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https://github.com/NVIDIA/TensorRT-LLM.git
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f7eca56161
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com> Co-authored-by: zhang-ge-hao <842720660@qq.com>
1059 lines
45 KiB
Python
1059 lines
45 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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import math
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from typing import List, Optional
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import numpy as np
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import tensorrt as trt
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from .._common import default_net, precision
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from .._utils import numpy_fp32_to_bf16, trt_dtype_to_np
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from ..functional import (AttentionMaskType, PositionEmbeddingType,
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RotaryScalingType, Tensor, bert_attention, cast, clip,
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concat, constant, embedding, expand_dims, expand_mask,
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generate_alibi_biases, generate_alibi_slopes,
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gpt_attention, matmul, repeat_interleave, round,
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shape, slice, softmax, split, view, where)
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from ..module import Module
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from ..parameter import Parameter
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from ..quantization import QuantMode
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from ..quantization.functional import dequantize, quantize
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from ..quantization.layers import FP8Linear, FP8RowLinear
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from .linear import ColumnLinear, RowLinear
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from .lora import Lora
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class RopeEmbeddingUtils:
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@staticmethod
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def create_sinusoidal_positions(num_pos: int,
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dim: int,
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theta: float = 10000.0,
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dtype=np.float32):
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inv_freq = 1.0 / (theta**(np.arange(0, dim, 2) / dim)).astype(dtype)
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sinusoid_inp = np.einsum("i , j -> i j",
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np.arange(num_pos, dtype=dtype),
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inv_freq,
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dtype=dtype)
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concat = np.concatenate((np.sin(sinusoid_inp), np.cos(sinusoid_inp)),
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axis=1)
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return np.expand_dims(concat, axis=0).astype(np.float32)
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@staticmethod
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def rotate_every_two(tensor: Tensor) -> Tensor:
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assert tensor.ndim() == 4
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shape_tensor = concat([
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shape(tensor, i) / 2 if i == (tensor.ndim() -
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1) else shape(tensor, i)
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for i in range(tensor.ndim())
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])
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x1 = slice(tensor, [0, 0, 0, 0], shape_tensor, [1, 1, 1, 2])
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x2 = slice(tensor, [0, 0, 0, 1], shape_tensor, [1, 1, 1, 2])
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x1 = expand_dims(x1, 4)
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x2 = expand_dims(x2, 4)
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zero = constant(
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np.ascontiguousarray(np.zeros([1],
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dtype=trt_dtype_to_np(x2.dtype))))
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x2 = zero - x2
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x = concat([x2, x1], 4)
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return view(
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x, concat([shape(x, 0),
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shape(x, 1),
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shape(x, 2),
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shape(x, 3) * 2]))
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@staticmethod
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def rotate_half(tensor: Tensor) -> Tensor:
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# [bs, num_attention_kv_heads, seqlen, attention_head_size]
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assert tensor.ndim() == 4
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shape_tensor = concat([
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shape(tensor, i) / 2 if i == (tensor.ndim() -
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1) else shape(tensor, i)
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for i in range(tensor.ndim())
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])
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last_dim = shape(tensor, tensor.ndim() - 1) / 2
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x1 = slice(tensor, [0, 0, 0, 0], shape_tensor, [1, 1, 1, 1])
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x2 = slice(tensor, concat([0, 0, 0, last_dim]), shape_tensor,
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[1, 1, 1, 1])
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zero = constant(
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np.ascontiguousarray(np.zeros([1],
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dtype=trt_dtype_to_np(x2.dtype))))
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x2 = zero - x2
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x = concat([x2, x1], 3)
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return x
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@staticmethod
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def apply_rotary_pos_emb(
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tensor: Tensor,
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position_embedding: List[Tensor] = None,
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pos_emb_type: PositionEmbeddingType = PositionEmbeddingType.rope_gptj
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) -> Tensor:
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rotate_func = None
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if pos_emb_type == PositionEmbeddingType.rope_gpt_neox:
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assert len(position_embedding) == 2
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cos, sin = position_embedding
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sin = expand_dims(sin, 2)
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cos = expand_dims(cos, 2)
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sin = concat([sin, sin], 3)
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cos = concat([cos, cos], 3)
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rotate_func = RopeEmbeddingUtils.rotate_half
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elif pos_emb_type == PositionEmbeddingType.rope_gptj:
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assert len(position_embedding) == 2
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cos, sin = position_embedding
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sin = expand_dims(sin, 2)
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cos = expand_dims(cos, 2)
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sin = repeat_interleave(sin, 2, 3)
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cos = repeat_interleave(cos, 2, 3)
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rotate_func = RopeEmbeddingUtils.rotate_every_two
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elif pos_emb_type == PositionEmbeddingType.chatglm:
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assert len(position_embedding) == 4
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cos0, cos1, sin0, sin1 = position_embedding
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if default_net().strongly_typed and tensor.dtype != cos0.dtype:
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tensor = cast(tensor, cos0.dtype)
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shape_tensor = concat([
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shape(tensor, i) / 2 if i == (tensor.ndim() -
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1) else shape(tensor, i)
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for i in range(tensor.ndim())
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])
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last_dim = shape(tensor, tensor.ndim() - 1) / 2
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x_part0 = slice(tensor, [0, 0, 0, 0], shape_tensor, [1, 1, 1, 1])
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x_part1 = slice(tensor, concat([0, 0, 0, last_dim]), shape_tensor,
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[1, 1, 1, 1])
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y_part0 = (x_part0 *
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cos0) + (RopeEmbeddingUtils.rotate_half(x_part0) * sin0)
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y_part1 = (x_part1 *
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cos1) + (RopeEmbeddingUtils.rotate_half(x_part1) * sin1)
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result = concat([y_part0, y_part1], dim=3)
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return result.view(shape(tensor))
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else:
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raise ValueError('The PositionEmbeddingType is not RoPE')
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return (tensor * cos) + (rotate_func(tensor) * sin)
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@staticmethod
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def apply_rotary_pos_emb_chatglm(
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qkv,
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position_embedding,
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num_attention_heads,
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attention_head_size,
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max_position_embeddings,
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rotary_embedding_scale,
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) -> Tensor:
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half_head_size = attention_head_size // 2
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qkv_shape = shape(qkv)
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qkv = qkv.view(
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concat([
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shape(qkv, 0),
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shape(qkv, 1),
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num_attention_heads,
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3,
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attention_head_size,
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]))
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query, key, value = split(qkv, 1, dim=3)
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q_shape = concat([
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shape(qkv, 0),
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shape(qkv, 1),
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num_attention_heads,
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attention_head_size,
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])
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query = query.view(q_shape)
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key = key.view(q_shape)
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value = value.view(q_shape)
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embedding_weight = RopeEmbeddingUtils.create_sinusoidal_positions(
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max_position_embeddings, half_head_size)
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embedding_weight /= rotary_embedding_scale
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embedding_weight = np.split(embedding_weight.squeeze(0), 2, axis=1)
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embedding_weight = np.concatenate(
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[
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embedding_weight[0],
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embedding_weight[0],
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embedding_weight[1],
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embedding_weight[1],
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],
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axis=1,
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)
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embedding_weight = constant(embedding_weight)
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position_embedding = embedding(position_embedding, embedding_weight)
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position_embedding, block_embedding = split(
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position_embedding,
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1,
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dim=1,
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)
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sin0, cos0 = split(position_embedding, half_head_size, dim=3)
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sin1, cos1 = split(block_embedding, half_head_size, dim=3)
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new_shape = concat([
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shape(qkv, 0),
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shape(qkv, 1),
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1,
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half_head_size,
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])
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position_embedding = [
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tensor.view(new_shape) for tensor in [cos0, cos1, sin0, sin1]
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]
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query = RopeEmbeddingUtils.apply_rotary_pos_emb(
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tensor=query,
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position_embedding=position_embedding,
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pos_emb_type=PositionEmbeddingType.chatglm)
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key = RopeEmbeddingUtils.apply_rotary_pos_emb(
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tensor=key,
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position_embedding=position_embedding,
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pos_emb_type=PositionEmbeddingType.chatglm)
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if default_net().strongly_typed:
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if query.dtype != value.dtype:
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query = cast(query, value.dtype)
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if key.dtype != value.dtype:
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key = cast(key, value.dtype)
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qkv = concat([query, key, value], dim=2)
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qkv = qkv.view(qkv_shape)
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return qkv
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class AttentionParams(object):
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def __init__(self,
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sequence_length: Tensor = None,
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context_lengths: Tensor = None,
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host_context_lengths: Tensor = None,
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max_context_length: int = None,
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host_request_types: Tensor = None,
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encoder_input_lengths: Tensor = None,
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encoder_max_input_length: Tensor = None):
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self.sequence_length = sequence_length
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self.context_lengths = context_lengths
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self.host_context_lengths = host_context_lengths
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# max allowed context length. Required to
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# compute scratch memory size.
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self.max_context_length = max_context_length
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self.host_request_types = host_request_types
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self.encoder_input_lengths = encoder_input_lengths
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self.encoder_max_input_length = encoder_max_input_length
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def is_valid_cross_attn(self, do_cross_attention):
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if do_cross_attention:
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if self.encoder_input_lengths is None:
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return False
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if self.encoder_max_input_length is None:
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return False
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return True
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def is_valid(self, gpt_attention_plugin, remove_input_padding):
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if gpt_attention_plugin:
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if self.sequence_length is None:
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return False
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if self.context_lengths is None:
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return False
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if self.host_request_types is None:
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return False
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if self.max_context_length is None:
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return False
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if remove_input_padding:
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if self.host_context_lengths is None:
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return False
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if not gpt_attention_plugin:
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return False
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return True
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class KeyValueCacheParams:
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def __init__(self,
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past_key_value: List[Tensor] = None,
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host_past_key_value_lengths: Tensor = None,
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host_max_attention_window_sizes: List[Tensor] = None,
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kv_cache_block_pointers: List[Tensor] = None,
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host_kv_cache_block_pointers: List[Tensor] = None,
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cache_indirection: Tensor = None,
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past_key_value_length: Tensor = None):
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self.past_key_value = past_key_value
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self.host_past_key_value_lengths = host_past_key_value_lengths
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self.host_max_attention_window_sizes = host_max_attention_window_sizes
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self.kv_cache_block_pointers = kv_cache_block_pointers
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self.host_kv_cache_block_pointers = host_kv_cache_block_pointers
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self.cache_indirection = cache_indirection
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# self.past_key_value_length = past_key_value_length
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def get_first_past_key_value(self):
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if self.past_key_value is None:
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return None
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return self.past_key_value[0]
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def get_first_kv_cache_block_pointers(self):
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if self.kv_cache_block_pointers is None:
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return None
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return self.kv_cache_block_pointers[0]
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def get_first_host_kv_cache_block_pointers(self):
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if self.host_kv_cache_block_pointers is None:
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return None
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return self.host_kv_cache_block_pointers[0]
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def fill_none_tensor_list(self, list_size):
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if self.past_key_value is None:
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self.past_key_value = tuple([None] * list_size)
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if self.host_max_attention_window_sizes is None:
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self.host_max_attention_window_sizes = tuple([None] * list_size)
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def is_valid(self, gpt_attention_plugin):
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if gpt_attention_plugin:
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if self.host_past_key_value_lengths is None:
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return False
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if self.host_max_attention_window_sizes is None:
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return False
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if self.cache_indirection is None:
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return False
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return True
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class Attention(Module):
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def __init__(
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self,
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hidden_size,
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num_attention_heads,
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num_kv_heads=None,
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max_position_embeddings=1024,
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num_layers=1,
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apply_query_key_layer_scaling=False,
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attention_head_size=None,
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attention_mask_type=AttentionMaskType.padding,
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bias=True,
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dtype=None,
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position_embedding_type=PositionEmbeddingType.learned_absolute,
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rotary_embedding_base=10000.0,
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rotary_embedding_scaling=None,
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rotary_embedding_percentage=1.0,
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tp_group=None,
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tp_size=1,
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tp_rank=0,
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quant_mode: QuantMode = QuantMode(0),
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q_scaling=1.0,
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cross_attention=False,
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relative_attention=False,
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max_distance=0,
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num_buckets=0,
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instance_id: int = 0,
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dense_bias=None,
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):
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super().__init__()
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self.cross_attention = cross_attention
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self.attention_mask_type = attention_mask_type
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self.attention_head_size = hidden_size // num_attention_heads if attention_head_size is None else attention_head_size
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assert num_attention_heads % tp_size == 0, \
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"num_attention_heads must be divisible by tp_size"
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self.num_attention_heads = num_attention_heads // tp_size
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self.num_attention_kv_heads = (
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num_kv_heads + tp_size - 1
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) // tp_size if num_kv_heads is not None else self.num_attention_heads
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self.hidden_size = hidden_size // tp_size
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self.max_position_embeddings = max_position_embeddings
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self.bias = bias
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self.tp_size = tp_size
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self.tp_rank = tp_rank
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self.dtype = dtype
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if dense_bias is None:
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dense_bias = bias
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self.num_layers = num_layers
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self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
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self.norm_factor = math.sqrt(self.attention_head_size)
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self.q_scaling = q_scaling
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if self.apply_query_key_layer_scaling:
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self.norm_factor *= self.num_layers
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self.q_scaling *= self.num_layers
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# Whether to scale ALiBi bias. Mathematically, it's equivalent to
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# normalizing QK after adding bias.
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# - False, inv_sqrt_Dh * Q*K^T + alibi_bias
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# - True, inv_sqrt_Dh * Q*K^T + inv_sqrt_Dh * alibi_bias
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self.scale_alibi_bias = position_embedding_type == PositionEmbeddingType.alibi_with_scale
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self.position_embedding_type = position_embedding_type
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self.relative_attention = relative_attention
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self.max_distance = max_distance
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self.rotary_embedding_base = rotary_embedding_base
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self.rotary_embedding_scale_type = RotaryScalingType.none
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self.rotary_embedding_scale = 1.0
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if rotary_embedding_scaling is not None:
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assert rotary_embedding_scaling["type"] in ["linear", "dynamic"]
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self.rotary_embedding_scale_type = RotaryScalingType.linear if rotary_embedding_scaling[
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"type"] == "linear" else RotaryScalingType.dynamic
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self.rotary_embedding_scale = rotary_embedding_scaling["factor"]
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assert self.rotary_embedding_scale > 1.0
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self.embed_positions = None
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self.rotary_enabled = False
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self.rotary_embedding_dim = 0
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if self.position_embedding_type.is_rope():
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self.rotary_embedding_dim = int(self.attention_head_size *
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rotary_embedding_percentage)
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self.rotary_enabled = True
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self.embed_positions = RopeEmbeddingUtils.create_sinusoidal_positions(
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self.max_position_embeddings,
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self.rotary_embedding_dim,
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)
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self.quant_mode = quant_mode
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self.use_int8_kv_cache = self.quant_mode.has_int8_kv_cache()
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if self.quant_mode.has_kv_cache_quant():
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self.kv_orig_quant_scale = Parameter(shape=(1, ), dtype='float32')
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self.kv_quant_orig_scale = Parameter(shape=(1, ), dtype='float32')
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else:
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self.register_parameter('kv_orig_quant_scale', None)
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self.register_parameter('kv_quant_orig_scale', None)
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# The output feature size is therefore (h/tp + 2*kvh/tp) * d, where h is num_heads,
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# d is head_size, kvh is the num_kv_heads and tp is tensor_parallel_size.
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# In ColumnLinear op, the output dim is calculated by (h + 2*kvh) * d / tp,
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# which matches the desired output size (h/tp + 2*kvh/tp) * d after splitting
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self.use_fp8_qdq = self.quant_mode.has_fp8_qdq()
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if self.use_fp8_qdq:
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self.qkv = FP8Linear(
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hidden_size,
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tp_size * self.num_attention_heads * self.attention_head_size +
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(2 * tp_size * self.num_attention_kv_heads *
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self.attention_head_size),
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bias=bias,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size,
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gather_output=False)
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self.dense = FP8RowLinear(hidden_size,
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hidden_size,
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bias=dense_bias,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size,
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instance_id=instance_id)
|
|
else:
|
|
# out dim is not necessarily hidden_size + kv specific size (in MQA/GQA), but num_heads * heads_size
|
|
# example: d_model != num_heads * head_size in Flan-T5
|
|
self.qkv = ColumnLinear(
|
|
hidden_size,
|
|
tp_size * self.num_attention_heads * self.attention_head_size +
|
|
(2 * tp_size * self.num_attention_kv_heads *
|
|
self.attention_head_size),
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
gather_output=False)
|
|
self.dense = RowLinear(tp_size * self.num_attention_heads *
|
|
self.attention_head_size,
|
|
hidden_size,
|
|
bias=dense_bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
instance_id=instance_id)
|
|
|
|
# per-layer relative attention table
|
|
if relative_attention:
|
|
self.rel_attn_table = Parameter(shape=(num_attention_heads //
|
|
tp_size, num_buckets),
|
|
dtype=dtype)
|
|
|
|
self.q_lora = Lora(
|
|
in_hidden_size=hidden_size,
|
|
out_hidden_size=self.num_attention_heads * self.attention_head_size,
|
|
max_low_rank=min(
|
|
hidden_size,
|
|
self.num_attention_heads * self.attention_head_size),
|
|
)
|
|
|
|
self.k_lora = Lora(
|
|
in_hidden_size=hidden_size,
|
|
out_hidden_size=self.num_attention_kv_heads *
|
|
self.attention_head_size,
|
|
max_low_rank=min(
|
|
hidden_size,
|
|
self.num_attention_kv_heads * self.attention_head_size),
|
|
)
|
|
|
|
self.v_lora = Lora(
|
|
in_hidden_size=hidden_size,
|
|
out_hidden_size=self.num_attention_kv_heads *
|
|
self.attention_head_size,
|
|
max_low_rank=min(
|
|
hidden_size,
|
|
self.num_attention_kv_heads * self.attention_head_size),
|
|
)
|
|
|
|
def forward(self,
|
|
hidden_states: Tensor,
|
|
attention_mask=None,
|
|
use_cache=False,
|
|
kv_cache_params=None,
|
|
attention_params=None,
|
|
encoder_output: Optional[Tensor] = None,
|
|
workspace=None,
|
|
position_embedding=None,
|
|
norm_before_bmm1=False,
|
|
lora_param=None):
|
|
|
|
assert isinstance(hidden_states, Tensor)
|
|
|
|
alibi_slopes = None
|
|
if self.position_embedding_type.is_alibi():
|
|
dtype = trt.float32
|
|
if default_net().plugin_config.gpt_attention_plugin:
|
|
dtype = hidden_states.dtype
|
|
alibi_scale = 1. / self.norm_factor if self.scale_alibi_bias else 1.
|
|
alibi_slopes = generate_alibi_slopes(self.num_attention_heads *
|
|
self.tp_size,
|
|
dtype=dtype,
|
|
tp_size=self.tp_size,
|
|
tp_rank=self.tp_rank,
|
|
alibi_scale=alibi_scale)
|
|
|
|
qkv_lora_param = None
|
|
if lora_param is not None:
|
|
qkv_lora_param = lora_param.get_runtime_params(0, "attn_qkv")
|
|
qkv = self.qkv(hidden_states, qkv_lora_param)
|
|
|
|
if default_net().plugin_config.lora_plugin and lora_param is not None:
|
|
q_lora_param = lora_param.get_runtime_params(0, "attn_q")
|
|
k_lora_param = lora_param.get_runtime_params(0, "attn_k")
|
|
v_lora_param = lora_param.get_runtime_params(0, "attn_v")
|
|
|
|
assert (q_lora_param is not None and k_lora_param is not None and v_lora_param is not None) or \
|
|
(q_lora_param is None and k_lora_param is None and v_lora_param is None), "q_lora_param, k_lora_param and v_lora_param should be all enabled or all disabled at the same time."
|
|
if q_lora_param is not None and k_lora_param is not None and v_lora_param is not None:
|
|
assert qkv_lora_param is None, "Cannot enable qkv_lora_param and split q_lora_parm, k_lora_param, v_lora_param at the same time."
|
|
q_lora = self.q_lora(hidden_states,
|
|
lora_runtime_param=q_lora_param)
|
|
k_lora = self.k_lora(hidden_states,
|
|
lora_runtime_param=k_lora_param)
|
|
v_lora = self.v_lora(hidden_states,
|
|
lora_runtime_param=v_lora_param)
|
|
qkv_lora = concat([q_lora, k_lora, v_lora], dim=2)
|
|
qkv = qkv + qkv_lora
|
|
|
|
if self.position_embedding_type == PositionEmbeddingType.chatglm:
|
|
qkv = RopeEmbeddingUtils.apply_rotary_pos_emb_chatglm(
|
|
qkv,
|
|
position_embedding,
|
|
self.num_attention_heads,
|
|
self.attention_head_size,
|
|
self.max_position_embeddings,
|
|
self.rotary_embedding_scale,
|
|
)
|
|
self.rotary_embedding_scale_type = RotaryScalingType.none
|
|
self.rotary_embedding_scale = 1.0
|
|
|
|
paged_kv_cache = default_net().plugin_config.paged_kv_cache
|
|
|
|
assert attention_params is None or attention_params.is_valid(
|
|
default_net().plugin_config.gpt_attention_plugin,
|
|
default_net().plugin_config.remove_input_padding)
|
|
assert kv_cache_params is None or kv_cache_params.is_valid(
|
|
default_net().plugin_config.gpt_attention_plugin)
|
|
|
|
past_key_value = None if kv_cache_params is None else kv_cache_params.get_first_past_key_value(
|
|
)
|
|
if self.cross_attention and (past_key_value is not None):
|
|
past_key_value = kv_cache_params.past_key_value[1]
|
|
|
|
# if cross attention, cross QKV only needs to be calculated once in the
|
|
# 1st decoding step --> write to cross KV cache --> remains constant
|
|
# during the entire decoding. 1st and >1 steps are distinguished by
|
|
# whether past_key_value exists or not
|
|
# also, cross KV cache max length is set from encoder output seqlen,
|
|
# this maps to the max context length concept in decoder-only models
|
|
cross_qkv = None
|
|
# get length data in every run
|
|
if encoder_output:
|
|
assert isinstance(encoder_output, Tensor)
|
|
# but only do projection once at 1st decoding step
|
|
if self.cross_attention and encoder_output:
|
|
cross_qkv = self.qkv(encoder_output)
|
|
|
|
if default_net().plugin_config.gpt_attention_plugin:
|
|
assert self.attention_mask_type in [
|
|
AttentionMaskType.causal, AttentionMaskType.bidirectional,
|
|
AttentionMaskType.bidirectionalglm
|
|
], 'Plugin only support masked MHA.'
|
|
kv_orig_quant_scale = self.kv_orig_quant_scale.value if self.quant_mode.has_kv_cache_quant(
|
|
) else None
|
|
kv_quant_orig_scale = self.kv_quant_orig_scale.value if self.quant_mode.has_kv_cache_quant(
|
|
) else None
|
|
context, past_key_value = gpt_attention(
|
|
qkv=qkv,
|
|
past_key_value=past_key_value,
|
|
sequence_length=attention_params.sequence_length,
|
|
host_past_key_value_lengths=kv_cache_params.
|
|
host_past_key_value_lengths,
|
|
host_max_attention_window_sizes=kv_cache_params.
|
|
host_max_attention_window_sizes,
|
|
context_lengths=attention_params.context_lengths,
|
|
cache_indirection=kv_cache_params.cache_indirection,
|
|
host_request_types=attention_params.host_request_types,
|
|
num_heads=self.num_attention_heads,
|
|
num_kv_heads=self.num_attention_kv_heads,
|
|
hidden_size_per_head=self.attention_head_size,
|
|
q_scaling=self.q_scaling,
|
|
rotary_embedding_dim=self.rotary_embedding_dim,
|
|
rotary_embedding_base=self.rotary_embedding_base,
|
|
rotary_embedding_scale_type=self.rotary_embedding_scale_type,
|
|
rotary_embedding_scale=self.rotary_embedding_scale,
|
|
rotary_embedding_max_positions=self.max_position_embeddings,
|
|
position_embedding_type=self.position_embedding_type,
|
|
kv_orig_quant_scale=kv_orig_quant_scale,
|
|
kv_quant_orig_scale=kv_quant_orig_scale,
|
|
kv_cache_quant_mode=self.quant_mode,
|
|
max_context_length=attention_params.max_context_length,
|
|
mask_type=self.attention_mask_type,
|
|
alibi_slopes=alibi_slopes,
|
|
tp_size=self.tp_size,
|
|
tp_rank=self.tp_rank,
|
|
kv_cache_block_pointers=kv_cache_params.
|
|
get_first_kv_cache_block_pointers(),
|
|
host_kv_cache_block_pointers=kv_cache_params.
|
|
get_first_host_kv_cache_block_pointers(),
|
|
do_cross_attention=self.cross_attention,
|
|
cross_qkv=cross_qkv,
|
|
cross_qkv_length=attention_params.encoder_max_input_length,
|
|
encoder_input_lengths=attention_params.encoder_input_lengths,
|
|
relative_attention_bias=self.rel_attn_table.value
|
|
if self.relative_attention else None,
|
|
max_distance=self.max_distance,
|
|
host_context_lengths=attention_params.host_context_lengths,
|
|
use_cache=use_cache,
|
|
)
|
|
|
|
else:
|
|
# plain TensorRT mode
|
|
assert paged_kv_cache == False
|
|
past_key_value = None if kv_cache_params is None else kv_cache_params.get_first_past_key_value(
|
|
)
|
|
|
|
def transpose_for_scores(x,
|
|
rotary: bool = False,
|
|
is_kv: bool = False):
|
|
_num_attention_heads = self.num_attention_kv_heads if is_kv else self.num_attention_heads
|
|
new_x_shape = concat([
|
|
shape(x, 0),
|
|
shape(x, 1), _num_attention_heads, self.attention_head_size
|
|
])
|
|
if rotary:
|
|
return x.view(new_x_shape)
|
|
else:
|
|
return x.view(new_x_shape).permute([0, 2, 1, 3])
|
|
|
|
# qkv after projection is of shape
|
|
# [bs, seqlen, (num_attention_heads + 2 * num_attention_kv_heads), attention_head_size].
|
|
# The projected and split qkv after transpose_for_scores():
|
|
# Q[bs, num_attention_heads, seqlen, attention_head_size]
|
|
# K[bs, num_attention_kv_heads, seqlen, attention_head_size]
|
|
# V[bs, num_attention_kv_heads, seqlen, attention_head_size]
|
|
kv_size = self.attention_head_size * self.num_attention_kv_heads
|
|
query, key, value = split(qkv, [self.hidden_size, kv_size, kv_size],
|
|
dim=2)
|
|
|
|
# in cross attention mode, replace kv by encoder_output
|
|
if self.cross_attention and encoder_output is not None:
|
|
encoder_qkv = self.qkv(encoder_output)
|
|
_, key, value = split(encoder_qkv,
|
|
[self.hidden_size, kv_size, kv_size],
|
|
dim=2)
|
|
|
|
query = transpose_for_scores(query, rotary=self.rotary_enabled)
|
|
key = transpose_for_scores(key,
|
|
is_kv=True,
|
|
rotary=self.rotary_enabled)
|
|
value = transpose_for_scores(value, is_kv=True)
|
|
|
|
if self.rotary_enabled:
|
|
if self.dtype == trt.bfloat16:
|
|
embed_positions = numpy_fp32_to_bf16(
|
|
self.embed_positions.astype(np.float32))
|
|
embed_positions = constant(embed_positions)
|
|
else:
|
|
embed_positions = constant(self.embed_positions)
|
|
|
|
if default_net().strongly_typed and (embed_positions.dtype !=
|
|
value.dtype):
|
|
embed_positions = cast(embed_positions, value.dtype)
|
|
|
|
if self.rotary_embedding_dim is not None:
|
|
# When shape(hidden_states, 1) > 1(Context phase), the embedding start from 0,
|
|
# otherwise (Generation phase) move start to position
|
|
start = where(
|
|
shape(hidden_states, 1) > 1, 0,
|
|
shape(past_key_value, 3))
|
|
size = where(
|
|
shape(hidden_states, 1) > 1, shape(hidden_states, 1), 1)
|
|
sincos = slice(embed_positions, concat([0, start, 0]),
|
|
concat([1, size, self.rotary_embedding_dim]))
|
|
sin, cos = split(sincos,
|
|
self.rotary_embedding_dim // 2,
|
|
dim=-1)
|
|
|
|
key_rot_size = concat([
|
|
shape(key, 0),
|
|
shape(key, 1),
|
|
shape(key, 2), self.rotary_embedding_dim
|
|
])
|
|
query_rot_size = concat([
|
|
shape(query, 0),
|
|
shape(query, 1),
|
|
shape(query, 2), self.rotary_embedding_dim
|
|
])
|
|
remaining = shape(key, 3) - self.rotary_embedding_dim
|
|
key_pass_size = concat([
|
|
shape(key, 0),
|
|
shape(key, 1),
|
|
shape(key, 2), remaining
|
|
])
|
|
query_pass_size = concat([
|
|
shape(query, 0),
|
|
shape(query, 1),
|
|
shape(query, 2), remaining
|
|
])
|
|
k_rot = slice(key, [0, 0, 0, 0], key_rot_size)
|
|
k_pass = slice(key, [0, 0, 0, self.rotary_embedding_dim],
|
|
key_pass_size)
|
|
|
|
q_rot = slice(query, [0, 0, 0, 0], query_rot_size)
|
|
q_pass = slice(query, [0, 0, 0, self.rotary_embedding_dim],
|
|
query_pass_size)
|
|
|
|
k_rot = RopeEmbeddingUtils.apply_rotary_pos_emb(
|
|
k_rot, [cos, sin], self.position_embedding_type)
|
|
q_rot = RopeEmbeddingUtils.apply_rotary_pos_emb(
|
|
q_rot, [cos, sin], self.position_embedding_type)
|
|
|
|
key = concat([k_rot, k_pass], dim=3)
|
|
query = concat([q_rot, q_pass], dim=3)
|
|
else:
|
|
key = RopeEmbeddingUtils.apply_rotary_pos_emb(
|
|
key, [cos, sin], self.position_embedding_type)
|
|
query = RopeEmbeddingUtils.apply_rotary_pos_emb(
|
|
query, [cos, sin], self.position_embedding_type)
|
|
|
|
key = key.permute([0, 2, 1, 3])
|
|
query = query.permute([0, 2, 1, 3])
|
|
|
|
past_key_value = None if kv_cache_params is None else kv_cache_params.get_first_past_key_value(
|
|
)
|
|
if past_key_value is not None:
|
|
|
|
def dequantize_tensor(x, scale):
|
|
# Cast from int8 to dtype
|
|
casted_x = cast(x, self.dtype)
|
|
return casted_x * scale
|
|
|
|
if self.use_int8_kv_cache:
|
|
past_key_value = dequantize_tensor(
|
|
past_key_value, self.kv_quant_orig_scale.value)
|
|
|
|
if self.use_fp8_qdq and self.quant_mode.has_kv_cache_quant():
|
|
past_key_value = dequantize(past_key_value,
|
|
self.kv_quant_orig_scale.value)
|
|
|
|
# past_key_value [bs, 2, num_heads, max_seq_len, head_dim]
|
|
past_key, past_value = split(past_key_value, 1, dim=1)
|
|
|
|
key_shape = concat([
|
|
shape(past_key, 0),
|
|
shape(past_key, 2),
|
|
shape(past_key, 3),
|
|
shape(past_key, 4)
|
|
])
|
|
past_key = past_key.view(key_shape, zero_is_placeholder=False)
|
|
past_value = past_value.view(key_shape,
|
|
zero_is_placeholder=False)
|
|
|
|
key = concat([past_key, key], dim=2).cast(self.dtype)
|
|
value = concat([past_value, value], dim=2).cast(self.dtype)
|
|
|
|
if use_cache:
|
|
key_inflated_shape = concat([
|
|
shape(key, 0), 1,
|
|
shape(key, 1),
|
|
shape(key, 2),
|
|
shape(key, 3)
|
|
])
|
|
inflated_key = key.view(key_inflated_shape,
|
|
zero_is_placeholder=False)
|
|
inflated_value = value.view(key_inflated_shape,
|
|
zero_is_placeholder=False)
|
|
past_key_value = concat([inflated_key, inflated_value], dim=1)
|
|
|
|
if self.use_int8_kv_cache:
|
|
|
|
def quantize_tensor(x, scale):
|
|
scaled = x * scale
|
|
rounded = round(scaled)
|
|
clipped = clip(rounded, -128, 127)
|
|
quantized = cast(clipped, 'int8')
|
|
return quantized
|
|
|
|
past_key_value = quantize_tensor(
|
|
past_key_value, self.kv_orig_quant_scale.value)
|
|
|
|
if self.use_fp8_qdq and self.quant_mode.has_kv_cache_quant():
|
|
past_key_value = quantize(past_key_value,
|
|
self.kv_orig_quant_scale.value,
|
|
dtype='fp8')
|
|
|
|
# MQA broadcast
|
|
if self.num_attention_heads // self.num_attention_kv_heads > 1:
|
|
key = repeat_interleave(
|
|
key,
|
|
self.num_attention_heads // self.num_attention_kv_heads, 1)
|
|
value = repeat_interleave(
|
|
value,
|
|
self.num_attention_heads // self.num_attention_kv_heads, 1)
|
|
|
|
key_length = shape(key, 2)
|
|
|
|
# The following code creates a 2D tensor with 0s in the lower triangular (including the diagonal) and
|
|
# +INF in the upper triangular parts. This bias tensor will be added to the output of the Q*K^T matrix
|
|
# multiplication (BMM1). The +INF elements will be transformed to 0s by the Softmax operator that
|
|
# follows. The elements that corresponds to 0s in the bias are unaffected by the bias tensor.
|
|
#
|
|
# Note that when we added to another bias tensor B (for example, with AliBi), the values in the lower-
|
|
# triangular part of the B tensor are not affected and the upper-triangular ones are set to +INF.
|
|
if self.attention_mask_type == AttentionMaskType.causal:
|
|
query_length = shape(query, 2)
|
|
starts = concat([0, 0, key_length - query_length, 0])
|
|
sizes = concat([1, 1, query_length, key_length])
|
|
select_buf = np.expand_dims(
|
|
np.tril(
|
|
np.ones((self.max_position_embeddings,
|
|
self.max_position_embeddings))).astype(bool),
|
|
(0, 1))
|
|
|
|
select_buf = np.logical_not(select_buf)
|
|
mask_buf = np.zeros_like(select_buf, np.float32)
|
|
mask_buf[select_buf] = float('-inf')
|
|
buffer = constant(mask_buf)
|
|
generated_mask = slice(buffer, starts, sizes)
|
|
|
|
elif self.attention_mask_type == AttentionMaskType.bidirectional:
|
|
query_length = shape(query, 2)
|
|
zero_buf = np.expand_dims(
|
|
np.zeros((self.max_position_embeddings,
|
|
self.max_position_embeddings),
|
|
dtype=np.float32), (0, 1))
|
|
|
|
zero_buf[:, :, :-1, -1] = 1
|
|
zero_buf *= -10000
|
|
|
|
mask = constant(zero_buf)
|
|
|
|
# context phase, query_length
|
|
mask_size = where(query_length > 1, query_length, 1)
|
|
mask_start = where(query_length > 1,
|
|
self.max_position_embeddings - mask_size, 1)
|
|
start = concat([0, 0, mask_start, mask_start])
|
|
size = concat([1, 1, mask_size, mask_size])
|
|
generated_mask = slice(mask, start, size)
|
|
|
|
if attention_mask is not None:
|
|
attention_mask = expand_mask(attention_mask, shape(query, 2))
|
|
bias = attention_mask
|
|
if self.position_embedding_type.is_alibi():
|
|
alibi_biases = generate_alibi_biases(alibi_slopes, key_length)
|
|
bias = alibi_biases if bias is None else bias + alibi_biases
|
|
|
|
key = key.permute([0, 1, 3, 2])
|
|
with precision('float32'):
|
|
if norm_before_bmm1:
|
|
# Apply norm on query earlier to prevent matmul fp16 overflow.
|
|
query /= self.norm_factor
|
|
attention_scores = matmul(cast(query, 'float32'),
|
|
cast(key, 'float32'))
|
|
if not norm_before_bmm1:
|
|
attention_scores = attention_scores / self.norm_factor
|
|
|
|
if self.attention_mask_type in [
|
|
AttentionMaskType.causal,
|
|
AttentionMaskType.bidirectional
|
|
]:
|
|
bias = generated_mask if bias is None else bias + generated_mask
|
|
|
|
if bias is not None and not self.cross_attention:
|
|
attention_scores = attention_scores + bias
|
|
|
|
attention_probs = softmax(attention_scores, dim=-1)
|
|
|
|
if default_net().strongly_typed and (attention_probs.dtype !=
|
|
value.dtype):
|
|
attention_probs = cast(attention_probs, value.dtype)
|
|
|
|
context = matmul(attention_probs, value).permute([0, 2, 1, 3])
|
|
context = context.view(
|
|
concat([shape(context, 0),
|
|
shape(context, 1), self.hidden_size]))
|
|
|
|
dense_lora_param = None
|
|
if lora_param is not None:
|
|
dense_lora_param = lora_param.get_runtime_params(0, "attn_dense")
|
|
context = self.dense(context,
|
|
workspace,
|
|
lora_runtime_param=dense_lora_param)
|
|
|
|
if use_cache:
|
|
return (context, past_key_value)
|
|
else:
|
|
return context
|
|
|
|
|
|
class BertAttention(Module):
|
|
|
|
def __init__(self,
|
|
hidden_size,
|
|
num_attention_heads,
|
|
max_position_embeddings=1024,
|
|
num_layers=1,
|
|
attention_head_size=None,
|
|
num_kv_heads=None,
|
|
q_scaling=1.0,
|
|
apply_query_key_layer_scaling=False,
|
|
bias=True,
|
|
dtype=None,
|
|
tp_group=None,
|
|
tp_size=1,
|
|
tp_rank=0,
|
|
relative_attention=False,
|
|
max_distance=0,
|
|
num_buckets=0):
|
|
super().__init__()
|
|
|
|
self.attention_head_size = hidden_size // num_attention_heads if attention_head_size is None else attention_head_size
|
|
self.num_attention_heads = num_attention_heads // tp_size
|
|
self.num_attention_kv_heads = (
|
|
num_kv_heads + tp_size - 1
|
|
) // tp_size if num_kv_heads is not None else self.num_attention_heads
|
|
self.hidden_size = hidden_size // tp_size
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.norm_factor = math.sqrt(self.attention_head_size)
|
|
self.tp_size = tp_size
|
|
self.tp_rank = tp_rank
|
|
|
|
self.num_layers = num_layers
|
|
self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
|
|
self.norm_factor = math.sqrt(self.attention_head_size)
|
|
self.q_scaling = q_scaling
|
|
if self.apply_query_key_layer_scaling:
|
|
self.norm_factor *= self.num_layers
|
|
self.q_scaling *= self.num_layers
|
|
|
|
self.dtype = dtype
|
|
|
|
self.relative_attention = relative_attention
|
|
self.max_distance = max_distance
|
|
|
|
# out dim is not necessarily hidden_size + kv specific size (in MQA/GQA), but num_heads * heads_size
|
|
# example: d_model != num_heads * head_size in Flan-T5
|
|
self.qkv = ColumnLinear(
|
|
hidden_size,
|
|
tp_size * self.num_attention_heads * self.attention_head_size +
|
|
(2 * tp_size * self.num_attention_kv_heads *
|
|
self.attention_head_size),
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
gather_output=False)
|
|
self.dense = RowLinear(tp_size * self.num_attention_heads *
|
|
self.attention_head_size,
|
|
hidden_size,
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size)
|
|
|
|
# per-layer relative attention table
|
|
if relative_attention:
|
|
self.rel_attn_table = Parameter(shape=(num_attention_heads //
|
|
tp_size, num_buckets),
|
|
dtype=dtype)
|
|
|
|
def forward(self,
|
|
hidden_states: Tensor,
|
|
attention_mask=None,
|
|
input_lengths=None,
|
|
workspace=None,
|
|
max_input_length=None):
|
|
assert isinstance(hidden_states, Tensor)
|
|
|
|
qkv = self.qkv(hidden_states)
|
|
|
|
if default_net().plugin_config.bert_attention_plugin:
|
|
# TRT plugin mode
|
|
assert input_lengths is not None
|
|
context = bert_attention(
|
|
qkv,
|
|
input_lengths,
|
|
self.num_attention_heads,
|
|
self.attention_head_size,
|
|
q_scaling=self.q_scaling,
|
|
relative_attention=self.relative_attention,
|
|
max_distance=self.max_distance,
|
|
relative_attention_bias=self.rel_attn_table.value
|
|
if self.relative_attention else None,
|
|
max_input_length=max_input_length)
|
|
else:
|
|
# plain TRT mode
|
|
def transpose_for_scores(x):
|
|
new_x_shape = concat([
|
|
shape(x, 0),
|
|
shape(x, 1), self.num_attention_heads,
|
|
self.attention_head_size
|
|
])
|
|
return x.view(new_x_shape).permute([0, 2, 1, 3])
|
|
|
|
query, key, value = split(qkv, self.hidden_size, dim=2)
|
|
query = transpose_for_scores(query)
|
|
key = transpose_for_scores(key)
|
|
value = transpose_for_scores(value)
|
|
|
|
key = key.permute([0, 1, 3, 2])
|
|
attention_scores = matmul(query, key)
|
|
attention_scores = attention_scores / self.norm_factor
|
|
|
|
if attention_mask is not None:
|
|
attention_scores = attention_scores + attention_mask
|
|
|
|
attention_probs = softmax(attention_scores, dim=-1)
|
|
|
|
context = matmul(attention_probs, value).permute([0, 2, 1, 3])
|
|
context = context.view(
|
|
concat([shape(context, 0),
|
|
shape(context, 1), self.hidden_size]))
|
|
|
|
context = self.dense(context, workspace)
|
|
|
|
return context
|