sqrt(
config.d_model) if config.scale_embedding else 1.0
self.vocab_size = config.vocab_size
self.lm_head = BartParallelLMHead(self.vocab_size,
config.d_model,
embed_scale=embed_scale)
self.logits_processor = LogitsProcessor(self.vocab_size,
config.vocab_size)
self.sampler = Sampler()
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
encoder_input_ids: torch.Tensor,
encoder_positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
**kwargs,
) -> torch.Tensor:
r"""
Args:
input_ids
torch.Tensor of *decoder* input token ids.
positions
torch.Tensor of *decoder* position indices.
encoder_input_ids
torch.Tensor of *encoder* input token ids.
encoder_positions
torch.Tensor of *encoder* position indices
kv_caches:
Layer-wise list of KV cache tensors
attn_metadata:
vLLM Attention metadata structure
Returns:
Output torch.Tensor
"""
return self.model(input_ids, positions, encoder_input_ids,
encoder_positions, kv_caches, attn_metadata)
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: torch.Tensor,
sampling_metadata: SamplingMetadata) -> 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_dict = dict(self.named_parameters())
for name, loaded_weight in weights:
for (param_name, weight_name, shard_id) in stacked_params_mapping:
if weight_name not in name:
continue
param = params_dict[name.replace(weight_name, param_name)]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
if "final_logits_bias" in name:
continue
if self.config.tie_word_embeddings and "embed_tokens" in name:
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
class Florence2ForConditionalGeneration(nn.Module):
def __init__(self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None):
super().__init__()
# TODO(Isotr0py): Add vision backbone
self.language_model = Florence2LanguageForConditionalGeneration(
config=config.text_config,
cache_config=cache_config,
quant_config=quant_config)
@property
def sampler(self):
return self.language_model.sampler
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors] = None,
*,
encoder_input_ids: torch.Tensor,
encoder_positions: torch.Tensor,
**kwargs,
) -> torch.Tensor:
r"""
Args:
input_ids
torch.Tensor of *decoder* input token ids.
positions
torch.Tensor of *decoder* position indices.
encoder_input_ids
torch.Tensor of *encoder* input token ids.
encoder_positions
torch.Tensor of *encoder* position indices
kv_caches:
Layer-wise list of KV cache tensors
attn_metadata:
vLLM Attention metadata structure
Returns:
Output torch.Tensor
"""
return self.language_model(input_ids, positions, encoder_input_ids,
encoder_positions, kv_caches, attn_metadata)
def compute_logits(
self,
hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[torch.Tensor]:
return self.language_model.compute_logits(hidden_states,
sampling_metadata)
def sample(
self,
logits: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> SamplerOutput:
return self.language_model.sample(logits, sampling_metadata)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
skip_prefixes = [
'image_projection', "vision_tower", "image_proj_norm",
"image_pos_embed", "visual_temporal_embed"
]
loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
loader.load_weights(weights)
vllm/model_executor/models/gemma.py
View file @ 4b4eeb26
......@@ -22,6 +22,7 @@ from torch import nn
from transformers import GemmaConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, LoRAConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.logger import init_logger
......@@ -239,6 +240,7 @@ class GemmaDecoderLayer(nn.Module):
return hidden_states, residual
@support_torch_compile
class GemmaModel(nn.Module):
def __init__(
......
vllm/model_executor/models/gpt2.py
View file @ 4b4eeb26
......@@ -24,6 +24,7 @@ from torch import nn
from transformers import GPT2Config
from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig
from vllm.distributed.parallel_state import (
get_pp_group, get_tensor_model_parallel_world_size)
......@@ -182,6 +183,7 @@ class GPT2Block(nn.Module):
return hidden_states
@support_torch_compile
class GPT2Model(nn.Module):
def __init__(
......
vllm/model_executor/models/idefics2_vision_model.py
View file @ 4b4eeb26
......@@ -113,7 +113,8 @@ class Idefics2VisionAttention(nn.Module):
self,
config: Idefics2Config,
quant_config: Optional[QuantizationConfig] = None,
):
prefix: str = "",
) -> None:
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
......@@ -130,12 +131,14 @@ class Idefics2VisionAttention(nn.Module):
self.head_dim,
self.num_heads,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj",
)
self.out_proj = RowParallelLinear(
self.embed_dim,
self.embed_dim,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.out_proj",
)
self.tp_size = get_tensor_model_parallel_world_size()
self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
......@@ -178,7 +181,8 @@ class Idefics2VisionMLP(nn.Module):
self,
config: Idefics2Config,
quant_config: Optional[QuantizationConfig] = None,
):
prefix: str = "",
) -> None:
super().__init__()
self.config = config
self.activation_fn = get_act_fn(config.hidden_act)
......@@ -187,12 +191,14 @@ class Idefics2VisionMLP(nn.Module):
config.intermediate_size,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.fc1",
)
self.fc2 = RowParallelLinear(
config.intermediate_size,
config.hidden_size,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.fc2",
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
......@@ -204,13 +210,22 @@ class Idefics2VisionMLP(nn.Module):
class Idefics2EncoderLayer(nn.Module):
def __init__(self, config: Idefics2Config):
def __init__(
self,
config: Idefics2Config,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = Idefics2VisionAttention(config)
self.self_attn = Idefics2VisionAttention(config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn")
self.layer_norm1 = nn.LayerNorm(self.embed_dim,
eps=config.layer_norm_eps)
self.mlp = Idefics2VisionMLP(config)
self.mlp = Idefics2VisionMLP(config,
quant_config=quant_config,
prefix=f"{prefix}.mlp")
self.layer_norm2 = nn.LayerNorm(self.embed_dim,
eps=config.layer_norm_eps)
......@@ -245,12 +260,20 @@ class Idefics2Encoder(nn.Module):
config: Idefics2Config
"""
def __init__(self, config: Idefics2Config):
def __init__(
self,
config: Idefics2Config,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
self.layers = nn.ModuleList([
Idefics2EncoderLayer(config)
for _ in range(config.num_hidden_layers)
Idefics2EncoderLayer(config,
quant_config=quant_config,
prefix=f"{prefix}.layers.{layer_idx}")
for layer_idx in range(config.num_hidden_layers)
])
def forward(
......@@ -275,12 +298,20 @@ class Idefics2Encoder(nn.Module):
class Idefics2VisionTransformer(nn.Module):
def __init__(self, config: Idefics2VisionConfig):
def __init__(
self,
config: Idefics2VisionConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
embed_dim = config.hidden_size
self.config = config
self.embeddings = Idefics2VisionEmbeddings(config)
self.encoder = Idefics2Encoder(config)
self.encoder = Idefics2Encoder(config,
quant_config=quant_config,
prefix=f"{prefix}.encoder")
self.post_layernorm = nn.LayerNorm(embed_dim,
eps=config.layer_norm_eps)
......
vllm/model_executor/models/intern_vit.py
View file @ 4b4eeb26
......@@ -137,6 +137,7 @@ class InternParallelAttention(nn.Module):
quant_config: Optional[QuantizationConfig] = None,
*,
num_dummy_heads: int = 0,
prefix: str = "",
) -> None:
super().__init__()
......@@ -165,6 +166,7 @@ class InternParallelAttention(nn.Module):
num_dummy_heads + self.num_heads,
bias=config.qkv_bias,
quant_config=quant_config,
prefix=f"{prefix}.qkv",
)
self.qk_normalization = config.qk_normalization
......@@ -181,6 +183,7 @@ class InternParallelAttention(nn.Module):
self.dummy_dim,
self.embed_dim,
quant_config=quant_config,
prefix=f"{prefix}.proj",
)
def _apply_qk_norm(self, q: torch.Tensor, k: torch.Tensor):
......@@ -284,20 +287,26 @@ class InternSdpaAttention(nn.Module):
class InternMLP(nn.Module):
def __init__(self,
def __init__(
self,
config: PretrainedConfig,
quant_config: Optional[QuantizationConfig] = None):
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
self.activation_fn = get_act_fn(config.hidden_act)
self.fc1 = ColumnParallelLinear(config.hidden_size,
config.intermediate_size,
bias=True,
quant_config=quant_config)
quant_config=quant_config,
prefix=f"{prefix}.fc1")
self.fc2 = RowParallelLinear(config.intermediate_size,
config.hidden_size,
bias=True,
quant_config=quant_config)
quant_config=quant_config,
prefix=f"{prefix}.fc2")
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states, _ = self.fc1(hidden_states)
......@@ -315,6 +324,7 @@ class InternVisionEncoderLayer(nn.Module):
quant_config: Optional[QuantizationConfig] = None,
*,
num_dummy_heads: int = 0,
prefix: str = "",
) -> None:
super().__init__()
......@@ -324,9 +334,12 @@ class InternVisionEncoderLayer(nn.Module):
self.attn = self._init_attn(config,
quant_config,
num_dummy_heads=num_dummy_heads)
num_dummy_heads=num_dummy_heads,
prefix=f"{prefix}.attn")
self.mlp = InternMLP(config, quant_config=quant_config)
self.mlp = InternMLP(config,
quant_config=quant_config,
prefix=f"{prefix}.mlp")
self.norm1 = NORM2FN[self.norm_type](self.embed_dim,
eps=config.layer_norm_eps)
self.norm2 = NORM2FN[self.norm_type](self.embed_dim,
......@@ -343,6 +356,7 @@ class InternVisionEncoderLayer(nn.Module):
quant_config: Optional[QuantizationConfig],
*,
num_dummy_heads: int,
prefix: str = "",
):
# fallback to sdpa attention if tp unavailable
tp_size = get_tensor_model_parallel_world_size()
......@@ -351,7 +365,8 @@ class InternVisionEncoderLayer(nn.Module):
if USE_XFORMERS_OPS and (num_heads + num_dummy_heads) % tp_size == 0:
return InternParallelAttention(config,
quant_config=quant_config,
num_dummy_heads=num_dummy_heads)
num_dummy_heads=num_dummy_heads,
prefix=prefix)
return InternSdpaAttention(config, num_dummy_heads=num_dummy_heads)
......@@ -377,6 +392,7 @@ class InternVisionEncoder(nn.Module):
*,
num_hidden_layers_override: Optional[int] = None,
num_dummy_heads: int = 0,
prefix: str = "",
):
super().__init__()
......@@ -390,8 +406,9 @@ class InternVisionEncoder(nn.Module):
self.layers = nn.ModuleList([
InternVisionEncoderLayer(config,
quant_config,
num_dummy_heads=num_dummy_heads)
for _ in range(num_hidden_layers)
num_dummy_heads=num_dummy_heads,
prefix=f"{prefix}.layers.{layer_idx}")
for layer_idx in range(num_hidden_layers)
])
def forward(self, inputs_embeds: torch.Tensor):
......@@ -412,7 +429,8 @@ class InternVisionModel(nn.Module):
*,
num_hidden_layers_override: Optional[int] = None,
num_dummy_heads: int = 0,
):
prefix: str = "",
) -> None:
super().__init__()
self.config = config
......@@ -423,6 +441,7 @@ class InternVisionModel(nn.Module):
quant_config=quant_config,
num_hidden_layers_override=num_hidden_layers_override,
num_dummy_heads=num_dummy_heads,
prefix=f"{prefix}.encoder",
)
def get_input_embeddings(self):
......
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