mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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678 lines
26 KiB
Python
678 lines
26 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 numpy as np
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import tensorrt as trt
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import torch
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from ..._common import default_net
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from ..._utils import pad_vocab_size, str_dtype_to_trt
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from ...functional import (PositionEmbeddingType, Tensor, concat, constant,
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expand, expand_dims, gather_last_token_logits,
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gpt_attention, index_select, select, shape, slice,
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split)
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from ...layers import (MLP, AttentionMaskType, AttentionParams, ColumnLinear,
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Embedding, KeyValueCacheParams, RmsNorm, RowLinear)
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from ...mapping import Mapping
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from ...module import Module, ModuleList
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from ...parameter import Parameter
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from ...quantization import QuantMode
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from ..generation_mixin import GenerationMixin
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def apply_rotary_pos_emb_trt(x: Tensor, rope_cache: Tensor) -> Tensor:
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# x-> [seq, batch, num_heads, 2]
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x = x.permute((1, 0, 2, 3))
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# sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3)
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sq = shape(x, 0)
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b = shape(x, 1)
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nh = shape(x, 2)
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shape(x, 3)
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# rope_cache shape: seq,batch,heads,2 rot_dim = 2* numheads
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#rope_cache: seq,batch,num_states/4,2
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rot_dim = shape(rope_cache, 2) * constant(np.array(2, dtype=np.int32))
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starts = concat([0, 0, 0, 0])
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sizes = concat([sq, b, nh, rot_dim])
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# first half
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x_rot = slice(x, starts, sizes)
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starts = concat([0, 0, 0, rot_dim])
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# second half
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x_pass = slice(x, starts, sizes)
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# truncate to support variable sizes
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rope_cache = slice(rope_cache, (0, 0, 0, 0), (concat(
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[sq,
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shape(rope_cache, 1),
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shape(rope_cache, 2),
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shape(rope_cache, 3)])))
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xshaped = x_rot.view(concat([sq, b, nh, rot_dim / 2, 2]))
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rope_cache = rope_cache.view(concat([sq, b, 1, shape(xshaped, 3), 2]))
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# first half
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xshape0 = select(xshaped, 4, 0)
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# second half
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xshape1 = select(xshaped, 4, 1)
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# first half
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rope_cache0 = select(rope_cache, 4, 0)
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# second half
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rope_cache1 = select(rope_cache, 4, 1)
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out0 = xshape0 * rope_cache0 - xshape1 * rope_cache1
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out1 = xshape1 * rope_cache0 + xshape0 * rope_cache1
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out0 = expand_dims(out0, 4)
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out1 = expand_dims(out1, 4)
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x_out2_v1 = concat([out0, out1], 4)
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x_out2 = x_out2_v1.view(
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concat([sq, b, nh, shape(x_out2_v1, 3) * shape(x_out2_v1, 4)]))
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output = concat([x_out2, x_pass], dim=3)
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# to batch,seq,num_group,head_states
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output = output.permute((1, 0, 2, 3))
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return output
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class RotaryEmbedding(Module):
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def __init__(self, dim):
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super().__init__()
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self.dim = dim
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def forward(self, seq_len: int):
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theta = 1.0 / (10000**(torch.arange(0, self.dim, 2) / self.dim))
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seq_idx = torch.arange(seq_len)
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idx_theta = torch.outer(seq_idx, theta).float()
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cache = torch.stack(
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[torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)
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cache = cache.half()
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# create rope embeddings and make it constant
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cache = constant(cache.numpy())
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return cache
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class ChatGLM2Attention(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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layer_number,
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kv_channels=128,
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multi_query_group_num=2,
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apply_query_key_layer_scaling=False,
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attention_mask_type=AttentionMaskType.causal,
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qkv_bias=True,
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linear_bias=False,
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dtype='float16',
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use_int8_kv_cache=False,
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tp_group=None,
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tp_size=1,
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):
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super().__init__()
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self.attention_mask_type = attention_mask_type
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self.attention_head_size = hidden_size // num_attention_heads
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self.num_attention_heads = num_attention_heads // tp_size
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self.num_multi_query_groups_per_partition = multi_query_group_num
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self.num_attention_kv_heads = self.num_attention_heads
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self.hidden_size = hidden_size // tp_size
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self.projection_size = num_attention_heads * kv_channels
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self.hidden_size_per_attention_head = kv_channels
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self.layer_number = layer_number
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self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
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self.q_scaling = 1
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if apply_query_key_layer_scaling:
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self.q_scaling *= self.layer_number
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self.position_embedding_type = PositionEmbeddingType.learned_absolute
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self.multi_block_mode = False
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self.multi_query_mode = False
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self.rotary_embedding_dim = 0
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self.dtype = dtype
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self.use_int8_kv_cache = use_int8_kv_cache
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if self.use_int8_kv_cache:
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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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# Note: in multi_query_mode, only query heads are split between multiple GPUs,
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# while key/value head are not split as there is only one head per key/value.
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# The output feature size is therefore (h/tp + 2) * d, where h is num_heads,
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# d is head_size, and tp is tensor_parallel_size.
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# In ColumnLinear op, the output dim is calculated by (h + 2*tp) * d / tp,
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# which matches the desired output size (h/tp + 2) * d after splitting
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self.qkv_hidden_size = (self.projection_size +
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2 * self.hidden_size_per_attention_head * 2)
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self.qkv = ColumnLinear(hidden_size,
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self.qkv_hidden_size,
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bias=qkv_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 = RowLinear(hidden_size,
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hidden_size,
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bias=linear_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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def forward(self,
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hidden_states: Tensor,
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rotary_pos_emb,
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use_cache=True,
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kv_cache_params=None,
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attention_params=None):
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if not default_net().plugin_config.gpt_attention_plugin:
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raise ValueError(
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'ChatGLM2 is only supported with GPTAttention plugin,pleas build it with --use_gpt_attention_plugin argument.'
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)
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assert isinstance(hidden_states, Tensor)
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qkv = self.qkv(hidden_states)
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query, key, value = split(qkv, [
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self.num_attention_heads * self.hidden_size_per_attention_head,
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self.num_multi_query_groups_per_partition *
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self.hidden_size_per_attention_head,
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self.num_multi_query_groups_per_partition *
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self.hidden_size_per_attention_head,
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],
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dim=-1)
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query = query.view(
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concat([
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shape(qkv, 0),
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shape(qkv, 1), self.num_attention_heads,
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self.attention_head_size
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]))
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key = key.view(
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concat([
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shape(qkv, 0),
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shape(qkv, 1), self.num_multi_query_groups_per_partition,
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self.attention_head_size
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]))
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value = value.view(
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concat([
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shape(qkv, 0),
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shape(qkv, 1), self.num_multi_query_groups_per_partition,
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self.attention_head_size
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]))
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if rotary_pos_emb is not None:
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query = apply_rotary_pos_emb_trt(query, rotary_pos_emb)
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key = apply_rotary_pos_emb_trt(key, rotary_pos_emb)
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# batch,seq,num_group,1,head_states
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key = expand_dims(key, 3)
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#expand 16x
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expand_rate = self.num_attention_heads // self.num_multi_query_groups_per_partition
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key = expand(
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key,
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concat([
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shape(key, 0),
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shape(key, 1),
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shape(key, 2), expand_rate,
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shape(key, 4)
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]))
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# batch,seq,num_heads,head_states
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key = key.view(
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concat([
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shape(key, 0),
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shape(key, 1),
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shape(key, 2) * shape(key, 3),
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shape(key, 4)
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]))
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value = expand_dims(value, 3)
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value = expand(
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value,
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concat([
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shape(value, 0),
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shape(value, 1),
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shape(value, 2), expand_rate,
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shape(value, 4)
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]))
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value = value.view(
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concat([
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shape(value, 0),
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shape(value, 1),
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shape(value, 2) * shape(value, 3),
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shape(value, 4)
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]))
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qkv = concat([query, key, value], dim=2)
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qkv = qkv.view(
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concat([shape(qkv, 0),
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shape(qkv, 1), self.hidden_size * 3]))
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assert attention_params.is_valid(
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default_net().plugin_config.gpt_attention_plugin,
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default_net().plugin_config.remove_input_padding)
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assert kv_cache_params.is_valid(
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default_net().plugin_config.gpt_attention_plugin)
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kv_orig_quant_scale = self.kv_orig_quant_scale.value if self.use_int8_kv_cache else None
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kv_quant_orig_scale = self.kv_quant_orig_scale.value if self.use_int8_kv_cache else None
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context, past_key_value = gpt_attention(
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tensor=qkv,
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past_key_value=kv_cache_params.get_first_past_key_value(),
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sequence_length=attention_params.sequence_length,
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host_past_key_value_lengths=kv_cache_params.
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host_past_key_value_lengths,
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context_lengths=attention_params.context_lengths,
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cache_indirection=kv_cache_params.cache_indirection,
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host_request_types=attention_params.host_request_types,
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num_heads=self.num_attention_heads,
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num_kv_heads=self.
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num_attention_heads, # since self.multi_query_mode is set to False
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hidden_size_per_head=self.attention_head_size,
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q_scaling=self.q_scaling,
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rotary_embedding_dim=self.rotary_embedding_dim,
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position_embedding_type=self.position_embedding_type,
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multi_block_mode=self.multi_block_mode,
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kv_orig_quant_scale=kv_orig_quant_scale,
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kv_quant_orig_scale=kv_quant_orig_scale,
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kv_cache_quant_mode=QuantMode.INT8_KV_CACHE
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if self.use_int8_kv_cache else QuantMode(0),
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kv_cache_block_pointers=kv_cache_params.
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get_first_kv_cache_block_pointers(),
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max_context_length=attention_params.max_context_length,
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host_context_lengths=attention_params.host_context_lengths)
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# dense layer after self-attention
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context = self.dense(context)
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if use_cache:
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return (context, past_key_value)
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else:
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return context
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class ChatGLM2Block(Module):
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def __init__(self,
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hidden_size,
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num_attention_heads,
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kv_channels=128,
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multi_query_group_num=2,
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apply_query_key_layer_scaling=False,
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attention_mask_type=AttentionMaskType.causal,
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qkv_bias=True,
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linear_bias=False,
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use_int8_kv_cache=False,
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tp_group=None,
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tp_size=1,
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ffn_hiden_size=13696,
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layer_number=1,
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eps=1e-5,
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act_func='swiglu',
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dtype=trt.float16,
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quant_mode=QuantMode(0)):
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super(ChatGLM2Block, self).__init__()
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self.layer_number = layer_number
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.dtype = dtype
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self.ffn_hiden_size = ffn_hiden_size
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self.apply_residual_connection_post_layernorm = False
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self.fp32_residual_connection = False
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LayerNormFunc = RmsNorm
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# Layernorm on the input data.
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self.input_layernorm = LayerNormFunc(self.hidden_size,
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eps=eps,
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dtype=dtype)
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# Self attention.
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self.self_attention = ChatGLM2Attention(
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hidden_size, num_attention_heads, layer_number, kv_channels,
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multi_query_group_num, apply_query_key_layer_scaling,
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attention_mask_type, qkv_bias, linear_bias, dtype,
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use_int8_kv_cache, tp_group, tp_size)
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self.hidden_dropout = 0.0
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# Layernorm on the attention output
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self.post_attention_layernorm = LayerNormFunc(self.hidden_size,
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eps=eps,
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dtype=dtype)
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self.mlp = MLP(self.hidden_size, ffn_hiden_size, act_func, linear_bias,
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dtype)
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def forward(self,
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hidden_states,
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rotary_pos_emb,
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use_cache=True,
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kv_cache_params=None,
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attention_params=None):
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# hidden_states: [s, b, h]
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# Layer norm at the beginning of the transformer layer.
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layernorm_output = self.input_layernorm(hidden_states)
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# Self attention.
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attention_output, kv_cache = self.self_attention(
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layernorm_output,
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rotary_pos_emb,
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use_cache=use_cache,
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kv_cache_params=kv_cache_params,
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attention_params=attention_params)
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# Residual connection.
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = hidden_states
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layernorm_input = hidden_states + attention_output
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# Layer norm post the self attention.
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layernorm_output = self.post_attention_layernorm(layernorm_input)
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# MLP.
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mlp_output = self.mlp(layernorm_output)
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# Second residual connection.
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = layernorm_input
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output = residual + mlp_output
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return output, kv_cache
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class ChatGLM2Transformer(Module):
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"""Transformer class."""
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def __init__(self,
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hidden_size,
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num_attention_heads,
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kv_channels=128,
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multi_query_group_num=2,
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apply_query_key_layer_scaling=False,
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attention_mask_type=AttentionMaskType.causal,
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qkv_bias=True,
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linear_bias=False,
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use_int8_kv_cache=False,
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tp_group=None,
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tp_size=1,
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ffn_hiden_size=13696,
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num_layers=28,
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eps=1e-5,
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act_func='swiglu',
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dtype=trt.float16,
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quant_mode=QuantMode(0)):
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super(ChatGLM2Transformer, self).__init__()
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self.fp32_residual_connection = False
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self.post_layer_norm = True
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# Number of layers.
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self.num_layers = num_layers
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# Transformer layers.
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def build_layer(layer_number):
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return ChatGLM2Block(hidden_size, num_attention_heads, kv_channels,
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multi_query_group_num,
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apply_query_key_layer_scaling,
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attention_mask_type, qkv_bias, linear_bias,
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use_int8_kv_cache, tp_group, tp_size,
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ffn_hiden_size, layer_number, eps, act_func,
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dtype, quant_mode)
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self.layers = ModuleList(
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build_layer(i + 1) for i in range(self.num_layers))
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if self.post_layer_norm:
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self.final_layernorm = RmsNorm(hidden_size, eps=eps, dtype=dtype)
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self.gradient_checkpointing = False
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def _get_layer(self, layer_number):
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return self.layers[layer_number]
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def forward(self,
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hidden_states,
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rotary_pos_emb,
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use_cache=True,
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kv_cache_params=None,
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attention_params=None):
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presents = []
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for index in range(self.num_layers):
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layer = self._get_layer(index)
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hidden_states, kv_cache = layer(
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hidden_states,
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rotary_pos_emb,
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use_cache=use_cache,
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kv_cache_params=KeyValueCacheParams(
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past_key_value=[kv_cache_params.past_key_value[index]],
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kv_cache_block_pointers=[
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kv_cache_params.kv_cache_block_pointers[index]
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],
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host_past_key_value_lengths=kv_cache_params.
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host_past_key_value_lengths,
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cache_indirection=kv_cache_params.cache_indirection),
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attention_params=attention_params)
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presents.append(kv_cache)
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if self.post_layer_norm:
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states, presents
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class ChatGLM2Model(Module):
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def __init__(self,
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hidden_size,
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num_attention_heads,
|
|
kv_channels=128,
|
|
multi_query_group_num=2,
|
|
apply_query_key_layer_scaling=False,
|
|
attention_mask_type=AttentionMaskType.causal,
|
|
qkv_bias=True,
|
|
linear_bias=False,
|
|
use_int8_kv_cache=False,
|
|
mapping=Mapping(),
|
|
ffn_hiden_size=13696,
|
|
num_layers=28,
|
|
eps=1e-5,
|
|
act_func='swiglu',
|
|
dtype=trt.float16,
|
|
quant_mode=QuantMode(0),
|
|
max_seq_length=32768,
|
|
vocab_size=65024):
|
|
super(ChatGLM2Model, self).__init__()
|
|
|
|
self.dtype = dtype
|
|
self.embedding = Embedding(vocab_size, hidden_size, dtype=dtype)
|
|
self.num_layers = num_layers
|
|
self.multi_query_group_num = multi_query_group_num
|
|
self.kv_channels = kv_channels
|
|
|
|
# Rotary positional embeddings
|
|
self.max_seq_length = max_seq_length
|
|
rotary_dim = kv_channels
|
|
self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, )
|
|
self.encoder = ChatGLM2Transformer(
|
|
hidden_size, num_attention_heads, kv_channels,
|
|
multi_query_group_num, apply_query_key_layer_scaling,
|
|
attention_mask_type, qkv_bias, linear_bias, use_int8_kv_cache,
|
|
mapping.tp_group, mapping.tp_size, ffn_hiden_size, num_layers, eps,
|
|
act_func, dtype, quant_mode)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Tensor,
|
|
position_ids,
|
|
use_cache=True,
|
|
kv_cache_params=None,
|
|
attention_params=None,
|
|
):
|
|
|
|
inputs_embeds = self.embedding(input_ids)
|
|
# Rotary positional embeddings
|
|
# generate 32768 pos embeddings
|
|
# max_seq_length,head_dim/4,2
|
|
rotary_pos_emb = self.rotary_pos_emb(self.max_seq_length)
|
|
flat_position = position_ids.view(
|
|
concat([shape(position_ids, 0) * shape(position_ids, 1)]))
|
|
selected_pos_emb = index_select(rotary_pos_emb, 0, flat_position)
|
|
# selected batch,seq from rotary_pos_emb
|
|
selected_pos_emb = selected_pos_emb.view(
|
|
concat([
|
|
shape(position_ids, 0),
|
|
shape(position_ids, 1),
|
|
shape(rotary_pos_emb, 1),
|
|
shape(rotary_pos_emb, 2)
|
|
]))
|
|
# seq,batch
|
|
selected_pos_emb = selected_pos_emb.permute((1, 0, 2, 3))
|
|
# return inputs_embeds,selected_pos_emb
|
|
# Run encoder.
|
|
|
|
hidden_states, presents = self.encoder(
|
|
inputs_embeds,
|
|
selected_pos_emb,
|
|
use_cache=use_cache,
|
|
kv_cache_params=kv_cache_params,
|
|
attention_params=attention_params,
|
|
)
|
|
return hidden_states, presents
|
|
|
|
|
|
class ChatGLM2HeadModel(ChatGLM2Model, GenerationMixin):
|
|
|
|
def __init__(self,
|
|
hidden_size,
|
|
num_attention_heads,
|
|
kv_channels=128,
|
|
multi_query_group_num=2,
|
|
apply_query_key_layer_scaling=False,
|
|
attention_mask_type=AttentionMaskType.causal,
|
|
qkv_bias=True,
|
|
linear_bias=False,
|
|
use_int8_kv_cache=False,
|
|
mapping=Mapping(),
|
|
ffn_hiden_size=13696,
|
|
num_layers=28,
|
|
eps=1e-5,
|
|
act_func='swiglu',
|
|
dtype=trt.float16,
|
|
quant_mode=QuantMode(0),
|
|
max_seq_length=32768,
|
|
vocab_size=65024,
|
|
use_cache=True,
|
|
kv_cache_block_pointers=None):
|
|
if isinstance(dtype, str):
|
|
self._kv_dtype = str_dtype_to_trt(dtype)
|
|
else:
|
|
assert isinstance(dtype, trt.DataType)
|
|
self._kv_dtype = dtype
|
|
self._dtype = self._kv_dtype
|
|
if quant_mode.has_int8_kv_cache():
|
|
self._kv_dtype = str_dtype_to_trt('int8')
|
|
elif quant_mode.has_fp8_kv_cache():
|
|
self._kv_dtype = str_dtype_to_trt('fp8')
|
|
self.use_cache = use_cache
|
|
self.kv_cache_block_pointers = kv_cache_block_pointers
|
|
self.quant_mode = quant_mode
|
|
self._num_layers = num_layers
|
|
self._num_heads = num_attention_heads
|
|
self._hidden_size = hidden_size
|
|
self._vocab_size = vocab_size
|
|
self._tp_size = mapping.tp_size
|
|
super().__init__(hidden_size, num_attention_heads, kv_channels,
|
|
multi_query_group_num, apply_query_key_layer_scaling,
|
|
attention_mask_type, qkv_bias, linear_bias,
|
|
use_int8_kv_cache, mapping, ffn_hiden_size, num_layers,
|
|
eps, act_func, dtype, quant_mode, max_seq_length,
|
|
vocab_size)
|
|
vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
|
|
self.lm_head = ColumnLinear(hidden_size,
|
|
vocab_size_padded,
|
|
bias=False,
|
|
dtype=dtype,
|
|
tp_group=mapping.tp_group,
|
|
tp_size=mapping.tp_size,
|
|
gather_output=True)
|
|
|
|
def forward(self,
|
|
input_ids=None,
|
|
position_ids=None,
|
|
last_token_ids=None,
|
|
kv_cache_params=None,
|
|
attention_params=None):
|
|
|
|
hidden_states = super().forward(input_ids, position_ids, self.use_cache,
|
|
kv_cache_params, attention_params)
|
|
|
|
if self.use_cache:
|
|
hidden_states, presents = hidden_states
|
|
|
|
hidden_states = gather_last_token_logits(
|
|
hidden_states, last_token_ids,
|
|
default_net().plugin_config.remove_input_padding)
|
|
|
|
lm_logits = self.lm_head(hidden_states)
|
|
lm_logits.mark_output('logits', self._dtype)
|
|
|
|
if default_net().plugin_config.paged_kv_cache == False:
|
|
for i, present in enumerate(presents):
|
|
present.mark_output(f'present_key_value_{i}', self._kv_dtype)
|
|
return (lm_logits, presents)
|
|
return lm_logits
|
|
|
|
def prepare_inputs(self,
|
|
max_batch_size,
|
|
max_input_len,
|
|
max_new_tokens,
|
|
use_cache,
|
|
max_beam_width: int = 1):
|
|
'''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
|
|
ranges of the dimensions of when using TRT dynamic shapes.
|
|
|
|
@return: a list contains values which can be fed into the self.forward()
|
|
'''
|
|
# Prepare inputs
|
|
head_size = self._hidden_size // self._num_heads
|
|
num_heads = self._num_heads // self._tp_size
|
|
remove_input_padding = default_net().plugin_config.remove_input_padding
|
|
use_gpt_attention_plugin = default_net(
|
|
).plugin_config.gpt_attention_plugin
|
|
use_gemm_plugin = default_net().plugin_config.gemm_plugin
|
|
|
|
model_inputs = self.prepare_basic_inputs(
|
|
max_batch_size,
|
|
max_beam_width,
|
|
max_input_len,
|
|
max_new_tokens,
|
|
num_heads,
|
|
head_size,
|
|
self.num_layers,
|
|
self._kv_dtype,
|
|
remove_input_padding,
|
|
use_gpt_attention_plugin,
|
|
use_gemm_plugin=use_gemm_plugin)
|
|
|
|
return (model_inputs['input_ids'], model_inputs['position_ids'],
|
|
model_inputs['last_token_ids'],
|
|
KeyValueCacheParams(
|
|
past_key_value=model_inputs['past_key_value'],
|
|
host_past_key_value_lengths=model_inputs[
|
|
'host_past_key_value_lengths'],
|
|
kv_cache_block_pointers=model_inputs[
|
|
'kv_cache_block_pointers_list'],
|
|
cache_indirection=model_inputs['cache_indirection'],
|
|
),
|
|
AttentionParams(
|
|
sequence_length=model_inputs['sequence_length'],
|
|
context_lengths=model_inputs['context_lengths'],
|
|
host_context_lengths=model_inputs['host_context_lengths'],
|
|
max_context_length=max_input_len,
|
|
host_request_types=model_inputs['host_request_types']))
|