init src 0.9.2

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Custom activation functions."""
import math
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from vllm.distributed import (divide, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
from vllm.model_executor.custom_op import CustomOp
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.utils import LazyDict
import vllm.envs as envs
@CustomOp.register("fatrelu_and_mul")
class FatreluAndMul(CustomOp):
"""An activation function for FATReLU.
The function computes x -> FATReLU(x[:d]) * x[d:] where
d = x.shape[-1] // 2.
This is used in openbmb/MiniCPM-S-1B-sft.
Shapes:
x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
return: (num_tokens, d) or (batch_size, seq_len, d)
"""
def __init__(self, threshold: float = 0.):
super().__init__()
self.threshold = threshold
if current_platform.is_cuda_alike():
self.op = torch.ops._C.fatrelu_and_mul
elif current_platform.is_cpu():
self._forward_method = self.forward_native
def forward_native(self, x: torch.Tensor) -> torch.Tensor:
d = x.shape[-1] // 2
x1 = x[..., :d]
x2 = x[..., d:]
x1 = F.threshold(x1, self.threshold, 0.0)
return x1 * x2
def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
d = x.shape[-1] // 2
output_shape = (x.shape[:-1] + (d, ))
out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
self.op(out, x, self.threshold)
return out
@CustomOp.register("silu_and_mul")
class SiluAndMul(CustomOp):
"""An activation function for SwiGLU.
The function computes x -> silu(x[:d]) * x[d:] where d = x.shape[-1] // 2.
Shapes:
x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
return: (num_tokens, d) or (batch_size, seq_len, d)
"""
def __init__(self):
super().__init__()
if current_platform.is_cuda_alike() or current_platform.is_cpu():
self.op = torch.ops._C.silu_and_mul
self.op_opt = torch.ops._C.silu_and_mul_opt
elif current_platform.is_xpu():
from vllm._ipex_ops import ipex_ops
self.op = ipex_ops.silu_and_mul
def forward_native(self, x: torch.Tensor) -> torch.Tensor:
"""PyTorch-native implementation equivalent to forward()."""
d = x.shape[-1] // 2
return F.silu(x[..., :d]) * x[..., d:]
def forward_cuda(self, x: torch.Tensor) -> 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 envs.VLLM_USE_OPT_OP:
self.op_opt(out, x)
else:
self.op(out, x)
return out
def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
d = x.shape[-1] // 2
output_shape = (x.shape[:-1] + (d, ))
out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
self.op(out, x)
return out
def forward_neuron(self, x: torch.Tensor) -> torch.Tensor:
d = x.shape[-1] // 2
x_reshaped = x.view(-1, x.shape[-1])
s = x_reshaped[:, :d] * F.sigmoid(x_reshaped[:, :d])
result = s * x_reshaped[:, d:]
return result.view(*x.shape[:-1], d)
@CustomOp.register("mul_and_silu")
class MulAndSilu(CustomOp):
"""An activation function for SwiGLU.
The function computes x -> x[:d] * silu(x[d:]) where d = x.shape[-1] // 2.
Shapes:
x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
return: (num_tokens, d) or (batch_size, seq_len, d)
"""
def __init__(self):
super().__init__()
if current_platform.is_cuda_alike():
self.op = torch.ops._C.mul_and_silu
elif current_platform.is_xpu():
from vllm._ipex_ops import ipex_ops
self.op = ipex_ops.silu_and_mul
elif current_platform.is_cpu():
self._forward_method = self.forward_native
def forward_native(self, x: torch.Tensor) -> torch.Tensor:
"""PyTorch-native implementation equivalent to forward()."""
d = x.shape[-1] // 2
return x[..., :d] * F.silu(x[..., d:])
def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
d = x.shape[-1] // 2
output_shape = (x.shape[:-1] + (d, ))
out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
self.op(out, x)
return out
# TODO implement forward_xpu for MulAndSilu
# def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
@CustomOp.register("gelu_and_mul_sparse")
class GeluAndMulSparse(CustomOp):
"""An activation function for GeluAndMulSparse.
This activation function is used in Gemma3n. It computes:
up_proj = self.up_proj(x)
gate_proj = self.gate_proj(x)
gate_proj = self._gaussian_topk(gate_proj) # sparsity
activations = self.act_fn(gate_proj) # gelu
down_proj = self.down_proj(activations * up_proj)
Shapes:
x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
return: (num_tokens, d) or (batch_size, seq_len, d)
"""
def __init__(self, activation_sparsity: float, approximate: str = "none"):
super().__init__()
# Gelu.
self.approximate = approximate
if approximate not in ("none", "tanh"):
raise ValueError(f"Unknown approximate mode: {approximate}")
# Sparsity.
if activation_sparsity == 0.0:
raise ValueError(
"activation_sparsity is 0.0. Please use GeluAndMul.")
target_sparsity_tensor = torch.tensor(activation_sparsity,
dtype=torch.float32)
normal_dist = torch.distributions.normal.Normal(0, 1)
self.std_multiplier = normal_dist.icdf(target_sparsity_tensor)
def _gaussian_topk(self, x: torch.Tensor) -> torch.Tensor:
"""Get % sparse percentile of the Gaussian distribution."""
# NOTE(rob): for TP>1, we could all-gather to get the means/std.
# But we do not do this because in expectation they are the same
# and in practice the eval scores are good without gathering.
mean = torch.mean(x, dim=-1, keepdim=True)
std = torch.std(x, dim=-1, keepdim=True, unbiased=False)
cutoff_x = mean + std * self.std_multiplier
return nn.functional.relu(x - cutoff_x)
def forward_native(self, x: torch.Tensor) -> torch.Tensor:
"""PyTorch-native implementation equivalent to forward()."""
d = x.shape[-1] // 2
out = self._gaussian_topk(x[..., :d])
out = F.gelu(out, approximate=self.approximate)
return out * x[..., d:]
def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
return self.forward_native(x)
@CustomOp.register("gelu_and_mul")
class GeluAndMul(CustomOp):
"""An activation function for GeGLU.
The function computes x -> GELU(x[:d]) * x[d:] where d = x.shape[-1] // 2.
Shapes:
x: (batch_size, seq_len, 2 * d) or (num_tokens, 2 * d)
return: (batch_size, seq_len, d) or (num_tokens, d)
"""
def __init__(self, approximate: str = "none"):
super().__init__()
self.approximate = approximate
if approximate not in ("none", "tanh"):
raise ValueError(f"Unknown approximate mode: {approximate}")
if current_platform.is_cuda_alike() or current_platform.is_cpu():
if approximate == "none":
self.op = torch.ops._C.gelu_and_mul
self.op_opt = torch.ops._C.gelu_and_mul_opt
elif approximate == "tanh":
self.op = torch.ops._C.gelu_tanh_and_mul
self.op_opt = torch.ops._C.gelu_tanh_and_mul_opt
elif current_platform.is_xpu():
from vllm._ipex_ops import ipex_ops
if approximate == "none":
self.op = ipex_ops.gelu_and_mul
else:
self.op = ipex_ops.gelu_tanh_and_mul
def forward_native(self, x: torch.Tensor) -> torch.Tensor:
"""PyTorch-native implementation equivalent to forward()."""
d = x.shape[-1] // 2
return F.gelu(x[..., :d], approximate=self.approximate) * x[..., d:]
def forward_cuda(self, x: torch.Tensor) -> 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 envs.VLLM_USE_OPT_OP:
self.op_opt(out, x)
else:
self.op(out, x)
return out
def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
d = x.shape[-1] // 2
output_shape = (x.shape[:-1] + (d, ))
out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
self.op(out, x)
return out
def extra_repr(self) -> str:
return f'approximate={repr(self.approximate)}'
@CustomOp.register("gelu_new")
class NewGELU(CustomOp):
def __init__(self):
super().__init__()
if current_platform.is_cuda_alike() or current_platform.is_cpu():
self.op = torch.ops._C.gelu_new
elif current_platform.is_xpu():
from vllm._ipex_ops import ipex_ops
self.op = ipex_ops.gelu_new
def forward_native(self, x: torch.Tensor) -> torch.Tensor:
"""PyTorch-native implementation equivalent to forward()."""
c = math.sqrt(2.0 / math.pi)
return 0.5 * x * (1.0 + torch.tanh(c *
(x + 0.044715 * torch.pow(x, 3.0))))
def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
out = torch.empty_like(x)
self.op(out, x)
return out
def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
return self.op(x)
@CustomOp.register("gelu_fast")
class FastGELU(CustomOp):
def __init__(self):
super().__init__()
if current_platform.is_cuda_alike() or current_platform.is_cpu():
self.op = torch.ops._C.gelu_fast
elif current_platform.is_xpu():
from vllm._ipex_ops import ipex_ops
self.op = ipex_ops.gelu_fast
def forward_native(self, x: torch.Tensor) -> torch.Tensor:
"""PyTorch-native implementation equivalent to forward()."""
return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 *
(1.0 + 0.044715 * x * x)))
def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
out = torch.empty_like(x)
self.op(out, x)
return out
def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
return self.op(x)
@CustomOp.register("quick_gelu")
class QuickGELU(CustomOp):
# https://github.com/huggingface/transformers/blob/main/src/transformers/activations.py#L90
def __init__(self):
super().__init__()
if current_platform.is_cuda_alike() or current_platform.is_cpu():
self.op = torch.ops._C.gelu_quick
elif current_platform.is_xpu():
from vllm._ipex_ops import ipex_ops
self.op = ipex_ops.gelu_quick
def forward_native(self, x: torch.Tensor) -> torch.Tensor:
"""PyTorch-native implementation equivalent to forward()."""
return x * torch.sigmoid(1.702 * x)
def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
out = torch.empty_like(x)
self.op(out, x)
return out
def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
out = torch.empty_like(x)
self.op(out, x)
return out
# TODO implement forward_xpu for QuickGELU
# def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
@CustomOp.register("relu2")
class ReLUSquaredActivation(CustomOp):
"""
Applies the relu^2 activation introduced in https://arxiv.org/abs/2109.08668v2
"""
def forward_native(self, x: torch.Tensor) -> torch.Tensor:
"""PyTorch-native implementation equivalent to forward()."""
return torch.square(F.relu(x))
def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
return self.forward_native(x)
class ScaledActivation(nn.Module):
"""An activation function with post-scale parameters.
This is used for some quantization methods like AWQ.
"""
def __init__(
self,
act_module: nn.Module,
intermediate_size: int,
input_is_parallel: bool = True,
params_dtype: Optional[torch.dtype] = None,
):
super().__init__()
self.act = act_module
self.input_is_parallel = input_is_parallel
if input_is_parallel:
tp_size = get_tensor_model_parallel_world_size()
intermediate_size_per_partition = divide(intermediate_size,
tp_size)
else:
intermediate_size_per_partition = intermediate_size
if params_dtype is None:
params_dtype = torch.get_default_dtype()
self.scales = nn.Parameter(
torch.empty(intermediate_size_per_partition, dtype=params_dtype))
set_weight_attrs(self.scales, {"weight_loader": self.weight_loader})
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.act(x) / self.scales
def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
param_data = param.data
if self.input_is_parallel:
tp_rank = get_tensor_model_parallel_rank()
shard_size = param_data.shape[0]
start_idx = tp_rank * shard_size
loaded_weight = loaded_weight.narrow(0, start_idx, shard_size)
assert param_data.shape == loaded_weight.shape
param_data.copy_(loaded_weight)
_ACTIVATION_REGISTRY = LazyDict({
"gelu":
lambda: nn.GELU(),
"gelu_fast":
lambda: FastGELU(),
"gelu_new":
lambda: NewGELU(),
"gelu_pytorch_tanh":
lambda: nn.GELU(approximate="tanh"),
"relu":
lambda: nn.ReLU(),
"relu2":
lambda: ReLUSquaredActivation(),
"silu":
lambda: nn.SiLU(),
"quick_gelu":
lambda: QuickGELU(),
})
def get_act_fn(act_fn_name: str) -> nn.Module:
"""Get an activation function by name."""
act_fn_name = act_fn_name.lower()
if act_fn_name not in _ACTIVATION_REGISTRY:
raise ValueError(
f"Activation function {act_fn_name!r} is not supported.")
return _ACTIVATION_REGISTRY[act_fn_name]
_ACTIVATION_AND_MUL_REGISTRY = LazyDict({
"gelu": lambda: GeluAndMul(),
"silu": lambda: SiluAndMul(),
"geglu": lambda: GeluAndMul(),
})
def get_act_and_mul_fn(act_fn_name: str) -> nn.Module:
"""Get an activation-and-mul (i.e. SiluAndMul) function by name."""
act_fn_name = act_fn_name.lower()
if act_fn_name not in _ACTIVATION_AND_MUL_REGISTRY:
raise ValueError(
f"Activation function {act_fn_name!r} is not supported.")
return _ACTIVATION_AND_MUL_REGISTRY[act_fn_name]

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from contextlib import contextmanager
from typing import Any, Optional
from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
from vllm.model_executor.layers.fused_moe.layer import (
FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
from vllm.model_executor.layers.fused_moe.modular_kernel import (
FusedMoEActivationFormat, FusedMoEPermuteExpertsUnpermute,
FusedMoEPrepareAndFinalize)
from vllm.triton_utils import HAS_TRITON
_config: Optional[dict[str, Any]] = None
@contextmanager
def override_config(config):
global _config
old_config = _config
_config = config
yield
_config = old_config
def get_config() -> Optional[dict[str, Any]]:
return _config
__all__ = [
"FusedMoE",
"FusedMoEConfig",
"FusedMoEMethodBase",
"FusedMoeWeightScaleSupported",
"FusedMoEPermuteExpertsUnpermute",
"FusedMoEActivationFormat",
"FusedMoEPrepareAndFinalize",
"override_config",
"get_config",
]
if HAS_TRITON:
# import to register the custom ops
import vllm.model_executor.layers.fused_moe.fused_marlin_moe # noqa
import vllm.model_executor.layers.fused_moe.fused_moe # noqa
from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
BatchedDeepGemmExperts)
from vllm.model_executor.layers.fused_moe.batched_triton_or_deep_gemm_moe import ( # noqa: E501
BatchedTritonOrDeepGemmExperts)
from vllm.model_executor.layers.fused_moe.cutlass_moe import (
CutlassExpertsFp8, cutlass_moe_fp4, cutlass_moe_fp8)
from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
DeepGemmExperts)
from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
BatchedTritonExperts)
from vllm.model_executor.layers.fused_moe.fused_moe import (
TritonExperts, fused_experts, fused_moe, fused_topk,
get_config_file_name, grouped_topk)
from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
TritonOrDeepGemmExperts)
__all__ += [
"fused_moe",
"fused_topk",
"fused_experts",
"get_config_file_name",
"grouped_topk",
"cutlass_moe_fp8",
"cutlass_moe_fp4",
"CutlassExpertsFp8",
"TritonExperts",
"BatchedTritonExperts",
"DeepGemmExperts",
"BatchedDeepGemmExperts",
"TritonOrDeepGemmExperts",
"BatchedTritonOrDeepGemmExperts",
]

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional
import torch
import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
from vllm.model_executor.layers.fused_moe.utils import _resize_cache
from vllm.triton_utils import tl, triton
logger = init_logger(__name__)
@triton.jit
def _silu_mul_fp8_quant_deep_gemm(
# Pointers ------------------------------------------------------------
input_ptr, # 16-bit activations (E, T, 2*H)
y_q_ptr, # fp8 quantized activations (E, T, H)
y_s_ptr, # 16-bit scales (E, T, G)
counts_ptr, # int32 num tokens per expert (E)
# Sizes ---------------------------------------------------------------
H: tl.constexpr, # hidden dimension (per output)
GROUP_SIZE: tl.constexpr, # elements per group (usually 128)
# Strides for input (elements) ---------------------------------------
stride_i_e,
stride_i_t,
stride_i_h,
# Strides for y_q (elements) -----------------------------------------
stride_yq_e,
stride_yq_t,
stride_yq_h,
# Strides for y_s (elements) -----------------------------------------
stride_ys_e,
stride_ys_t,
stride_ys_g,
# Stride for counts (elements)
stride_counts_e,
# Numeric params ------------------------------------------------------
eps: tl.constexpr,
fp8_min: tl.constexpr,
fp8_max: tl.constexpr,
# Meta ---------------------------------------------------------------
BLOCK: tl.constexpr,
):
G = H // GROUP_SIZE
# map program id -> (e, g)
pid = tl.program_id(0)
e = pid // G
g = pid % G
e = e.to(tl.int64)
g = g.to(tl.int64)
# number of valid tokens for this expert
n_tokens = tl.load(counts_ptr + e * stride_counts_e).to(tl.int64)
cols = tl.arange(0, BLOCK)
cols = cols.to(tl.int64)
mask_h = cols < BLOCK
t = tl.zeros([], tl.int64)
while t < n_tokens:
base_i_offset = (e * stride_i_e + t * stride_i_t +
g * GROUP_SIZE * stride_i_h)
base_yq_offset = (e * stride_yq_e + t * stride_yq_t +
g * GROUP_SIZE * stride_yq_h)
base_ys_offset = e * stride_ys_e + t * stride_ys_t + g * stride_ys_g
mask = mask_h
x = tl.load(input_ptr + base_i_offset + cols * stride_i_h,
mask=mask,
other=0.0).to(tl.float32)
y2 = tl.load(input_ptr + base_i_offset + H * stride_i_h +
cols * stride_i_h,
mask=mask,
other=0.0).to(tl.float32)
x = x * (1.0 / (1.0 + tl.exp(-x)))
y = x * y2
_absmax = tl.maximum(tl.max(tl.abs(y)), eps)
y_s = _absmax / fp8_max
y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)
tl.store(y_q_ptr + base_yq_offset + cols * stride_yq_h, y_q, mask=mask)
tl.store(y_s_ptr + base_ys_offset, y_s)
t += 1
def silu_mul_fp8_quant_deep_gemm(
y: torch.Tensor, # (E, T, 2*H) float32
tokens_per_expert: torch.Tensor, # (E,) number of valid tokens per expert
group_size: int = 128,
eps: float = 1e-10,
):
"""Quantize silu(y[..., :H]) * y[..., H:] to FP8 with group per-token scales
y has shape (E, T, 2*H). The first half of the last dimension is
silu-activated, multiplied by the second half, then quantized into FP8.
Returns `(y_q, y_s)` where
* `y_q` is the FP8 tensor of shape `(E, T, H)`, same layout as `y[..., :H]`.
* `y_s` has shape `(E, T, H // group_size)` and strides `(T*G, 1, T)`
"""
assert y.ndim == 3, "y must be (E, T, 2*H)"
E, T, H2 = y.shape
assert H2 % 2 == 0, "last dim of y must be even (2*H)"
H = H2 // 2
G = H // group_size
assert H % group_size == 0, "H must be divisible by group_size"
assert tokens_per_expert.ndim == 1 and tokens_per_expert.shape[0] == E, \
"tokens_per_expert must be shape (E,)"
tokens_per_expert = tokens_per_expert.to(device=y.device,
dtype=torch.int32)
# allocate outputs
fp8_dtype = torch.float8_e4m3fn
y_q = torch.empty((E, T, H), dtype=fp8_dtype, device=y.device)
# strides (elements)
stride_i_e, stride_i_t, stride_i_h = y.stride()
stride_yq_e, stride_yq_t, stride_yq_h = y_q.stride()
# desired scale strides (elements): (T*G, 1, T)
stride_ys_e = T * G
stride_ys_t = 1
stride_ys_g = T
y_s = torch.empty_strided((E, T, G),
(stride_ys_e, stride_ys_t, stride_ys_g),
dtype=torch.float32,
device=y.device)
stride_cnt_e = tokens_per_expert.stride()[0]
# static grid over experts and H-groups.
# A loop inside the kernel handles the token dim
grid = (E * G, )
f_info = torch.finfo(fp8_dtype)
fp8_max = f_info.max
fp8_min = f_info.min
_silu_mul_fp8_quant_deep_gemm[grid](
y,
y_q,
y_s,
tokens_per_expert,
H,
group_size,
stride_i_e,
stride_i_t,
stride_i_h,
stride_yq_e,
stride_yq_t,
stride_yq_h,
stride_ys_e,
stride_ys_t,
stride_ys_g,
stride_cnt_e,
eps,
fp8_min,
fp8_max,
BLOCK=group_size,
num_warps=4,
)
return y_q, y_s
class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
# The Deep Gemm kernels only support block size of 128
DEEPGEMM_BLOCK_SHAPE: list[int] = [128, 128]
def __init__(self,
max_num_tokens: int,
num_dispatchers: int,
block_shape: list[int],
per_act_token_quant=False):
"""
max_num_tokens: Maximum number of tokens from a DP Rank
num_dispatchers: The number of DP dispatchers.
block_shape: Block quantization block shape.
per_act_token_quant: Per activation token quantization flag.
"""
super().__init__(
FusedMoEQuantConfig(
quant_dtype=torch.float8_e4m3fn,
per_act_token_quant=per_act_token_quant,
block_shape=block_shape,
))
assert self.block_shape == self.DEEPGEMM_BLOCK_SHAPE
self.max_num_tokens = max_num_tokens
self.num_dispatchers = num_dispatchers
@property
def activation_formats(
self
) -> tuple[mk.FusedMoEActivationFormat, mk.FusedMoEActivationFormat]:
return (mk.FusedMoEActivationFormat.BatchedExperts,
mk.FusedMoEActivationFormat.BatchedExperts)
def supports_chunking(self) -> bool:
return False
def supports_expert_map(self) -> bool:
return False
def workspace_shapes(
self,
a: torch.Tensor,
aq: torch.Tensor,
M: int,
N: int,
K: int,
topk: int,
global_num_experts: int,
local_num_experts: int,
) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], torch.dtype]:
assert a.dim() == 2
# FIXME (varun): We should be able to dispatch only from the leader
# DP ranks in the case of TP > 1. At the moment, all the Ranks
# end up sending their tokens. This needs to be fixed.
num_dispatchers = self.num_dispatchers
num_experts = local_num_experts
max_num_tokens = a.size(
0) if self.max_num_tokens is None else self.max_num_tokens
workspace13 = (num_experts, max_num_tokens * num_dispatchers,
max(K, N))
workspace2 = (num_experts, max_num_tokens * num_dispatchers, (N // 2))
output = (num_experts, max_num_tokens * num_dispatchers, K)
return (workspace13, workspace2, output, a.dtype)
def apply(
self,
output: torch.Tensor,
hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_ids: torch.Tensor,
activation: str,
global_num_experts: int,
expert_map: Optional[torch.Tensor],
w1_scale: Optional[torch.Tensor],
w2_scale: Optional[torch.Tensor],
w1_zp: Optional[torch.Tensor],
w2_zp: Optional[torch.Tensor],
a1q_scale: Optional[torch.Tensor],
a2_scale: Optional[torch.Tensor],
workspace13: torch.Tensor,
workspace2: torch.Tensor,
expert_num_tokens: Optional[torch.Tensor],
):
import deep_gemm as dg
assert hidden_states.ndim == 3
assert self.block_shape is not None
a1q = hidden_states
_, N, K = w1.size()
assert w2.size(1) == K
E, max_num_tokens, N, K, top_k_num = mk._moe_problem_size(
hidden_states, w1, w2, topk_ids)
workspace1 = _resize_cache(workspace13, (E, max_num_tokens, N))
# (from deepgemm docs) : A value hint (which is a value on CPU)
# for the M expectation of each batch, correctly setting this value
# may lead to better performance.
expected_m = max_num_tokens
dg.m_grouped_gemm_fp8_fp8_bf16_nt_masked((a1q, a1q_scale),
(w1, w1_scale),
out=workspace1,
masked_m=expert_num_tokens,
expected_m=expected_m)
assert expert_num_tokens is not None
a2q, a2q_scale = silu_mul_fp8_quant_deep_gemm(workspace1,
expert_num_tokens)
dg.m_grouped_gemm_fp8_fp8_bf16_nt_masked((a2q, a2q_scale),
(w2, w2_scale),
out=output,
masked_m=expert_num_tokens,
expected_m=expected_m)

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional
import torch
import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
BatchedDeepGemmExperts)
from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
BatchedTritonExperts)
class BatchedTritonOrDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
def __init__(self,
max_num_tokens: int,
num_dispatchers: int,
use_fp8_w8a8: bool = False,
use_int8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
block_shape: Optional[list[int]] = None,
per_act_token_quant: bool = False,
allow_deep_gemm: bool = False):
assert not use_int8_w8a8, "NYI"
assert not use_int8_w8a16, "NYI"
assert not use_int4_w4a16, "NYI"
super().__init__(
FusedMoEQuantConfig.make(
use_fp8_w8a8=use_fp8_w8a8,
use_int8_w8a8=use_int8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
block_shape=block_shape,
per_act_token_quant=per_act_token_quant,
))
self.allow_deep_gemm = allow_deep_gemm
self.batched_triton_experts = BatchedTritonExperts(
max_num_tokens=max_num_tokens,
num_dispatchers=num_dispatchers,
use_fp8_w8a8=use_fp8_w8a8,
use_int8_w8a8=use_int8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
per_act_token_quant=self.per_act_token_quant,
block_shape=self.block_shape,
)
self.allow_deep_gemm = (allow_deep_gemm and use_fp8_w8a8
and self.block_shape
== BatchedDeepGemmExperts.DEEPGEMM_BLOCK_SHAPE)
self.batched_deep_gemm_experts = BatchedDeepGemmExperts(
max_num_tokens=max_num_tokens,
num_dispatchers=num_dispatchers,
block_shape=self.block_shape, # type: ignore[arg-type]
) if self.allow_deep_gemm else None
assert (self.batched_deep_gemm_experts is not None
or self.batched_triton_experts is not None)
@property
def activation_formats(
self
) -> tuple[mk.FusedMoEActivationFormat, mk.FusedMoEActivationFormat]:
if self.batched_triton_experts is not None:
assert (self.batched_deep_gemm_experts is None
or self.batched_deep_gemm_experts.activation_formats
== self.batched_triton_experts.activation_formats)
return self.batched_triton_experts.activation_formats
else:
assert self.batched_deep_gemm_experts is not None
return self.batched_deep_gemm_experts.activation_formats
def supports_chunking(self) -> bool:
bdge = self.batched_deep_gemm_experts
bte = self.batched_triton_experts
return ((bdge is None or bdge.supports_chunking())
and (bte is None or bte.supports_chunking()))
def supports_expert_map(self) -> bool:
bdge = self.batched_deep_gemm_experts
bte = self.batched_triton_experts
return ((bdge is None or bdge.supports_expert_map())
and (bte is None or bte.supports_expert_map()))
def workspace_shapes(
self,
a: torch.Tensor,
aq: torch.Tensor,
M: int,
N: int,
K: int,
topk: int,
global_num_experts: int,
local_num_experts: int,
) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], torch.dtype]:
# Note: the deep gemm workspaces are strictly larger than the triton
# workspaces so we can be pessimistic here and allocate for DeepGemm
# even if we fall back to triton later, e.g. if expert maps are set.
if self.allow_deep_gemm:
assert self.batched_deep_gemm_experts is not None
return self.batched_deep_gemm_experts.workspace_shapes(
a, aq, M, N, K, topk, global_num_experts, local_num_experts)
else:
assert self.batched_triton_experts is not None
return self.batched_triton_experts.workspace_shapes(
a, aq, M, N, K, topk, global_num_experts, local_num_experts)
def apply(
self,
output: torch.Tensor,
hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_ids: torch.Tensor,
activation: str,
global_num_experts: int,
expert_map: Optional[torch.Tensor],
w1_scale: Optional[torch.Tensor],
w2_scale: Optional[torch.Tensor],
w1_zp: Optional[torch.Tensor],
w2_zp: Optional[torch.Tensor],
a1q_scale: Optional[torch.Tensor],
a2_scale: Optional[torch.Tensor],
workspace13: torch.Tensor,
workspace2: torch.Tensor,
expert_num_tokens: Optional[torch.Tensor],
):
experts = (self.batched_deep_gemm_experts
if self.allow_deep_gemm else self.batched_triton_experts)
assert experts is not None
experts.apply(output, hidden_states, w1, w2, topk_ids, activation,
global_num_experts, expert_map, w1_scale, w2_scale,
w1_zp, w2_zp, a1q_scale, a2_scale, workspace13,
workspace2, expert_num_tokens)

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass
from typing import Optional, Union
import torch
from compressed_tensors.quantization import (QuantizationArgs,
QuantizationStrategy,
QuantizationType)
import vllm.envs as envs
from vllm.config import ParallelConfig
from vllm.distributed import get_dp_group, get_tensor_model_parallel_rank
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.utils import cdiv
logger = init_logger(__name__)
def _get_quant_config_quantization_args(
quant_config: Optional[QuantizationConfig],
prop_name: str,
) -> Optional[QuantizationArgs]:
if (quant_config is not None and hasattr(quant_config, 'target_scheme_map')
and "Linear" in quant_config.target_scheme_map and
"input_activations" in quant_config.target_scheme_map["Linear"]):
return quant_config.target_scheme_map["Linear"].get(prop_name)
else:
return None
def get_quant_config_input_quant(
quant_config: Optional[QuantizationConfig]
) -> Optional[QuantizationArgs]:
return _get_quant_config_quantization_args(quant_config,
"input_activations")
def get_quant_config_weight_quant(
quant_config: Optional[QuantizationConfig]
) -> Optional[QuantizationArgs]:
return _get_quant_config_quantization_args(quant_config, "weights")
# TODO (bnell): use scalar_type instead of bools?
def get_config_quant_dtype(
use_fp8_w8a8: bool,
use_int8_w8a8: bool,
use_int8_w8a16: bool,
use_int4_w4a16: bool,
use_int4_w4a8: bool,
) -> Optional[torch.dtype]:
if use_fp8_w8a8:
return torch.float8_e4m3fn
elif use_int8_w8a8:
return torch.int8
return None
@dataclass
class FusedMoEQuantConfig:
# The post quantization activation type.
quant_dtype: Optional[torch.dtype] = None
per_act_token_quant: bool = False
per_out_ch_quant: bool = False
block_shape: Optional[list[int]] = None
# TODO: add col major flag?
# add detailed quant info for input, intermediates, weights, etc?
def __post_init__(self):
assert (not self.per_act_token_quant
or self.block_shape is None), "illegal quantization"
@property
def is_quantized(self) -> bool:
return self.quant_dtype is not None
@property
def is_per_act_token(self) -> bool:
return self.per_act_token_quant
@property
def is_block_quantized(self) -> bool:
return self.block_shape is not None
@property
def is_per_tensor(self) -> bool:
return not self.per_act_token_quant and self.block_shape is None
def scale_shape(
self,
max_tokens: int,
hidden_dim: int,
) -> Optional[tuple[int, int]]:
if self.is_quantized:
if self.is_block_quantized:
assert self.block_shape is not None
_, block_k = self.block_shape
k_tiles = cdiv(hidden_dim, block_k)
return (max_tokens, k_tiles)
elif self.is_per_act_token:
return (max_tokens, 1)
else:
return (1, 1)
else:
return None
def batched_scale_shape(
self,
num_experts: int,
max_tokens: int,
hidden_dim: int,
) -> Optional[tuple[int, int, int]]:
if self.is_quantized:
scale_shape = self.scale_shape(max_tokens, hidden_dim)
assert scale_shape is not None
return (num_experts, *scale_shape)
else:
return None
@staticmethod
def make(
use_fp8_w8a8: bool = False,
use_int8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
use_int4_w4a8: bool = False,
per_act_token_quant: bool = False,
per_out_ch_quant: bool = False,
block_shape: Optional[list[int]] = None,
) -> "FusedMoEQuantConfig":
assert sum([
int(flag) for flag in [
use_fp8_w8a8,
use_int8_w8a8,
use_int8_w8a16,
use_int4_w4a16,
use_int4_w4a8,
]
]) <= 1, "Quantization flags are mutually exclusive."
quant_dtype = get_config_quant_dtype(
use_fp8_w8a8=use_fp8_w8a8,
use_int8_w8a8=use_int8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
use_int4_w4a8=use_int4_w4a8,
)
return FusedMoEQuantConfig(
quant_dtype,
per_act_token_quant,
per_out_ch_quant,
block_shape,
)
@dataclass
class FusedMoEParallelConfig:
tp_size: int
dp_size: int
ep_size: int
tp_rank: int
dp_rank: int
ep_rank: int
use_ep: bool # whether to use EP or not
@property
def use_all2all_kernels(self):
return self.dp_size > 1 and self.use_ep
@property
def use_pplx_kernels(self):
return (self.use_all2all_kernels
and envs.VLLM_ALL2ALL_BACKEND == "pplx")
@property
def use_deepep_ht_kernels(self):
return (self.use_all2all_kernels
and envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput")
@property
def use_deepep_ll_kernels(self):
return (self.use_all2all_kernels
and envs.VLLM_ALL2ALL_BACKEND == "deepep_low_latency")
@staticmethod
def make(tp_size_: int, dp_size_: int,
vllm_parallel_config: ParallelConfig) -> "FusedMoEParallelConfig":
"""
Determine MoE parallel configuration. Based on the input tp_size_,
dp_size_, ep_size_ and vllm's parallel config, determine what
level's of parallelism to use in the fused moe layer.
Args:
tp_size_ (int): tp_size passed into the FusedMoE constructor.
dp_size_ (int): dp_size passed into the FusedMoE constructor.
ep_size_ (int): ep_size passed into the FusedMoE constructor.
vllm_parallel_config (ParallelConfig): vllm's parallel config
object.
Examples:
When there is no parallelism requested, i.e. tp_size_ = dp_size_ = 1,
we simply return the sizes unaltered and the ranks set to 0.
Expert Parallelism is considered only when either dp_size_ or tp_size_
is non trivial.
When TP = 2, DP = 1 and EP = False, the configuration on different
devices,
- device 0 : TP = {2, 0} DP = {1, 0} EP = {1, 0} //
legend : {size, rank}
- device 1 : TP = {2, 1} DP = {1, 0} EP = {1, 0}
- Comment : Tensors are sharded across 2 devices.
When TP = 1, DP = 2 and EP = False, the configuration on different
devices,
- device 0 : TP = {2, 0} DP = {2, 0} EP = {1, 0}
- device 1 : TP = {2, 1} DP = {2, 1} EP = {1, 0}
- Comment: There are 2 engine instances and the tensors are sharded
across 2 decvices.
When TP = 2, DP = 2 and EP = False, the configuration on different
devices,
- device 0: TP = {4, 0} DP = {2, 0} EP = {1, 0}
- device 1: TP = {4, 1} DP = {2, 0} EP = {1, 0}
- device 2: TP = {4, 2} DP = {2, 1} EP = {1, 0}
- device 3: TP = {4, 3} DP = {2, 1} EP = {1, 0}
- Comment: There are 2 engine instances and the tensors are sharded
across 4 devices.
When, TP = 2, DP = 1 and EP = True, the configuration on different
devices,
- device 0: TP = {1, 0} DP = {1, 0} EP = {2, 0}
- device 1: TP = {1, 0} DP = {1, 0} EP = {2, 1}
- Comment: The experts are split between the 2 devices.
When, TP = 1, DP = 2 and EP = True, the configuration on different
devices,
- device 0: TP = {1, 0} DP = {2, 0} EP = {2, 0}
- device 1: TP = {1, 0} DP = {2, 1} EP = {2, 1}
- Comment: There are 2 engine instances and the experts are split
between the 2 devices.
When TP = 2, DP = 2 and EP = True, the configuration on different
devices,
- device 0: TP = {1, 0} DP = {2, 0} EP = {4, 0}
- device 1: TP = {1, 0} DP = {2, 0} EP = {4, 1}
- device 2: TP = {1, 0} DP = {2, 1} EP = {4, 2}
- device 3: TP = {1, 0} DP = {2, 1} EP = {4, 3}
- Comment: There are 2 engine instances and the experts are split
between the 4 devices.
"""
def flatten_tp_across_dp(dp_rank: int):
tp_rank = 0 if tp_size_ == 1 else get_tensor_model_parallel_rank()
# There are actually dp_size_ * tp_size_ devices. Update tp_size
# and tp_rank so we shard across all devices.
tp_size = dp_size_ * tp_size_
tp_rank = dp_rank * tp_size_ + tp_rank
return tp_size, tp_rank
use_ep = (dp_size_ * tp_size_ > 1
and vllm_parallel_config.enable_expert_parallel)
dp_size = dp_size_
dp_rank = get_dp_group().rank_in_group if dp_size > 1 else 0
tp_size, tp_rank = flatten_tp_across_dp(dp_rank)
if not use_ep:
return FusedMoEParallelConfig(tp_size=tp_size,
tp_rank=tp_rank,
dp_size=dp_size,
dp_rank=dp_rank,
ep_size=1,
ep_rank=0,
use_ep=False)
# DP + EP / TP + EP / DP + TP + EP
assert use_ep
# In EP, each device owns a set of experts fully. There is no tensor
# parallel update tp_size, tp_rank, ep_size and ep_rank to reflect that.
ep_size = tp_size
ep_rank = tp_rank
return FusedMoEParallelConfig(tp_size=1,
tp_rank=0,
dp_size=dp_size,
dp_rank=dp_rank,
ep_size=ep_size,
ep_rank=ep_rank,
use_ep=True)
# Adapted from pplx-kernels tests/all_to_all_utils.py
@dataclass
class FusedMoEConfig:
num_experts: int
experts_per_token: int
hidden_dim: int
num_local_experts: int
moe_parallel_config: FusedMoEParallelConfig
# The activation type.
in_dtype: torch.dtype
quant_config: Optional[FusedMoEQuantConfig] = None
max_num_tokens: int = envs.VLLM_MOE_DP_CHUNK_SIZE
def __post_init__(self):
if self.dp_size > 1:
logger.debug("Using FusedMoEConfig::max_num_tokens=%d",
self.max_num_tokens)
assert self.max_num_tokens > 0
@property
def quant_dtype(self) -> Optional[torch.dtype]:
if self.quant_config is not None:
return self.quant_config.quant_dtype
else:
return None
@property
def block_shape(self) -> Optional[list[int]]:
if self.quant_config is not None:
return self.quant_config.block_shape
else:
return None
@property
def per_act_token_quant(self) -> bool:
if self.quant_config is not None:
return self.quant_config.per_act_token_quant
else:
return False
@property
def per_out_ch_quant(self) -> bool:
if self.quant_config is not None:
return self.quant_config.per_out_ch_quant
else:
return False
@property
def tp_size(self):
return self.moe_parallel_config.tp_size
@property
def dp_size(self):
return self.moe_parallel_config.dp_size
@property
def ep_size(self):
return self.moe_parallel_config.ep_size
@property
def tp_rank(self):
return self.moe_parallel_config.tp_rank
@property
def dp_rank(self):
return self.moe_parallel_config.dp_rank
@property
def ep_rank(self):
return self.moe_parallel_config.ep_rank
@property
def use_ep(self):
return self.moe_parallel_config.use_ep
@property
def use_pplx_kernels(self):
return self.moe_parallel_config.use_pplx_kernels
@property
def use_deepep_ht_kernels(self):
return self.moe_parallel_config.use_deepep_ht_kernels
@property
def use_deepep_ll_kernels(self):
return self.moe_parallel_config.use_deepep_ll_kernels
@staticmethod
def make(
num_experts: int,
experts_per_token: int,
hidden_dim: int,
num_local_experts: int,
moe_parallel_config: FusedMoEParallelConfig,
in_dtype: torch.dtype,
max_num_tokens: int = envs.VLLM_MOE_DP_CHUNK_SIZE,
quant_config: Optional[Union[FusedMoEQuantConfig,
QuantizationConfig]] = None
) -> "FusedMoEConfig":
_quant_config: Optional[FusedMoEQuantConfig] = None
if quant_config is not None and isinstance(quant_config,
QuantizationConfig):
if hasattr(quant_config, 'weight_block_size'):
block_shape = quant_config.weight_block_size
else:
block_shape = None
per_act_token_quant = False
per_out_ch_quant = False
quant_dtype: Optional[torch.dtype] = None
input_quant = get_quant_config_input_quant(quant_config)
weight_quant = get_quant_config_weight_quant(quant_config)
if input_quant is not None:
per_act_token_quant = (input_quant.strategy
== QuantizationStrategy.TOKEN
if input_quant is not None else False)
if input_quant.num_bits == 8:
if input_quant.type == QuantizationType.FLOAT:
quant_dtype = torch.float8_e4m3fn
elif input_quant.type == QuantizationType.INT:
quant_dtype = torch.int8
from vllm.model_executor.layers.quantization.fp8 import Fp8Config
if quant_dtype is None and isinstance(quant_config, Fp8Config):
quant_dtype = torch.float8_e4m3fn
if weight_quant is not None:
per_out_ch_quant = (
weight_quant.strategy == QuantizationStrategy.CHANNEL)
if quant_dtype is not None:
_quant_config = FusedMoEQuantConfig(
quant_dtype=quant_dtype,
per_act_token_quant=per_act_token_quant,
per_out_ch_quant=per_out_ch_quant,
block_shape=block_shape,
)
else:
_quant_config = FusedMoEQuantConfig()
logger.warning_once("MoE DP setup unable to determine "
"quantization scheme or unsupported "
"quantization type. This model will "
"not run with DP enabled.")
else:
_quant_config = quant_config
return FusedMoEConfig(
num_experts=num_experts,
experts_per_token=experts_per_token,
hidden_dim=hidden_dim,
num_local_experts=num_local_experts,
moe_parallel_config=moe_parallel_config,
in_dtype=in_dtype,
quant_config=_quant_config,
max_num_tokens=max_num_tokens,
)

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@@ -0,0 +1,146 @@
{
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View File

@@ -0,0 +1,146 @@
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View File

@@ -0,0 +1,218 @@
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View File

@@ -0,0 +1,218 @@
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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

@@ -0,0 +1,164 @@
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View File

@@ -0,0 +1,164 @@
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View File

@@ -0,0 +1,164 @@
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View File

@@ -0,0 +1,164 @@
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View File

@@ -0,0 +1,164 @@
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View File

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View File

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View File

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View File

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View File

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View File

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View File

@@ -0,0 +1,164 @@
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View File

@@ -0,0 +1,164 @@
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View File

@@ -0,0 +1,146 @@
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View File

@@ -0,0 +1,146 @@
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View File

@@ -0,0 +1,146 @@
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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

@@ -0,0 +1,146 @@
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View File

@@ -0,0 +1,146 @@
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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

@@ -0,0 +1,164 @@
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View File

@@ -0,0 +1,147 @@
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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

@@ -0,0 +1,146 @@
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View File

@@ -0,0 +1,146 @@
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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

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View File

@@ -0,0 +1,164 @@
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View File

@@ -0,0 +1,164 @@
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View File

@@ -0,0 +1,164 @@
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View File

@@ -0,0 +1,164 @@
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View File

@@ -0,0 +1,200 @@
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View File

@@ -0,0 +1,200 @@
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View File

@@ -0,0 +1,173 @@
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View File

@@ -0,0 +1,164 @@
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View File

@@ -0,0 +1,182 @@
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View File

@@ -0,0 +1,182 @@
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View File

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View File

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View File

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View File

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View File

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View File

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View File

@@ -0,0 +1,146 @@
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View File

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View File

@@ -0,0 +1,200 @@
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View File

@@ -0,0 +1,200 @@
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View File

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}
}

View File

@@ -0,0 +1,146 @@
{
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}
}

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