mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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215 lines
7.7 KiB
Python
215 lines
7.7 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Optional
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import paddle
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from fastdeploy.model_executor.layers.attention.ops import (
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append_attention, get_block_shape_and_split_kv_block)
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if TYPE_CHECKING:
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from paddle._typing.dtype_like import _DTypeLiteral
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from fastdeploy.model_executor.layers.attention import Attention
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from fastdeploy.model_executor.layers.attention.base_attention_backend import \
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AttentionBackend
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from fastdeploy.worker.model_runner import ForwardMeta
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@dataclass
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class AppendAttentionMetadata:
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"""
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AppendAttentionMetadata
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"""
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max_len_kv: paddle.Tensor = None
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set_max_lengths: int = -1
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encoder_batch_ids: paddle.Tensor = None
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encoder_tile_ids_per_batch: paddle.Tensor = None
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encoder_num_blocks: paddle.Tensor = None
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kv_batch_ids: paddle.Tensor = None
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kv_tile_ids_per_batch: paddle.Tensor = None
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kv_num_blocks: paddle.Tensor = None
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decoder_batch_ids: paddle.Tensor = None
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decoder_tile_ids_per_batch: paddle.Tensor = None
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decoder_num_blocks: paddle.Tensor = None
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_dtype: _DTypeLiteral = paddle.bfloat16
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encoder_max_partition_size: int = 32768
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max_partition_size: int = 32768
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block_tables: Optional[paddle.Tensor] = None
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rotary_embs: Optional[paddle.Tensor] = None
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attn_mask: Optional[paddle.Tensor] = None
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encoder_block_shape_q: Optional[paddle.Tensor] = None
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decoder_block_shape_q: Optional[paddle.Tensor] = None
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_fuse_kernel_compute_dtype: str = "bf16"
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class AppendAttentionBackend(AttentionBackend):
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"""
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AppendAttentionBackend backend implementation.
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"""
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def __init__(
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self,
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model_runner: "ModelRunner",
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):
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"""
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AppendAttentionBackend __init__
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"""
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super().__init__()
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self.attention_metadata: AppendAttentionMetadata = None
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self.block_size = model_runner.args.block_size
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self.max_seq_len = model_runner.args.max_model_len
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self.rope_theta = (10000.0 if model_runner.model_cfg.rope_theta is None
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else model_runner.model_cfg.rope_theta)
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self.rope_3d = getattr(model_runner.model_cfg, "rope_3d", False)
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self.causal = getattr(model_runner.model_cfg, "causal", True)
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self.speculate_method = model_runner.args.speculate_method
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self.speculate_max_draft_token_num = model_runner.args.speculate_max_draft_tokens
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self.num_heads = model_runner.model_cfg.num_attention_heads // model_runner.nranks
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self.kv_num_heads = int(
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model_runner.model_cfg.num_key_value_heads) // model_runner.nranks
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def init_attention_metadata(self, forward_meta: ForwardMeta):
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"""Initialize attntion metadata hence all layers in the forward pass can reuse it."""
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metadata = AppendAttentionMetadata()
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metadata.encoder_block_shape_q = 64
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metadata.decoder_block_shape_q = 16
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metadata.max_partition_size = 32768
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metadata.encoder_max_partition_size = 32768
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metadata._dtype = paddle.get_default_dtype()
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if metadata._dtype == "bfloat16":
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metadata._fuse_kernel_compute_dtype = "bf16"
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elif metadata._dtype == "float16":
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metadata._fuse_kernel_compute_dtype = "fp16"
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elif metadata._dtype == "float32":
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metadata._fuse_kernel_compute_dtype = "fp32"
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metadata.block_tables = forward_meta.block_tables
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metadata.rotary_embs = forward_meta.rotary_embs
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metadata.attn_mask = forward_meta.attn_mask
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metadata.pre_caches_length = forward_meta.pre_caches_length
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(
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metadata.encoder_batch_ids,
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metadata.encoder_tile_ids_per_batch,
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metadata.encoder_num_blocks,
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metadata.kv_batch_ids,
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metadata.kv_tile_ids_per_batch,
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metadata.kv_num_blocks,
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metadata.decoder_batch_ids,
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metadata.decoder_tile_ids_per_batch,
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metadata.decoder_num_blocks,
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metadata.max_len_kv,
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metadata.set_max_lengths,
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) = get_block_shape_and_split_kv_block(
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forward_meta.seq_lens_encoder,
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forward_meta.seq_lens_decoder,
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forward_meta.seq_lens_this_time,
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forward_meta.cum_offsets,
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metadata.encoder_block_shape_q,
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metadata.decoder_block_shape_q,
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self.num_heads // self.kv_num_heads,
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self.block_size,
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self.speculate_max_draft_token_num + 1,
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)
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self.attention_metadata = metadata
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def get_attntion_meta(self):
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"""get_attntion_meta"""
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return self.attention_metadata
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@staticmethod
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def get_kv_cache_shape(
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max_num_blocks: int,
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block_size: int,
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kv_num_head: int,
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head_dim: int,
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):
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"""
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get_kv_cache_shape
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"""
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return (max_num_blocks, kv_num_head, block_size, head_dim)
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def forward_mixed(
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self,
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q,
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k,
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v,
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qkv,
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layer: Attention,
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forward_meta: ForwardMeta,
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):
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"""
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forward_mixed
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"""
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metadata = self.attention_metadata
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res = append_attention(
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qkv,
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forward_meta.caches[2 * layer.layer_id],
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forward_meta.caches[2 * layer.layer_id + 1],
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forward_meta.seq_lens_encoder,
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forward_meta.seq_lens_decoder,
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forward_meta.seq_lens_this_time,
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forward_meta.padding_offset,
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forward_meta.cum_offsets,
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metadata.block_tables,
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metadata.encoder_batch_ids,
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metadata.encoder_tile_ids_per_batch,
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metadata.encoder_num_blocks,
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metadata.kv_batch_ids,
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metadata.kv_tile_ids_per_batch,
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metadata.kv_num_blocks,
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metadata.decoder_batch_ids,
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metadata.decoder_tile_ids_per_batch,
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metadata.decoder_num_blocks,
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metadata.set_max_lengths,
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metadata.max_len_kv,
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metadata.rotary_embs,
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metadata.attn_mask,
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layer.qkv_bias,
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layer.qkv_scale,
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getattr(layer, "cache_k_scale", None),
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getattr(layer, "cache_v_scale", None),
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getattr(layer, "cache_k_out_scale", None),
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getattr(layer, "cache_v_out_scale", None),
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getattr(layer, "cache_k_zp", None),
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getattr(layer, "cache_v_zp", None),
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layer.linear_shift,
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layer.linear_smooth,
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None, # kv_signal_data,
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metadata._fuse_kernel_compute_dtype,
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getattr(layer, "cache_quant_type_str", "none"),
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layer.use_neox_rotary_style,
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self.rope_3d,
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self.max_seq_len,
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getattr(layer, "quant_max_bound", 0.0),
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getattr(layer, "quant_min_bound", 0.0),
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getattr(layer, "out_scale", -1.0),
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metadata.encoder_block_shape_q,
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metadata.decoder_block_shape_q,
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metadata.max_partition_size,
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metadata.encoder_max_partition_size,
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self.speculate_max_draft_token_num + 1,
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self.causal,
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self.speculate_method is not None,
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)[0]
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return res
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