403 lines
15 KiB
Python
403 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
from typing import Any, Callable, Dict, List, Optional
|
|
|
|
import gguf
|
|
import torch
|
|
from gguf import GGMLQuantizationType as WeightType
|
|
from torch.nn.parameter import Parameter, UninitializedParameter
|
|
|
|
from vllm import _custom_ops as ops
|
|
from vllm.logger import init_logger
|
|
from vllm.model_executor.layers.activation import SiluAndMul
|
|
from vllm.model_executor.layers.fused_moe.layer import (FusedMoE,
|
|
FusedMoEMethodBase)
|
|
from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
|
|
from vllm.model_executor.layers.quantization.base_config import (
|
|
QuantizationConfig, QuantizeMethodBase)
|
|
from vllm.model_executor.layers.vocab_parallel_embedding import (
|
|
VocabParallelEmbedding)
|
|
from vllm.model_executor.utils import set_weight_attrs
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
class GGUFConfig(QuantizationConfig):
|
|
"""Config class for GGUF."""
|
|
|
|
def __init__(self, ) -> None:
|
|
super().__init__()
|
|
|
|
def __repr__(self) -> str:
|
|
return ("GGUFConfig()")
|
|
|
|
def get_name(self) -> str:
|
|
return "gguf"
|
|
|
|
def get_supported_act_dtypes(self) -> List[torch.dtype]:
|
|
return [torch.half, torch.bfloat16, torch.float32]
|
|
|
|
@classmethod
|
|
def get_min_capability(cls) -> int:
|
|
return 60
|
|
|
|
@classmethod
|
|
def get_config_filenames(cls) -> List[str]:
|
|
return [] # no extra configs.
|
|
|
|
@classmethod
|
|
def from_config(cls, config: Dict[str, Any]) -> "GGUFConfig":
|
|
return cls()
|
|
|
|
def get_quant_method(self, layer: torch.nn.Module,
|
|
prefix: str) -> Optional["QuantizeMethodBase"]:
|
|
if isinstance(layer, LinearBase):
|
|
return GGUFLinearMethod(self)
|
|
elif isinstance(layer, VocabParallelEmbedding):
|
|
return GGUFEmbeddingMethod(self)
|
|
elif isinstance(layer, FusedMoE):
|
|
return GGUFMoEMethod(self)
|
|
return None
|
|
|
|
|
|
UNQUANTIZED_TYPES = {WeightType.F32, WeightType.F16, WeightType.BF16}
|
|
STANDARD_QUANT_TYPES = {
|
|
WeightType.Q4_0,
|
|
WeightType.Q4_1,
|
|
WeightType.Q5_0,
|
|
WeightType.Q5_1,
|
|
WeightType.Q8_0,
|
|
WeightType.Q8_1,
|
|
}
|
|
KQUANT_TYPES = {
|
|
WeightType.Q2_K,
|
|
WeightType.Q3_K,
|
|
WeightType.Q4_K,
|
|
WeightType.Q5_K,
|
|
WeightType.Q6_K,
|
|
}
|
|
IMATRIX_QUANT_TYPES = {
|
|
WeightType.IQ1_M,
|
|
WeightType.IQ1_S,
|
|
WeightType.IQ2_XXS,
|
|
WeightType.IQ2_XS,
|
|
WeightType.IQ2_S,
|
|
WeightType.IQ3_XXS,
|
|
WeightType.IQ3_S,
|
|
WeightType.IQ4_XS,
|
|
WeightType.IQ4_NL,
|
|
}
|
|
# TODO(Isotr0py): Currently, we don't have MMQ kernel for I-Matrix quantization.
|
|
# Consolidate DEQUANT_TYPES, MMVQ_QUANT_TYPES and MMQ_QUANT_TYPES after we add
|
|
# MMQ kernel for I-Matrix quantization.
|
|
DEQUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES | IMATRIX_QUANT_TYPES
|
|
MMVQ_QUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES | IMATRIX_QUANT_TYPES
|
|
MMQ_QUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES
|
|
|
|
|
|
def _fuse_mul_mat(x: torch.Tensor, qweight: torch.Tensor,
|
|
qweight_type: int) -> torch.Tensor:
|
|
# HACK: when doing chunked prefill we don't generate output tokens
|
|
# so input to logits generator is empty which causes invalid parameter
|
|
if x.shape[0] == 0:
|
|
return torch.empty(x.shape[0],
|
|
qweight.shape[0],
|
|
dtype=x.dtype,
|
|
device=x.device)
|
|
# there is no need to call any kernel for fp16/bf16
|
|
if qweight_type in UNQUANTIZED_TYPES:
|
|
return x @ qweight.T
|
|
# enable MMVQ in contiguous batching with batch_size=1
|
|
if x.shape[0] == 1 and qweight_type in MMVQ_QUANT_TYPES:
|
|
y = ops.ggml_mul_mat_vec_a8(qweight, x, qweight_type, qweight.shape[0])
|
|
# Use MMQ Kernel if it's available (standard + k-quants)
|
|
elif qweight_type in MMQ_QUANT_TYPES:
|
|
y = ops.ggml_mul_mat_a8(qweight, x, qweight_type, qweight.shape[0])
|
|
# If there is no available MMQ kernel, fallback to dequantize
|
|
elif qweight_type in DEQUANT_TYPES:
|
|
block_size, type_size = gguf.GGML_QUANT_SIZES[qweight_type]
|
|
shape = (qweight.shape[0], qweight.shape[1] // type_size * block_size)
|
|
weight = ops.ggml_dequantize(qweight, qweight_type, *shape, x.dtype)
|
|
y = x @ weight.T
|
|
else:
|
|
# Raise an error if the quantization type is not supported.
|
|
# Might be useful if llama.cpp adds a new quantization type.
|
|
# Wrap to GGMLQuantizationType IntEnum to make sure it's a valid type.
|
|
qweight_type = WeightType(qweight_type)
|
|
raise NotImplementedError(
|
|
f"Unsupported GGUF quantization type: {qweight_type}")
|
|
return y
|
|
|
|
|
|
def _fused_moe_gguf(
|
|
x: torch.Tensor,
|
|
w1: torch.Tensor,
|
|
w2: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
qweight_type: int,
|
|
qweight_type2: int,
|
|
act,
|
|
) -> torch.Tensor:
|
|
# lazy import to avoid triggering triton import in CPU backend
|
|
from vllm.model_executor.layers.fused_moe.fused_moe import (
|
|
moe_align_block_size)
|
|
|
|
out_hidden_states = torch.empty_like(x)
|
|
if qweight_type2 in MMQ_QUANT_TYPES and qweight_type in MMQ_QUANT_TYPES:
|
|
num_tokens, _ = x.shape
|
|
E, N, _ = w1.shape
|
|
top_k = topk_ids.shape[1]
|
|
BLOCK_SIZE = ops.ggml_moe_get_block_size(qweight_type)
|
|
|
|
sorted_token_ids, expert_ids, num_tokens_post_padded = \
|
|
moe_align_block_size(topk_ids, BLOCK_SIZE, E)
|
|
out = ops.ggml_moe_a8(x, w1, sorted_token_ids, expert_ids,
|
|
num_tokens_post_padded, qweight_type, N, top_k,
|
|
num_tokens)
|
|
out = act(out)
|
|
out = ops.ggml_moe_a8(out, w2, sorted_token_ids, expert_ids,
|
|
num_tokens_post_padded, qweight_type2,
|
|
w2.shape[1], 1, num_tokens * top_k)
|
|
out = out.reshape(num_tokens, top_k, w2.shape[1]).mul_(
|
|
topk_weights.view(num_tokens, top_k, 1))
|
|
ops.moe_sum(out, out_hidden_states)
|
|
else:
|
|
logger.warning_once("There is no support for fast MoE kernel "
|
|
"for current quantization method. "
|
|
"Falling back to slow implementation. ")
|
|
for tok, (w, idx) in enumerate(zip(topk_weights, topk_ids)):
|
|
inp = x[tok].reshape((1, ) + x.shape[1:])
|
|
current_hidden_state = None
|
|
for ww, ii in zip(w, idx):
|
|
expert_up = w1[ii]
|
|
|
|
out = _fuse_mul_mat(inp, expert_up, qweight_type)
|
|
out = act(out)
|
|
|
|
expert_down = w2[ii]
|
|
current_state = _fuse_mul_mat(out, expert_down,
|
|
qweight_type2).mul_(ww)
|
|
if current_hidden_state is None:
|
|
current_hidden_state = current_state
|
|
else:
|
|
current_hidden_state.add_(current_state)
|
|
out_hidden_states[tok] = current_hidden_state
|
|
return out_hidden_states
|
|
|
|
|
|
class GGUFLinearMethod(LinearMethodBase):
|
|
"""Linear method for GGUF.
|
|
|
|
Args:
|
|
quant_config: The GGUF quantization config.
|
|
"""
|
|
|
|
def __init__(self, quant_config: GGUFConfig):
|
|
self.quant_config = quant_config
|
|
|
|
def create_weights(self, layer: torch.nn.Module,
|
|
input_size_per_partition: int,
|
|
output_partition_sizes: List[int], input_size: int,
|
|
output_size: int, params_dtype: torch.dtype,
|
|
**extra_weight_attrs):
|
|
self.params_dtype = params_dtype
|
|
output_size_per_partition = sum(output_partition_sizes)
|
|
|
|
tensor_shape = (output_size_per_partition, input_size_per_partition)
|
|
qweight = GGUFUninitializedParameter(requires_grad=False)
|
|
set_weight_attrs(
|
|
qweight, {
|
|
"input_dim": 1,
|
|
"output_dim": 0,
|
|
"tensor_shape": tensor_shape,
|
|
"is_gguf_weight": True,
|
|
"data_container": [],
|
|
"shard_id": [],
|
|
"shard_id_map": {},
|
|
})
|
|
set_weight_attrs(qweight, extra_weight_attrs)
|
|
layer.register_parameter("qweight", qweight)
|
|
|
|
qweight_type = Parameter(torch.empty(len(output_partition_sizes),
|
|
dtype=torch.uint8),
|
|
requires_grad=False)
|
|
set_weight_attrs(
|
|
qweight_type, {
|
|
"is_gguf_weight_type": True,
|
|
"weight_type": 0,
|
|
"shard_weight_type": {},
|
|
"ignore_warning": True
|
|
})
|
|
set_weight_attrs(qweight_type, extra_weight_attrs)
|
|
layer.register_parameter("qweight_type", qweight_type)
|
|
|
|
def apply(self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
|
|
shard_id = getattr(layer.qweight, "shard_id", None)
|
|
|
|
if shard_id:
|
|
# dequantize shard weights respectively
|
|
shard_id = ["q", "k", "v"] if "q" in shard_id else shard_id
|
|
qweight = layer.qweight.unbind(0)
|
|
result = []
|
|
for idx in shard_id:
|
|
q_idx = layer.qweight.shard_id_map[idx]
|
|
qweight_type = layer.qweight_type.shard_weight_type[idx]
|
|
result.append(_fuse_mul_mat(x, qweight[q_idx], qweight_type))
|
|
out = torch.cat(result, axis=1)
|
|
else:
|
|
qweight = layer.qweight
|
|
qweight_type = layer.qweight_type.weight_type
|
|
out = _fuse_mul_mat(x, qweight, qweight_type)
|
|
if bias is not None:
|
|
out.add_(bias)
|
|
return out
|
|
|
|
|
|
class GGUFMoEMethod(FusedMoEMethodBase):
|
|
"""MoE method for GGUF.
|
|
|
|
Args:
|
|
quant_config: The GGUF quantization config.
|
|
"""
|
|
|
|
def __init__(self, quant_config: GGUFConfig):
|
|
self.quant_config = quant_config
|
|
|
|
def create_weights(self, layer: torch.nn.Module, num_experts: int,
|
|
hidden_size: int, intermediate_size_per_partition: int,
|
|
params_dtype: torch.dtype, **extra_weight_attrs):
|
|
|
|
tensor_shape = (num_experts, 2 * intermediate_size_per_partition,
|
|
hidden_size)
|
|
#gate up proj
|
|
w13_qweight = GGUFUninitializedParameter(requires_grad=False)
|
|
set_weight_attrs(
|
|
w13_qweight, {
|
|
"input_dim": 1,
|
|
"output_dim": 0,
|
|
"tensor_shape": tensor_shape,
|
|
"is_gguf_weight": True,
|
|
"data_container": [],
|
|
})
|
|
set_weight_attrs(w13_qweight, extra_weight_attrs)
|
|
layer.register_parameter("w13_qweight", w13_qweight)
|
|
|
|
w13_qweight_type = Parameter(torch.empty(1, dtype=torch.uint8),
|
|
requires_grad=False)
|
|
set_weight_attrs(w13_qweight_type, {
|
|
"is_gguf_weight_type": True,
|
|
"weight_type": 0,
|
|
"ignore_warning": True
|
|
})
|
|
set_weight_attrs(w13_qweight_type, extra_weight_attrs)
|
|
layer.register_parameter("w13_qweight_type", w13_qweight_type)
|
|
|
|
tensor_shape = (num_experts, intermediate_size_per_partition,
|
|
hidden_size)
|
|
#gate down proj
|
|
w2_qweight = GGUFUninitializedParameter(requires_grad=False)
|
|
set_weight_attrs(
|
|
w2_qweight, {
|
|
"input_dim": 1,
|
|
"output_dim": 0,
|
|
"tensor_shape": tensor_shape,
|
|
"is_gguf_weight": True,
|
|
"data_container": [],
|
|
})
|
|
set_weight_attrs(w2_qweight, extra_weight_attrs)
|
|
layer.register_parameter("w2_qweight", w2_qweight)
|
|
|
|
w2_qweight_type = Parameter(torch.empty(1, dtype=torch.uint8),
|
|
requires_grad=False)
|
|
set_weight_attrs(w2_qweight_type, {
|
|
"is_gguf_weight_type": True,
|
|
"weight_type": 0,
|
|
"ignore_warning": True
|
|
})
|
|
|
|
set_weight_attrs(w2_qweight_type, extra_weight_attrs)
|
|
layer.register_parameter("w2_qweight_type", w2_qweight_type)
|
|
self.act = SiluAndMul()
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
renormalize: bool,
|
|
use_grouped_topk: bool = False,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
global_num_experts: int = -1,
|
|
expert_map: Optional[torch.Tensor] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
activation: str = "silu",
|
|
):
|
|
assert activation == "silu", "Only SiLU activation is supported."
|
|
topk_weights, topk_ids = FusedMoE.select_experts(
|
|
hidden_states=x,
|
|
router_logits=router_logits,
|
|
use_grouped_topk=use_grouped_topk,
|
|
top_k=top_k,
|
|
renormalize=renormalize,
|
|
topk_group=topk_group,
|
|
num_expert_group=num_expert_group,
|
|
custom_routing_function=custom_routing_function,
|
|
scoring_func=scoring_func,
|
|
e_score_correction_bias=e_score_correction_bias)
|
|
return _fused_moe_gguf(x, layer.w13_qweight, layer.w2_qweight,
|
|
topk_weights, topk_ids,
|
|
layer.w13_qweight_type.weight_type,
|
|
layer.w2_qweight_type.weight_type, self.act)
|
|
|
|
|
|
class GGUFEmbeddingMethod(GGUFLinearMethod):
|
|
"""Embedding method for GGUF.
|
|
|
|
Args:
|
|
quant_config: The GGUF quantization config.
|
|
"""
|
|
|
|
def embedding(self, layer: torch.nn.Module,
|
|
x: torch.Tensor) -> torch.Tensor:
|
|
qweight = layer.qweight
|
|
qweight_type = layer.qweight_type.weight_type
|
|
|
|
block_size, type_size = gguf.GGML_QUANT_SIZES[qweight_type]
|
|
hidden_size = qweight.shape[1] // type_size * block_size
|
|
if qweight_type < 2:
|
|
return torch.embedding(qweight, x)
|
|
x_flat = x.flatten()
|
|
quant = torch.index_select(qweight, dim=0, index=x_flat)
|
|
dequant = ops.ggml_dequantize(quant, qweight_type, hidden_size,
|
|
x_flat.shape[0], self.params_dtype)
|
|
return dequant.view(*x.shape, hidden_size)
|
|
|
|
|
|
class GGUFUninitializedParameter(UninitializedParameter):
|
|
cls_to_become = Parameter
|
|
data_container: List[torch.Tensor]
|
|
|
|
def materialize_nested(self) -> Parameter:
|
|
dtype = {data.dtype for data in self.data_container}
|
|
assert len(dtype) == 1, ValueError(
|
|
f"Data container has mixed dtypes: {dtype}")
|
|
dtype = next(iter(dtype))
|
|
nested_data = torch.nested.nested_tensor(self.data_container,
|
|
device=self.device,
|
|
dtype=dtype)
|
|
self.data_container.clear()
|
|
param = torch.Tensor._make_subclass(self.cls_to_become,
|
|
nested_data,
|
|
require_grad=False)
|
|
for k, v in self.__dict__.items():
|
|
setattr(param, k, v)
|
|
return param
|