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Commit 4eabe123 authored by zhuwenwen's avatar zhuwenwen
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Merge remote-tracking branch 'mirror/releases/v0.9.0' into v0.9.0-ori

parents 45840cd2 58738772
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Showing with 352 additions and 214 deletions
vllm/model_executor/layers/quantization/auto_round.py
View file @ 4eabe123
......@@ -8,6 +8,7 @@ import torch
from vllm.logger import init_logger
from vllm.model_executor.layers.linear import (LinearBase,
UnquantizedLinearMethod)
from vllm.model_executor.layers.quantization import QuantizationMethods
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
......@@ -74,7 +75,7 @@ class AutoRoundConfig(QuantizationConfig):
f"group_size={self.group_size}, sym={self.sym})")
@classmethod
def get_name(cls): ## use str will trigger preci issue
def get_name(cls) -> QuantizationMethods:
return "auto-round"
@classmethod
......@@ -142,18 +143,18 @@ class AutoRoundConfig(QuantizationConfig):
prefix, layer.__class__.__name__, weight_bits, group_size,
sym)
if backend == "auto" or "marlin" in backend:
AWQ_TYPE_MAP = {
4: scalar_types.uint4,
8: scalar_types.uint8,
}
use_marlin = (weight_bits
in AWQ_TYPE_MAP) and check_marlin_supported(
AWQ_TYPE_MAP[weight_bits], group_size, not sym)
if isinstance(layer, FusedMoE):
use_marlin = check_moe_marlin_supports_layer(layer, group_size)
else:
use_marlin = use_marlin and check_moe_marlin_supports_layer(
layer, group_size)
AWQ_TYPE_MAP = {
4: scalar_types.uint4,
8: scalar_types.uint8,
}
use_marlin = ((weight_bits, sym) in AWQ_TYPE_MAP
and check_marlin_supported(
AWQ_TYPE_MAP[(weight_bits)], group_size,
not sym))
else:
use_marlin = False
if use_marlin:
......@@ -180,10 +181,11 @@ class AutoRoundConfig(QuantizationConfig):
from vllm.model_executor.layers.quantization.moe_wna16 import (
MoeWNA16Config)
config = {
"linear_quant_method": "awq",
"weight_bits": weight_bits,
"quant_method": "awq",
"bits": weight_bits,
"group_size": group_size,
"zero_point": not sym,
"lm_head": False,
}
return MoeWNA16Config.from_config(config).get_quant_method(
layer, prefix)
......@@ -213,18 +215,18 @@ class AutoRoundConfig(QuantizationConfig):
prefix, layer.__class__.__name__, weight_bits, group_size,
sym)
if backend == "auto" or "marlin" in backend:
GPTQ_TYPE_MAP = {
(4, True): scalar_types.uint4b8,
(8, True): scalar_types.uint8b128,
}
use_marlin = ((weight_bits, sym) in GPTQ_TYPE_MAP
and check_marlin_supported(
GPTQ_TYPE_MAP[(weight_bits, sym)],
group_size,
has_zp=not sym))
if isinstance(layer, FusedMoE):
use_marlin = check_moe_marlin_supports_layer(layer, group_size)
else:
GPTQ_TYPE_MAP = {
(4, True): scalar_types.uint4b8,
(8, True): scalar_types.uint8b128,
}
use_marlin = ((weight_bits, sym) in GPTQ_TYPE_MAP
and check_marlin_supported(
GPTQ_TYPE_MAP[(weight_bits, sym)],
group_size,
has_zp=not sym))
use_marlin = use_marlin and check_moe_marlin_supports_layer(
layer, group_size)
else:
use_marlin = False
if use_marlin:
......@@ -251,11 +253,11 @@ class AutoRoundConfig(QuantizationConfig):
from vllm.model_executor.layers.quantization.moe_wna16 import (
MoeWNA16Config)
config = {
"linear_quant_method": "gptq",
"weight_bits": weight_bits,
"quant_method": "gptq",
"bits": weight_bits,
"group_size": group_size,
"sym": sym,
"lm_head_quantized": False,
"lm_head": False,
}
return MoeWNA16Config.from_config(config).get_quant_method(
layer, prefix)
......
vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py
View file @ 4eabe123
......@@ -286,9 +286,8 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
rocm_aiter_fused_experts, shuffle_weights)
# reshaping weights is required for aiter moe kernel.
shuffled_w13, shuffled_w2 = shuffle_weights(layer.w13_weight.data,
layer.w2_weight.data,
layout=(16, 16))
shuffled_w13, shuffled_w2 = shuffle_weights(
layer.w13_weight.data, layer.w2_weight.data)
layer.w13_weight = torch.nn.Parameter(shuffled_w13,
requires_grad=False)
......
vllm/model_executor/layers/quantization/compressed_tensors/utils.py
View file @ 4eabe123
# SPDX-License-Identifier: Apache-2.0
import re
from collections.abc import Iterable, Mapping
from types import MappingProxyType
from typing import Optional
import regex as re
from compressed_tensors import CompressionFormat
from torch.nn import Module
......
vllm/model_executor/layers/quantization/fp8.py
View file @ 4eabe123
......@@ -10,7 +10,6 @@ from torch.nn import Module
from torch.nn.parameter import Parameter
import vllm.envs as envs
import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from vllm import _custom_ops as ops
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.logger import init_logger
......@@ -63,10 +62,9 @@ class Fp8Config(QuantizationConfig):
weight_block_size: Optional[list[int]] = None,
) -> None:
super().__init__()
self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
if is_checkpoint_fp8_serialized:
logger.warning("Detected fp8 checkpoint. Please note that the "
"format is experimental and subject to change.")
if activation_scheme not in ACTIVATION_SCHEMES:
raise ValueError(
f"Unsupported activation scheme {activation_scheme}")
......@@ -461,7 +459,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
logger.warning_once(
"DeepGemm not supported on the current platform.")
self.fused_experts = functools.partial(
self.fused_experts = functools.partial( # type: ignore
fused_experts,
block_shape=self.quant_config.weight_block_size,
allow_deep_gemm=self.allow_deep_gemm)
......@@ -597,7 +595,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
def process_weights_after_loading(self, layer: Module) -> None:
# Lazy import to avoid importing triton too early.
from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
expand_weights, is_rocm_aiter_moe_enabled, shuffle_weights)
is_rocm_aiter_moe_enabled, shuffle_weights)
self.rocm_aiter_moe_enabled = is_rocm_aiter_moe_enabled()
......@@ -629,9 +627,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
if self.rocm_aiter_moe_enabled:
# reshaping weights is required for aiter moe kernel.
shuffled_w13, shuffled_w2 = shuffle_weights(
layer.w13_weight.data,
layer.w2_weight.data,
layout=(16, 16))
layer.w13_weight.data, layer.w2_weight.data)
layer.w13_weight = torch.nn.Parameter(shuffled_w13,
requires_grad=False)
......@@ -677,20 +673,8 @@ class Fp8MoEMethod(FusedMoEMethodBase):
requires_grad=False)
if self.rocm_aiter_moe_enabled:
# reshaping weights is required for aiter moe kernel.
w13_scales, w2_scales = expand_weights(
layer.w13_weight_scale.data,
layer.w2_weight_scale.data,
expansion_dims=[
layer.w13_weight.shape[1], layer.w2_weight.shape[1]
])
layer.w13_weight_scale = torch.nn.Parameter(
w13_scales.contiguous(), requires_grad=False)
layer.w2_weight_scale = torch.nn.Parameter(
w2_scales.contiguous(), requires_grad=False)
shuffled_w13, shuffled_w2 = shuffle_weights(layer.w13_weight,
layer.w2_weight,
layout=(16, 16))
shuffled_w13, shuffled_w2 = shuffle_weights(
layer.w13_weight, layer.w2_weight)
layer.w13_weight = torch.nn.Parameter(shuffled_w13,
requires_grad=False)
......@@ -762,20 +746,8 @@ class Fp8MoEMethod(FusedMoEMethodBase):
start += shard_size
if self.rocm_aiter_moe_enabled:
# reshaping weights is required for aiter moe kernel.
expansion_dims = [
layer.w13_weight.shape[1], layer.w2_weight.shape[1]
]
max_w13_scales, w2_scales = expand_weights(
max_w13_scales,
layer.w2_weight_scale.data,
expansion_dims=expansion_dims)
layer.w2_weight_scale = torch.nn.Parameter(
w2_scales.contiguous(), requires_grad=False)
shuffled_w13, shuffled_w2 = shuffle_weights(layer.w13_weight,
layer.w2_weight,
layout=(32, 32))
shuffled_w13, shuffled_w2 = shuffle_weights(
layer.w13_weight, layer.w2_weight)
layer.w13_weight = torch.nn.Parameter(shuffled_w13,
requires_grad=False)
......@@ -791,17 +763,12 @@ class Fp8MoEMethod(FusedMoEMethodBase):
del layer.w13_input_scale
del layer.w2_input_scale
def set_prepare_finalize(
self,
dp_size: int,
world_size: int,
prepare_finalize: mk.FusedMoEPrepareAndFinalize,
) -> bool:
def select_gemm_impl(self, prepare_finalize):
from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
TritonOrDeepGemmExperts)
if self.use_marlin or self.rocm_aiter_moe_enabled:
return False
assert not self.use_marlin and not self.rocm_aiter_moe_enabled, (
"Marlin and ROCm AITER are not supported with all2all yet.")
experts = TritonOrDeepGemmExperts(
use_fp8_w8a8=True,
......@@ -809,12 +776,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
allow_deep_gemm=self.allow_deep_gemm,
)
self.fused_experts = mk.FusedMoEModularKernel(
prepare_finalize,
experts,
)
return True
return experts
def apply(
self,
......
vllm/model_executor/layers/quantization/gguf.py
View file @ 4eabe123
......@@ -9,7 +9,6 @@ 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
......@@ -19,6 +18,7 @@ from vllm.model_executor.layers.quantization.base_config import (
from vllm.model_executor.layers.vocab_parallel_embedding import (
VocabParallelEmbedding)
from vllm.model_executor.utils import set_weight_attrs
from vllm.utils import direct_register_custom_op
logger = init_logger(__name__)
......@@ -96,8 +96,8 @@ 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:
def _fused_mul_mat_gguf(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:
......@@ -130,6 +130,30 @@ def _fuse_mul_mat(x: torch.Tensor, qweight: torch.Tensor,
return y
def _fused_mul_mat_gguf_fake(
x: torch.Tensor,
qweight: torch.Tensor,
qweight_type: int,
) -> torch.Tensor:
return torch.empty(x.shape[0],
qweight.shape[0],
dtype=x.dtype,
device=x.device)
try:
direct_register_custom_op(
op_name="_fused_mul_mat_gguf",
op_func=_fused_mul_mat_gguf,
mutates_args=[],
fake_impl=_fused_mul_mat_gguf_fake,
)
fused_mul_mat_gguf = torch.ops.vllm._fused_mul_mat_gguf
except AttributeError as error:
raise error
def _fused_moe_gguf(
x: torch.Tensor,
w1: torch.Tensor,
......@@ -138,8 +162,21 @@ def _fused_moe_gguf(
topk_ids: torch.Tensor,
qweight_type: int,
qweight_type2: int,
act,
activation: str,
) -> torch.Tensor:
def act(x: torch.Tensor):
d = x.shape[-1] // 2
output_shape = (x.shape[:-1] + (d, ))
out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
if activation == "silu":
torch.ops._C.silu_and_mul(out, x)
elif activation == "gelu":
torch.ops._C.gelu_and_mul(out, x)
else:
raise ValueError(f"Unsupported activation: {activation}")
return out
# lazy import to avoid triggering triton import in CPU backend
from vllm.model_executor.layers.fused_moe.fused_moe import (
moe_align_block_size)
......@@ -189,12 +226,12 @@ def _fused_moe_gguf(
for ww, ii in zip(w, idx):
expert_up = w1[ii]
out = _fuse_mul_mat(inp, expert_up, qweight_type)
out = fused_mul_mat_gguf(inp, expert_up, qweight_type)
out = act(out)
expert_down = w2[ii]
current_state = _fuse_mul_mat(out, expert_down,
qweight_type2).mul_(ww)
current_state = fused_mul_mat_gguf(out, expert_down,
qweight_type2).mul_(ww)
if current_hidden_state is None:
current_hidden_state = current_state
else:
......@@ -203,6 +240,78 @@ def _fused_moe_gguf(
return out_hidden_states
def _fused_moe_gguf_fake(
x: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
qweight_type: int,
qweight_type2: int,
activation: str,
) -> torch.Tensor:
return torch.empty_like(x)
try:
direct_register_custom_op(
op_name="_fused_moe_gguf",
op_func=_fused_moe_gguf,
mutates_args=[],
fake_impl=_fused_moe_gguf_fake,
)
fused_moe_gguf = torch.ops.vllm._fused_moe_gguf
except AttributeError as error:
raise error
def _apply_gguf_embedding(
x: torch.Tensor,
qweight: torch.Tensor,
qweight_type: int,
hidden_size: int,
dtype: Optional[torch.dtype] = None,
) -> torch.Tensor:
if qweight_type in UNQUANTIZED_TYPES:
return torch.embedding(qweight, x)
elif qweight_type in DEQUANT_TYPES:
block_size, type_size = gguf.GGML_QUANT_SIZES[qweight_type]
x_flat = x.flatten()
assert (hidden_size == qweight.shape[1] // type_size * block_size)
quant = torch.index_select(qweight, dim=0, index=x_flat)
dequant = ops.ggml_dequantize(quant, qweight_type, hidden_size,
x_flat.shape[0], dtype)
return dequant.view(*x.shape, hidden_size)
else:
qweight_type = WeightType(qweight_type)
raise NotImplementedError(
f"Unsupported GGUF quantization type: {qweight_type}")
def _apply_gguf_embedding_fake(
x: torch.Tensor,
qweight: torch.Tensor,
qweight_type: int,
hidden_size: int,
dtype: Optional[torch.dtype] = None,
) -> torch.Tensor:
return torch.empty(x.shape[0], hidden_size, dtype=dtype, device=x.device)
try:
direct_register_custom_op(
op_name="_apply_gguf_embedding",
op_func=_apply_gguf_embedding,
mutates_args=[],
fake_impl=_apply_gguf_embedding_fake,
)
apply_gguf_embedding = torch.ops.vllm._apply_gguf_embedding
except AttributeError as error:
raise error
class GGUFLinearMethod(LinearMethodBase):
"""Linear method for GGUF.
......@@ -249,26 +358,76 @@ class GGUFLinearMethod(LinearMethodBase):
set_weight_attrs(qweight_type, extra_weight_attrs)
layer.register_parameter("qweight_type", qweight_type)
def process_weights_after_loading(self, layer: torch.nn.Module):
qweight_type = layer.qweight_type.weight_type
if not (qweight_type in UNQUANTIZED_TYPES
or qweight_type in DEQUANT_TYPES):
qweight_type = WeightType(qweight_type)
raise ValueError(
f"Unsupported GGUF quantization type {qweight_type} in "
f"layer {layer}.")
# For MergedColumnParallelLinear and QKVParallelLinear, we need to
# materialize the padded weight parameter for CUDA Graph compatibility.
self._create_padded_weight_param(layer)
def _create_padded_weight_param(self, layer: torch.nn.Module):
"""Create padded weight parameter for GGUF MergedLinear layer."""
qweight = layer.qweight
shard_id_map = qweight.shard_id_map
shard_id = qweight.shard_id
if len(data_container := qweight.data_container) > 1:
dtype = {data.dtype for data in data_container}
assert len(dtype) == 1, ValueError(
f"Data container has mixed dtypes: {dtype}")
dtype = next(iter(dtype))
# concat dim0 and pad dim1
padded_side = max(x.size(1) for x in data_container)
concat_side = sum(x.size(0) for x in data_container)
# Pad the quantized weights to dense tensor, and create a map
# with the location of each shard in the padded tensor.
padded_data = torch.zeros((concat_side, padded_side),
dtype=dtype,
device=qweight.device)
# (dim0_start, dim0_end, dim1_size)
shard_offset_map = dict[str, tuple[int, int, int]]()
for idx in shard_id:
id_in_container = shard_id_map[idx]
start = sum(
x.size(0) for x in data_container[:id_in_container])
end = start + data_container[id_in_container].size(0)
size = data_container[id_in_container].size(1)
padded_data[start:end, :size] = data_container[id_in_container]
shard_offset_map[idx] = (start, end, size)
qweight.data_container.clear()
padded_param = Parameter(padded_data, requires_grad=False)
set_weight_attrs(padded_param, vars(qweight))
set_weight_attrs(padded_param,
{"shard_offset_map": shard_offset_map})
layer.register_parameter("qweight", padded_param)
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)
shard_id = layer.qweight.shard_id
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)
qweight = layer.qweight
result = []
for idx in shard_id:
q_idx = layer.qweight.shard_id_map[idx]
start, end, offset = layer.qweight.shard_offset_map[idx]
qweight_type = layer.qweight_type.shard_weight_type[idx]
result.append(_fuse_mul_mat(x, qweight[q_idx], qweight_type))
result.append(
fused_mul_mat_gguf(
x, qweight[start:end, :offset].contiguous(),
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)
out = fused_mul_mat_gguf(x, qweight, qweight_type)
if bias is not None:
out.add_(bias)
return out
......@@ -338,7 +497,6 @@ class GGUFMoEMethod(FusedMoEMethodBase):
set_weight_attrs(w2_qweight_type, extra_weight_attrs)
layer.register_parameter("w2_qweight_type", w2_qweight_type)
self.act = SiluAndMul()
def apply(
self,
......@@ -375,10 +533,10 @@ class GGUFMoEMethod(FusedMoEMethodBase):
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)
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, activation)
class GGUFEmbeddingMethod(GGUFLinearMethod):
......@@ -392,34 +550,15 @@ class GGUFEmbeddingMethod(GGUFLinearMethod):
x: torch.Tensor) -> torch.Tensor:
qweight = layer.qweight
qweight_type = layer.qweight_type.weight_type
hidden_size = qweight.tensor_shape[1]
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)
return apply_gguf_embedding(x,
qweight,
qweight_type,
hidden_size,
dtype=self.params_dtype)
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
vllm/model_executor/layers/quantization/ipex_quant.py
View file @ 4eabe123
......@@ -14,7 +14,7 @@ from vllm.model_executor.layers.quantization.base_config import (
from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod
from vllm.platforms import current_platform
MIN_IPEX_VERSION = "2.5.0"
MIN_IPEX_VERSION = "2.7.0"
class IPEXConfig(QuantizationConfig):
......@@ -181,8 +181,6 @@ class IPEXGPTQLinearMethod(GPTQLinearMethod):
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
reshaped_x = x.reshape(-1, x.shape[-1])
out = layer.ipex_qlinear(reshaped_x)
if bias is not None:
out.add_(bias)
return out.reshape(x.shape[:-1] + (layer.ipex_output_size, ))
......
vllm/model_executor/layers/quantization/modelopt.py
View file @ 4eabe123
......@@ -192,7 +192,7 @@ class ModelOptNvFp4Config(QuantizationConfig):
@classmethod
def get_name(cls) -> QuantizationMethods:
return "nvfp4"
return "modelopt_fp4"
@classmethod
def get_supported_act_dtypes(cls) -> list[torch.dtype]:
......@@ -228,7 +228,7 @@ class ModelOptNvFp4Config(QuantizationConfig):
exclude_modules, group_size)
def is_layer_excluded(self, prefix: str, exclude_modules: list):
import re
import regex as re
for pattern in exclude_modules:
regex_str = pattern.replace('.', r'\.').replace('*', r'.*')
if re.fullmatch(regex_str, prefix):
......
vllm/model_executor/layers/quantization/quark/utils.py
View file @ 4eabe123
# SPDX-License-Identifier: Apache-2.0
import re
from collections.abc import Iterable, Mapping
from types import MappingProxyType
from typing import Any, Optional
import regex as re
def deep_compare(dict1: Any, dict2: Any) -> bool:
if type(dict1) is not type(dict2):
......
vllm/model_executor/layers/quantization/utils/gptq_utils.py
View file @ 4eabe123
# SPDX-License-Identifier: Apache-2.0
import re
from copy import deepcopy
from typing import Optional, Union
import regex as re
import torch
from vllm.config import QuantizationConfig
......
vllm/model_executor/layers/rejection_sampler.py
View file @ 4eabe123
......@@ -262,16 +262,16 @@ class RejectionSampler(SpecDecodeStochasticBaseSampler):
True, then a token can be accepted, else it should be
rejected.
Given {math}`q(\hat{x}_{n+1}|x_1, \dots, x_n)`, the probability of
{math}`\hat{x}_{n+1}` given context {math}`x_1, \dots, x_n` according
to the target model, and {math}`p(\hat{x}_{n+1}|x_1, \dots, x_n)`, the
Given $q(\hat{x}_{n+1}|x_1, \dots, x_n)$, the probability of
$\hat{x}_{n+1}$ given context $x_1, \dots, x_n$ according
to the target model, and $p(\hat{x}_{n+1}|x_1, \dots, x_n)$, the
same conditional probability according to the draft model, the token
is accepted with probability:
:::{math}
$$
\min\left(1, \frac{q(\hat{x}_{n+1}|x_1, \dots, x_n)}
{p(\hat{x}_{n+1}|x_1, \dots, x_n)}\right)
:::
$$
This implementation does not apply causality. When using the output,
if a token is rejected, subsequent tokens should not be used.
......@@ -314,30 +314,31 @@ class RejectionSampler(SpecDecodeStochasticBaseSampler):
target model is recovered (within hardware numerics).
The probability distribution used in this rejection case is constructed
as follows. Given {math}`q(x|x_1, \dots, x_n)`, the probability of
{math}`x` given context {math}`x_1, \dots, x_n` according to the target
model and {math}`p(x|x_1, \dots, x_n)`, the same conditional probability
as follows. Given $q(x|x_1, \dots, x_n)$, the probability of
$x$ given context $x_1, \dots, x_n$ according to the target
model and $p(x|x_1, \dots, x_n)$, the same conditional probability
according to the draft model:
:::{math}
$$
x_{n+1} \sim (q(x|x_1, \dots, x_n) - p(x|x_1, \dots, x_n))_+
:::
$$
where {math}`(f(x))_+` is defined as:
where $(f(x))_+$ is defined as:
:::{math}
$$
(f(x))_+ = \frac{\max(0, f(x))}{\sum_x \max(0, f(x))}
:::
$$
See https://github.com/vllm-project/vllm/pull/2336 for a visualization
of the draft, target, and recovered probability distributions.
Returns a tensor of shape [batch_size, k, vocab_size].
Note: This batches operations on GPU and thus constructs the recovered
distribution for all tokens, even if they are accepted. This causes
division-by-zero errors, so we use self._smallest_positive_value to
avoid that. This introduces some drift to the distribution.
Note:
This batches operations on GPU and thus constructs the recovered
distribution for all tokens, even if they are accepted. This causes
division-by-zero errors, so we use self._smallest_positive_value to
avoid that. This introduces some drift to the distribution.
"""
_, k, _ = draft_probs.shape
......
vllm/model_executor/layers/sampler.py
View file @ 4eabe123
......@@ -228,17 +228,19 @@ class Sampler(nn.Module):
) -> Optional[SamplerOutput]:
"""
Single-step scheduling:
* Perform GPU-side sampling computation & compute
GPU-side logprobs tensor
* Pythonize sampling result & logprobs tensor
* Perform GPU-side sampling computation & compute
GPU-side logprobs tensor
* Pythonize sampling result & logprobs tensor
Multi-step scheduling:
* Perform GPU-side sampling computation & compute
GPU-side logprobs tensor
* Defer Pythonization of sampling result & logprobs
tensor
* Encapsulate arguments required for deferred Pythonization
in the {class}`SamplerOutput` structure
* Perform GPU-side sampling computation & compute
GPU-side logprobs tensor
* Defer Pythonization of sampling result & logprobs
tensor
* Encapsulate arguments required for deferred Pythonization
in the
[`SamplerOutput`][vllm.model_executor.layers.sampler.SamplerOutput]
structure
Args:
logits: (num_tokens, vocab_size).
......
vllm/model_executor/layers/typical_acceptance_sampler.py
View file @ 4eabe123
......@@ -93,29 +93,27 @@ class TypicalAcceptanceSampler(SpecDecodeDeterministicBaseSampler):
Evaluates and returns a mask of accepted tokens based on the
posterior probabilities.
Parameters:
----------
target_probs : torch.Tensor
A tensor of shape (batch_size, k, vocab_size) representing
the probabilities of each token in the vocabulary for each
position in the proposed sequence. This is the distribution
generated by the target model.
draft_token_ids : torch.Tensor
A tensor of shape (batch_size, k) representing the proposed
token ids.
Args:
target_probs (torch.Tensor): A tensor of shape
(batch_size, k, vocab_size) representing the probabilities of
each token in the vocabulary for each position in the proposed
sequence. This is the distribution generated by the target
model.
draft_token_ids (torch.Tensor): A tensor of shape (batch_size, k)
representing the proposed token ids.
A draft token_id x_{n+k} is accepted if it satisfies the
following condition
:::{math}
$$
p_{\text{original}}(x_{n+k} | x_1, x_2, \dots, x_{n+k-1}) >
\min \left( \epsilon, \delta * \exp \left(
-H(p_{\text{original}}(
\cdot | x_1, x_2, \ldots, x_{n+k-1})) \right) \right)
:::
$$
where {math}`p_{\text{original}}` corresponds to target_probs
and {math}`\epsilon` and {math}`\delta` correspond to hyperparameters
where $p_{\text{original}}$ corresponds to target_probs
and $\epsilon$ and $\delta$ correspond to hyperparameters
specified using self._posterior_threshold and self._posterior_alpha
This method computes the posterior probabilities for the given
......@@ -126,13 +124,10 @@ class TypicalAcceptanceSampler(SpecDecodeDeterministicBaseSampler):
returns a boolean mask indicating which tokens can be accepted.
Returns:
-------
torch.Tensor
A boolean tensor of shape (batch_size, k) where each element
indicates whether the corresponding draft token has been accepted
or rejected. True indicates acceptance and false indicates
rejection.
torch.Tensor: A boolean tensor of shape (batch_size, k) where each
element indicates whether the corresponding draft token has
been accepted or rejected. True indicates acceptance and false
indicates rejection.
"""
device = target_probs.device
candidates_prob = torch.gather(
......@@ -156,17 +151,14 @@ class TypicalAcceptanceSampler(SpecDecodeDeterministicBaseSampler):
The recovered token ids will fill the first unmatched token
by the target token.
Parameters
----------
target_probs : torch.Tensor
A tensor of shape (batch_size, k, vocab_size) containing
the target probability distribution
Returns
-------
torch.Tensor
A tensor of shape (batch_size, k) with the recovered token
ids which are selected from target probs.
Args:
target_probs (torch.Tensor): A tensor of shape
(batch_size, k, vocab_size) containing the target probability
distribution.
Returns:
torch.Tensor: A tensor of shape (batch_size, k) with the recovered
token ids which are selected from target probs.
"""
max_indices = torch.argmax(target_probs, dim=-1)
......
vllm/model_executor/model_loader/__init__.py
View file @ 4eabe123
# SPDX-License-Identifier: Apache-2.0
from typing import Optional
from torch import nn
from vllm.config import LoadConfig, LoadFormat, VllmConfig
from vllm.config import LoadConfig, LoadFormat, ModelConfig, VllmConfig
from vllm.model_executor.model_loader.base_loader import BaseModelLoader
from vllm.model_executor.model_loader.bitsandbytes_loader import (
BitsAndBytesModelLoader)
......@@ -47,9 +49,14 @@ def get_model_loader(load_config: LoadConfig) -> BaseModelLoader:
return DefaultModelLoader(load_config)
def get_model(*, vllm_config: VllmConfig) -> nn.Module:
def get_model(*,
vllm_config: VllmConfig,
model_config: Optional[ModelConfig] = None) -> nn.Module:
loader = get_model_loader(vllm_config.load_config)
return loader.load_model(vllm_config=vllm_config)
if model_config is None:
model_config = vllm_config.model_config
return loader.load_model(vllm_config=vllm_config,
model_config=model_config)
__all__ = [
......
vllm/model_executor/model_loader/base_loader.py
View file @ 4eabe123
......@@ -18,6 +18,7 @@ class BaseModelLoader(ABC):
raise NotImplementedError
@abstractmethod
def load_model(self, *, vllm_config: VllmConfig) -> nn.Module:
def load_model(self, *, vllm_config: VllmConfig,
model_config: ModelConfig) -> nn.Module:
"""Load a model with the given configurations."""
raise NotImplementedError
vllm/model_executor/model_loader/bitsandbytes_loader.py
View file @ 4eabe123
......@@ -569,10 +569,9 @@ class BitsAndBytesModelLoader(BaseModelLoader):
def download_model(self, model_config: ModelConfig) -> None:
self._prepare_weights(model_config.model, model_config.revision)
def load_model(self, vllm_config: VllmConfig) -> nn.Module:
def load_model(self, vllm_config: VllmConfig,
model_config: ModelConfig) -> nn.Module:
device_config = vllm_config.device_config
model_config = vllm_config.model_config
with set_default_torch_dtype(model_config.dtype):
with torch.device(device_config.device):
......
vllm/model_executor/model_loader/default_loader.py
View file @ 4eabe123
......@@ -11,8 +11,8 @@ import torch
from torch import nn
from transformers.utils import SAFE_WEIGHTS_INDEX_NAME
from vllm import envs
from vllm.config import LoadConfig, LoadFormat, ModelConfig, VllmConfig
from vllm.envs import VLLM_USE_MODELSCOPE
from vllm.logger import init_logger
from vllm.model_executor.model_loader.base_loader import BaseModelLoader
from vllm.model_executor.model_loader.utils import (
......@@ -64,7 +64,7 @@ class DefaultModelLoader(BaseModelLoader):
Returns the path to the downloaded model, or None if the model is not
downloaded from ModelScope."""
if VLLM_USE_MODELSCOPE:
if envs.VLLM_USE_MODELSCOPE:
# download model from ModelScope hub,
# lazy import so that modelscope is not required for normal use.
# pylint: disable=C.
......@@ -264,13 +264,14 @@ class DefaultModelLoader(BaseModelLoader):
fall_back_to_pt=True,
allow_patterns_overrides=None)
def load_model(self, vllm_config: VllmConfig) -> nn.Module:
def load_model(self, vllm_config: VllmConfig,
model_config: ModelConfig) -> nn.Module:
device_config = vllm_config.device_config
model_config = vllm_config.model_config
target_device = torch.device(device_config.device)
with set_default_torch_dtype(model_config.dtype):
with target_device:
model = initialize_model(vllm_config=vllm_config)
model = initialize_model(vllm_config=vllm_config,
model_config=model_config)
weights_to_load = {name for name, _ in model.named_parameters()}
loaded_weights = model.load_weights(
......
vllm/model_executor/model_loader/dummy_loader.py
View file @ 4eabe123
......@@ -22,9 +22,9 @@ class DummyModelLoader(BaseModelLoader):
def download_model(self, model_config: ModelConfig) -> None:
pass # Nothing to download
def load_model(self, vllm_config: VllmConfig) -> nn.Module:
def load_model(self, vllm_config: VllmConfig,
model_config: ModelConfig) -> nn.Module:
device_config = vllm_config.device_config
model_config = vllm_config.model_config
target_device = torch.device(device_config.device)
with set_default_torch_dtype(model_config.dtype):
with target_device:
......
vllm/model_executor/model_loader/gguf_loader.py
View file @ 4eabe123
......@@ -92,9 +92,9 @@ class GGUFModelLoader(BaseModelLoader):
def download_model(self, model_config: ModelConfig) -> None:
self._prepare_weights(model_config.model)
def load_model(self, vllm_config: VllmConfig) -> nn.Module:
def load_model(self, vllm_config: VllmConfig,
model_config: ModelConfig) -> nn.Module:
device_config = vllm_config.device_config
model_config = vllm_config.model_config
local_model_path = self._prepare_weights(model_config.model)
gguf_weights_map = self._get_gguf_weights_map(model_config)
# we can only know if tie word embeddings after mapping weights
......
vllm/model_executor/model_loader/neuronx_distributed.py
View file @ 4eabe123
......@@ -87,16 +87,29 @@ class NeuronCausalLM(nn.Module):
input_block_ids: torch.Tensor,
sampling_params: torch.Tensor,
) -> torch.Tensor:
# sort block ids sequentially for perf/neuron support reasons
sorted_input_block_ids, sorted_indices = torch.sort(input_block_ids)
input_ids = torch.index_select(input_ids, 0, sorted_indices)
positions = torch.index_select(positions, 0, sorted_indices)
sampling_params = torch.index_select(sampling_params, 0,
sorted_indices)
output = self.model(input_ids,
attention_mask=None,
position_ids=positions,
seq_ids=input_block_ids,
seq_ids=sorted_input_block_ids,
sampling_params=sampling_params)
# on-device sampling
if self.config.neuron_config.on_device_sampling_config:
return output.hidden_states
output = output.hidden_states
else:
return output.logits[:, -1, :]
output = output.logits[:, -1, :]
restored_indices = torch.argsort(sorted_indices)
if input_block_ids.shape[0] != 1:
output = torch.index_select(output, 0, restored_indices)
return output
def compute_logits(self, hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata) -> torch.Tensor:
......@@ -340,14 +353,26 @@ class NeuronSpeculationCausalLM(nn.Module):
input_block_ids: torch.Tensor,
sampling_params: torch.Tensor,
) -> torch.Tensor:
# sort block ids sequentially for perf/neuron support reasons
sorted_input_block_ids, sorted_indices = torch.sort(input_block_ids)
input_ids = torch.index_select(input_ids, 0, sorted_indices)
positions = torch.index_select(positions, 0, sorted_indices)
sampling_params = torch.index_select(sampling_params, 0,
sorted_indices)
output = self.model(input_ids,
attention_mask=None,
position_ids=positions,
seq_ids=input_block_ids,
seq_ids=sorted_input_block_ids,
sampling_params=sampling_params)
restored_indices = torch.argsort(sorted_indices)
# CTX encoding
if (positions[:, 0]).sum().item() == 0:
return output.fused_outputs[0][:, 0:1]
output = output.fused_outputs[0][:, 0:1]
if input_block_ids.shape[0] != 1:
output = torch.index_select(output, 0, restored_indices)
return output
# Fused Spec (Generation)
accepted_tokens_with_padding = output.fused_outputs[0]
......@@ -362,6 +387,10 @@ class NeuronSpeculationCausalLM(nn.Module):
-1) >= generated_token_counts
accepted_tokens_with_padding[mask] = -1
if input_block_ids.shape[0] != 1:
accepted_tokens_with_padding = torch.index_select(
accepted_tokens_with_padding, 0, restored_indices)
return accepted_tokens_with_padding
def sample(
......@@ -416,6 +445,10 @@ class NeuronSpeculationCausalLM(nn.Module):
draft_neuron_config.speculation_length = 0
draft_neuron_config.trace_tokengen_model = True
draft_neuron_config.enable_fused_speculation = False
if getattr(config.neuron_config, "draft_model_modules_to_not_convert",
None):
draft_neuron_config.modules_to_not_convert = (
draft_neuron_config.draft_model_modules_to_not_convert)
if config.neuron_config.enable_eagle_speculation:
draft_neuron_config.is_eagle_draft = True
draft_neuron_config.sequence_parallel_enabled = False
......@@ -502,7 +535,7 @@ def _get_default_neuron_config(model_config: ModelConfig,
max_context_length=scheduler_config.max_model_len,
seq_len=scheduler_config.max_model_len,
enable_bucketing=True,
is_continuous_batching=(batch_size > 1),
is_continuous_batching=True,
quantized=False,
torch_dtype=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
padding_side="right",
......@@ -520,6 +553,7 @@ def _get_default_speculation_config(model_config: ModelConfig,
args."""
neuron_config = dict(
tp_degree=parallel_config.tensor_parallel_size,
ctx_batch_size=1,
batch_size=scheduler_config.max_num_seqs,
max_context_length=scheduler_config.max_model_len,
seq_len=scheduler_config.max_model_len,
......@@ -527,6 +561,7 @@ def _get_default_speculation_config(model_config: ModelConfig,
trace_tokengen_model=False,
enable_fused_speculation=True,
enable_bucketing=True,
is_continuous_batching=True,
quantized=False,
torch_dtype=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
on_device_sampling_config=dict(
......
vllm/model_executor/model_loader/runai_streamer_loader.py
View file @ 4eabe123
......@@ -100,11 +100,10 @@ class RunaiModelStreamerLoader(BaseModelLoader):
"""Download model if necessary"""
self._prepare_weights(model_config.model, model_config.revision)
def load_model(self, vllm_config: VllmConfig) -> nn.Module:
def load_model(self, vllm_config: VllmConfig,
model_config: ModelConfig) -> nn.Module:
"""Perform streaming of the model to destination"""
device_config = vllm_config.device_config
model_config = vllm_config.model_config
target_device = torch.device(device_config.device)
with set_default_torch_dtype(model_config.dtype):
with target_device:
......
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