mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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817 lines
33 KiB
Python
817 lines
33 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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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from dataclasses import dataclass
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from typing import List, Optional, Tuple, Type, Union
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from tensorrt_llm.functional import (AllReduceFusionOp, AllReduceParams,
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AttentionMaskType, PositionEmbeddingType,
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Tensor, gather_last_token_logits, recv,
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send)
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from tensorrt_llm.layers.attention import (Attention, AttentionParams,
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KeyValueCacheParams,
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SpecDecodingParams)
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from tensorrt_llm.layers.embedding import Embedding
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from tensorrt_llm.layers.linear import ColumnLinear
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from tensorrt_llm.layers.lora import LoraParams
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from tensorrt_llm.layers.mlp import GatedMLP
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from tensorrt_llm.layers.normalization import RmsNorm
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from tensorrt_llm.llmapi.kv_cache_type import KVCacheType
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from tensorrt_llm.mapping import Mapping
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from tensorrt_llm.models.convert_utils import has_safetensors
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from tensorrt_llm.models.modeling_utils import DecoderModelForCausalLM
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from tensorrt_llm.models.nemotron_nas.config import DeciConfig
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from tensorrt_llm.models.nemotron_nas.convert import (
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load_weights_from_hf_model, load_weights_from_hf_safetensors,
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update_weights_following_modelopt_optimization)
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from tensorrt_llm.module import Module, ModuleList
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from tensorrt_llm.plugin.plugin import init_all_reduce_helper
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from ..._common import default_net
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from ..._utils import pad_vocab_size
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from ..modeling_utils import PretrainedConfig, QuantConfig, preprocess_weights
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@dataclass
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class DeciLMLayerOutput:
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hidden_states: Tensor
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present_kv: Optional[Tensor] = None
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@dataclass
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class DeciLMLayerListOutput:
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hidden_states: Tensor
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present_kvs: List[Tensor]
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class NoOp(Module):
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def forward(self, hidden_states: Tensor, *args, **kwargs) -> int:
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return 0
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class NoOpAttention(NoOp):
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def forward(self,
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hidden_states: Tensor,
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attention_mask=None,
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use_cache: bool = False,
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*args,
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**kwargs) -> Union[int, Tuple[int, None]]:
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out = super().forward(hidden_states=hidden_states,
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attention_mask=attention_mask,
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use_cache=use_cache,
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*args,
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**kwargs)
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if use_cache:
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return out, None
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return out
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class LinearAttention(ColumnLinear):
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def forward(self,
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hidden_states: Tensor,
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attention_mask=None,
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use_cache: bool = False,
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*args,
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**kwargs) -> Union[Tensor, Tuple[Tensor, None]]:
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out = super().forward(x=hidden_states,
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lora_runtime_params=None,
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lora_hidden_state=None)
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if use_cache:
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return out, None
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return out
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class LinearFFN(ColumnLinear):
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def forward(self,
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hidden_states,
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lora_layer_params=None,
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all_reduce_params: Optional[AllReduceParams] = None) -> Tensor:
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return super().forward(x=hidden_states,
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lora_runtime_params=None,
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lora_hidden_state=None)
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NoOpFFN = NoOp
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NoOpLayerNorm = NoOp
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class DeciLMDecoderLayer(Module):
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def __init__(self, config: DeciConfig, layer_idx: int):
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super().__init__()
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self.layer_idx = layer_idx
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self.config = config
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layers_range = config.mapping.pp_layers(config.num_hidden_layers)
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self.local_layer_idx = layer_idx - layers_range[0]
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self.layer_config = self.config.get_layer_config(self.layer_idx)
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self._init_attention()
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self._init_ffn()
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@property
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def input_layernorm_was_fused(self) -> bool:
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"""
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The previous layer ran our input_layernorm for us if:
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1. The reduce_fusion plugin is enabled and
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2. We are not the first local model layer and
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3. The previous layer is an MLP layer
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"""
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return default_net(
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).plugin_config.reduce_fusion and self.local_layer_idx > 0 and self.config.get_layer_config(
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self.layer_idx -
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1).is_mlp_layer and self.needs_input_layernorm_fusion
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@property
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def needs_input_layernorm_fusion(self) -> bool:
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"""
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This layer needs the previous layer to perform input_layernorm fusion if:
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1. The reduce_fusion plugin is enabled and
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2. This is not a NOOP attention layer (otherwise it has no input_layernorm)
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"""
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return default_net(
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).plugin_config.reduce_fusion and not self.layer_config.is_noop_attention_layer
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@property
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def can_fuse_post_layernorm(self) -> bool:
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"""
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This layer can fuse attention and post_layernorm if:
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1. The reduce_fusion plugin is enabled and
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2. It is an attention layer and
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3. It is not a NOOP FFN layer (othrewise it has no post_layernorm)
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"""
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return default_net(
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).plugin_config.reduce_fusion and self.layer_config.is_attention_layer and not self.layer_config.is_noop_ffn_layer
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@property
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def can_fuse_input_layernorm(self) -> bool:
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"""
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This layer can run the next layer's input_layernorm if:
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1. The reduce_fusion plugin is enable and
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2. It is an MLP layer
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"""
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return default_net(
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).plugin_config.reduce_fusion and self.layer_config.is_mlp_layer
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def _init_attention(self) -> None:
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"""
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Initialize some attention alternative
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"""
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# normal attention
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if self.layer_config.is_attention_layer:
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# according to recurrentgemma, len(layer_types) can be less than num_hidden_layers
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# in this case, the list should wrap-around
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# for example, if layer_types = ["attention", "recurrent", "recurrent"], and we have 5 layers, we get:
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# layer 0 ==> attention
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# layer 1 ==> recurrent
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# layer 2 ==> recurrent
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# layer 3 ==> attention
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# layer 4 ==> recurrent
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# we check which layers are local to our rank
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layers_range = self.config.mapping.pp_layers(
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self.config.num_hidden_layers)
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# then take the size of layer_types in the config
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layer_type_len = len(self.config.layer_types)
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# collect the layer types of all the local layers
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local_layer_types = [
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self.config.layer_types[layer_id % layer_type_len]
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for layer_id in layers_range
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]
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# and see how many of them are attention layers to determine our local attention layer idx
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local_attn_layer_idx = local_layer_types[:self.
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local_layer_idx].count(
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"attention")
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# Iterate over all local layer configs, getting num_kv_heads of the attention ones
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num_kv_heads_per_local_layer = [
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layer_config.attention.num_key_value_heads for layer_config in
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[self.config.layer_configs[idx] for idx in layers_range]
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if layer_config.is_attention_layer
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]
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# adjust num heads according to tp size
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num_kv_heads_per_local_layer = [
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(nheads + self.config.mapping.tp_size - 1) //
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self.config.mapping.tp_size
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for nheads in num_kv_heads_per_local_layer
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]
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nheads_tp = (self.layer_config.attention.num_key_value_heads +
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self.config.mapping.tp_size -
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1) // self.config.mapping.tp_size
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self.input_layernorm = RmsNorm(
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normalized_shape=self.config.hidden_size,
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eps=self.config.norm_epsilon,
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dtype=self.config.dtype,
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)
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self.attention = Attention(
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local_layer_idx=local_attn_layer_idx,
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hidden_size=self.config.hidden_size,
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attention_head_size=self.config.head_size,
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num_attention_heads=self.config.num_attention_heads,
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num_kv_heads=self.layer_config.attention.num_key_value_heads,
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max_position_embeddings=self.config.max_position_embeddings,
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dtype=self.config.dtype,
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attention_mask_type=AttentionMaskType.causal,
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bias=False,
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position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
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rotary_embedding_base=self.config.rotary_base,
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rotary_embedding_scaling=self.config.rotary_scaling,
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tp_group=self.config.mapping.tp_group,
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tp_size=self.config.mapping.tp_size,
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tp_rank=self.config.mapping.tp_rank,
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quant_mode=self.config.quant_mode)
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elif self.layer_config.is_noop_attention_layer:
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self.input_layernorm = NoOpLayerNorm()
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self.attention = NoOpAttention()
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elif self.layer_config.is_linear_attention_layer:
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self.input_layernorm = RmsNorm(
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normalized_shape=self.config.hidden_size,
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eps=self.config.norm_epsilon,
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dtype=self.config.dtype,
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)
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self.attention = LinearAttention(
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in_features=self.config.hidden_size,
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out_features=self.config.hidden_size,
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bias=False,
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dtype=self.config.dtype,
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tp_group=self.config.mapping.tp_group,
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tp_size=self.config.mapping.tp_size,
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gather_output=True)
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else:
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raise NotImplementedError(
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f"Attention of type {str(self.layer_config.attention.impl)} is not implemented"
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)
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def _init_ffn(self) -> None:
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"""
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Initialize some ffn alternative
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"""
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if self.layer_config.is_mlp_layer:
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intermediate_size = self.layer_config.ffn.intermediate_size or self.config.intermediate_size
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mlp_hidden_size = intermediate_size or self.config.hidden_size * 4
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self.post_layernorm = RmsNorm(
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normalized_shape=self.config.hidden_size,
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eps=self.config.norm_epsilon,
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dtype=self.config.dtype,
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)
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self.ffn = GatedMLP(
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hidden_size=self.config.hidden_size,
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ffn_hidden_size=mlp_hidden_size,
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hidden_act=self.config.hidden_act,
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bias=False,
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dtype=self.config.dtype,
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tp_group=self.config.mapping.tp_group,
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tp_size=self.config.mapping.tp_size,
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quant_mode=self.config.quant_mode,
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)
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elif self.layer_config.is_noop_ffn_layer:
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self.post_layernorm = NoOpLayerNorm()
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self.ffn = NoOpFFN()
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elif self.layer_config.is_linear_ffn_layer:
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self.post_layernorm = RmsNorm(
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normalized_shape=self.config.hidden_size,
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eps=self.config.norm_epsilon,
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dtype=self.config.dtype,
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)
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self.ffn = LinearFFN(in_features=self.config.hidden_size,
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out_features=self.config.hidden_size,
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bias=False,
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dtype=self.config.dtype,
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tp_group=self.config.mapping.tp_group,
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tp_size=self.config.mapping.tp_size,
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gather_output=True)
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else:
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raise NotImplementedError(
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f"FFN of type {str(self.layer_config.ffn.impl)} is not implemented"
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)
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def forward(self,
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hidden_states: Tensor | Tuple[Tensor, Tensor],
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attention_mask: Optional[Tensor] = None,
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use_cache: bool = False,
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spec_decoding_params=None,
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kv_cache_params: Optional[KeyValueCacheParams] = None,
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attention_params: Optional[AttentionParams] = None,
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lora_layer_params: Optional[LoraParams] = None,
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next_layer_input_layernorm_args: Optional[Tuple[Tensor,
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float]] = None):
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if self.input_layernorm_was_fused:
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# previous layer already performed our layer norm
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assert isinstance(hidden_states, tuple)
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hidden_states, residual = hidden_states
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else:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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if self.can_fuse_post_layernorm:
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all_reduce_params = AllReduceParams(
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fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM,
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residual=residual,
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norm_weight=self.post_layernorm.weight.value,
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eps=self.post_layernorm.eps)
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else:
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all_reduce_params = None
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attention_output = self._run_attention(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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use_cache=use_cache,
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spec_decoding_params=spec_decoding_params,
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kv_cache_params=kv_cache_params,
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attention_params=attention_params,
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lora_layer_params=lora_layer_params,
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all_reduce_params=all_reduce_params)
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if use_cache:
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attention_output, present_kv = attention_output
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else:
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present_kv = None
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if self.can_fuse_post_layernorm:
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hidden_states, residual = attention_output
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else:
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hidden_states = residual + attention_output
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residual = hidden_states
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hidden_states = self.post_layernorm(hidden_states)
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if next_layer_input_layernorm_args is not None:
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assert self.can_fuse_input_layernorm
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norm_weight, eps = next_layer_input_layernorm_args
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all_reduce_params = AllReduceParams(
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fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM,
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residual=residual,
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norm_weight=norm_weight,
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eps=eps)
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hidden_states = self._run_ffn(hidden_states,
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lora_layer_params=lora_layer_params,
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all_reduce_params=all_reduce_params)
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else:
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hidden_states = self._run_ffn(hidden_states,
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lora_layer_params=lora_layer_params)
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hidden_states = residual + hidden_states
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return DeciLMLayerOutput(hidden_states=hidden_states,
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present_kv=present_kv)
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def _run_attention(
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self,
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hidden_states: Tensor,
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attention_mask: Optional[Tensor] = None,
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use_cache: bool = False,
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spec_decoding_params=None,
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kv_cache_params: Optional[KeyValueCacheParams] = None,
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attention_params: Optional[AttentionParams] = None,
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lora_layer_params: Optional[LoraParams] = None,
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all_reduce_params: Optional[AllReduceParams] = None
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) -> Union[Tensor, Tuple[Tensor, None]]:
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"""
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Ideally, this functionality would be encapsulated in a LinearAttention class, but during
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FP8 and lower quantization, our linear classes get overrun by ModelOpt, thus we must
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control the attention inputs at the DecoderLayer level.
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"""
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if self.layer_config.is_linear_attention_layer:
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out = self.attention(hidden_states)
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return out, None if use_cache else out
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else:
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if not self.layer_config.is_attention_layer:
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assert all_reduce_params is None, f"Layer with attention of type {self.layer_config.attention.impl} can't do reduce_fusion"
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return self.attention(hidden_states=hidden_states,
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attention_mask=attention_mask,
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use_cache=use_cache,
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spec_decoding_params=spec_decoding_params,
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kv_cache_params=kv_cache_params,
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attention_params=attention_params,
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lora_layer_params=lora_layer_params,
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all_reduce_params=all_reduce_params)
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def _run_ffn(self,
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hidden_states,
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lora_layer_params=None,
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all_reduce_params: Optional[AllReduceParams] = None):
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"""
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Ideally, this functionality would be encapsulated in a LinearMLP class, but during
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FP8 and lower quantization, our linear classes get overrun by ModelOpt, thus we must
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control the MLP inputs at the DecoderLayer level.
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"""
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if all_reduce_params is not None:
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assert self.layer_config.is_mlp_layer, f"Layer with FFN of type {self.layer_config.ffn.impl} can't do reduce_fusion"
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if self.layer_config.is_linear_ffn_layer:
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return self.ffn(hidden_states)
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else:
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return self.ffn(hidden_states,
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lora_layer_params=lora_layer_params,
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all_reduce_params=all_reduce_params)
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class DeciLMDecoderLayerList(ModuleList):
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def __init__(self, cls: Type[DeciLMDecoderLayer], config: DeciConfig):
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self.num_hidden_layers = config.num_hidden_layers
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# global indices of local layers
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self.layer_list = config.mapping.pp_layers(config.num_hidden_layers)
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super().__init__([cls(config, idx) for idx in self.layer_list])
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# global indices of local attention layers
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self.attention_layer_list = [
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self.layer_list[i] for i, layer in enumerate(self)
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if layer.layer_config.is_attention_layer
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]
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def forward(
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self,
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hidden_states: Tensor,
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use_cache: bool,
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attention_mask: Optional[Tensor],
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kv_cache_params: KeyValueCacheParams,
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attention_params: Optional[AttentionParams] = None,
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position_ids: Optional[Tensor] = None,
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lora_params: Optional[LoraParams] = None,
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spec_decoding_params: Optional[SpecDecodingParams] = None,
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) -> DeciLMLayerListOutput:
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kv_cache_params.fill_none_tensor_list(len(self.layer_list))
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presents = []
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# put None where we don't have attention layers
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pkv_iter = iter(kv_cache_params.past_key_value)
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past_key_values = [x for x in pkv_iter]
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for layer_idx, (layer, past) in enumerate(zip(self, past_key_values)):
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next_layer_input_layernorm_args = None
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if default_net().plugin_config.reduce_fusion:
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if layer_idx < self.layer_list[-1]:
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# this is not the last layer
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next_layer = self[layer_idx + 1]
|
|
if layer.can_fuse_input_layernorm and next_layer.needs_input_layernorm_fusion:
|
|
# this layer can fuse the next layer's input_layernorm
|
|
next_layer_input_layernorm_args = (
|
|
next_layer.input_layernorm.weight.value,
|
|
next_layer.input_layernorm.eps)
|
|
|
|
layer_out = layer(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
attention_params=attention_params,
|
|
kv_cache_params=KeyValueCacheParams(
|
|
past_key_value=[past],
|
|
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,
|
|
host_sink_token_length=kv_cache_params.
|
|
host_sink_token_length,
|
|
kv_cache_block_offsets=kv_cache_params.
|
|
kv_cache_block_offsets,
|
|
host_kv_cache_block_offsets=kv_cache_params.
|
|
host_kv_cache_block_offsets,
|
|
host_kv_cache_pool_pointers=kv_cache_params.
|
|
host_kv_cache_pool_pointers,
|
|
host_kv_cache_pool_mapping=kv_cache_params.
|
|
host_kv_cache_pool_mapping,
|
|
cache_indirection=kv_cache_params.cache_indirection,
|
|
),
|
|
spec_decoding_params=spec_decoding_params,
|
|
use_cache=use_cache,
|
|
lora_layer_params=lora_params.get_layer_config(layer_idx)
|
|
if lora_params is not None
|
|
and lora_params.lora_ranks is not None else None,
|
|
next_layer_input_layernorm_args=next_layer_input_layernorm_args)
|
|
|
|
hidden_states = layer_out.hidden_states
|
|
if use_cache and layer_out.present_kv is not None:
|
|
presents.append(layer_out.present_kv)
|
|
|
|
return DeciLMLayerListOutput(hidden_states=hidden_states,
|
|
present_kvs=presents)
|
|
|
|
|
|
class DeciLMModel(Module):
|
|
|
|
def __init__(self, config: DeciConfig) -> None:
|
|
super().__init__()
|
|
init_all_reduce_helper()
|
|
|
|
self.mapping = config.mapping
|
|
if self.mapping.is_first_pp_rank():
|
|
# first rank in pipeline-parallel handles token embedding
|
|
assert config.vocab_size is not None
|
|
self.vocab_embedding = Embedding(config.vocab_size,
|
|
config.hidden_size,
|
|
dtype=config.dtype)
|
|
|
|
self.position_embedding_type = config.position_embedding_type
|
|
self.layers = DeciLMDecoderLayerList(DeciLMDecoderLayer, config)
|
|
|
|
if self.mapping.is_last_pp_rank():
|
|
# last rank in pipeline-parallel handles final norm
|
|
self.ln_f = RmsNorm(
|
|
normalized_shape=config.hidden_size,
|
|
eps=config.norm_epsilon,
|
|
dtype=config.dtype,
|
|
)
|
|
|
|
def _vocab_embedding(self,
|
|
input_ids: Tensor,
|
|
prompt_embedding_table: Optional[Tensor] = None,
|
|
prompt_tasks: Optional[Tensor] = None,
|
|
prompt_vocab_size: Optional[Tensor] = None) -> Tensor:
|
|
# prompt tuning
|
|
ptuning_args = ([
|
|
prompt_embedding_table, prompt_tasks, prompt_vocab_size
|
|
] if prompt_embedding_table is not None else [])
|
|
|
|
hidden_states = self.vocab_embedding(input_ids, *ptuning_args)
|
|
return hidden_states
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
position_ids=None,
|
|
use_cache: bool = False,
|
|
attention_mask: Optional[Tensor] = None,
|
|
spec_decoding_params=None,
|
|
kv_cache_params: Optional[KeyValueCacheParams] = None,
|
|
attention_params: Optional[AttentionParams] = None,
|
|
hidden_states: Optional[Tensor] = None,
|
|
prompt_embedding_table: Optional[Tensor] = None,
|
|
prompt_tasks: Optional[Tensor] = None,
|
|
prompt_vocab_size: Optional[Tensor] = None,
|
|
lora_params: Optional[LoraParams] = None,
|
|
) -> DeciLMLayerListOutput:
|
|
|
|
if self.mapping.is_first_pp_rank():
|
|
# first pipeline rank ==> do prompt embedding
|
|
hidden_states = self._vocab_embedding(
|
|
input_ids=input_ids,
|
|
prompt_embedding_table=prompt_embedding_table,
|
|
prompt_tasks=prompt_tasks,
|
|
prompt_vocab_size=prompt_vocab_size)
|
|
else:
|
|
# receive hidden states from prior rank in the pipeline
|
|
hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())
|
|
|
|
layers_out = self.layers.forward(
|
|
hidden_states,
|
|
use_cache=use_cache,
|
|
attention_mask=attention_mask,
|
|
kv_cache_params=kv_cache_params,
|
|
attention_params=attention_params,
|
|
lora_params=lora_params,
|
|
spec_decoding_params=spec_decoding_params,
|
|
)
|
|
|
|
if self.mapping.is_last_pp_rank():
|
|
# last pipeline rank ==> do final norm
|
|
hidden_states = self.ln_f(layers_out.hidden_states)
|
|
else:
|
|
# send hidden states to next rank in the pipeline
|
|
hidden_states = send(layers_out.hidden_states,
|
|
self.mapping.next_pp_rank())
|
|
|
|
return DeciLMLayerListOutput(hidden_states=hidden_states,
|
|
present_kvs=layers_out.present_kvs)
|
|
|
|
|
|
class DeciLMForCausalLM(DecoderModelForCausalLM):
|
|
config_class = DeciConfig
|
|
|
|
def __init__(self, config: DeciConfig):
|
|
|
|
transformer = DeciLMModel(config)
|
|
vocab_size_padded = pad_vocab_size(config.vocab_size,
|
|
config.mapping.tp_size)
|
|
|
|
if config.mapping.is_last_pp_rank():
|
|
# last pipeline rank needs to do calculate logits
|
|
lm_head = ColumnLinear(
|
|
config.hidden_size,
|
|
vocab_size_padded,
|
|
bias=False,
|
|
dtype=config.dtype,
|
|
tp_group=config.mapping.tp_group,
|
|
tp_size=config.mapping.tp_size,
|
|
gather_output=True,
|
|
)
|
|
else:
|
|
lm_head = None
|
|
super().__init__(config, transformer, lm_head)
|
|
|
|
# Create constant attention parameters to be reused by all layers.
|
|
Attention.create_attention_const_params(self, config)
|
|
self.position_embedding_type = config.position_embedding_type
|
|
|
|
@classmethod
|
|
def from_hugging_face(cls,
|
|
hf_model_or_dir: Union[
|
|
str, 'transformers.PreTrainedModel'],
|
|
dtype: str = 'auto',
|
|
mapping: Optional[Mapping] = None,
|
|
quant_config: Optional[QuantConfig] = None,
|
|
load_by_shard: bool = False,
|
|
load_model_on_cpu: bool = False,
|
|
trust_remote_code: bool = True,
|
|
**kwargs) -> "DeciLMForCausalLM":
|
|
import transformers
|
|
|
|
# TODO(oargov): add support for these
|
|
assert not load_by_shard, "load_by_shard is not implemented yet"
|
|
|
|
use_preloading = isinstance(hf_model_or_dir,
|
|
transformers.PreTrainedModel)
|
|
if use_preloading:
|
|
hf_config_or_dir = hf_model_or_dir.config
|
|
else:
|
|
hf_config_or_dir = hf_model_or_dir
|
|
|
|
config = DeciConfig.from_hugging_face(
|
|
hf_config_or_dir,
|
|
dtype=dtype,
|
|
mapping=mapping,
|
|
quant_config=quant_config,
|
|
trust_remote_code=trust_remote_code,
|
|
**kwargs)
|
|
|
|
if use_preloading:
|
|
assert not load_by_shard
|
|
weights = load_weights_from_hf_model(hf_model_or_dir, config)
|
|
elif has_safetensors(
|
|
hf_model_or_dir) and not config.quant_mode.has_any_quant():
|
|
weights = load_weights_from_hf_safetensors(hf_model_or_dir, config)
|
|
else:
|
|
hf_model = transformers.AutoModelForCausalLM.from_pretrained(
|
|
hf_model_or_dir,
|
|
device_map='auto' if not load_model_on_cpu else 'cpu',
|
|
dtype=dtype,
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
weights = load_weights_from_hf_model(hf_model, config)
|
|
preprocess_weights(weights, config)
|
|
|
|
model = DeciLMForCausalLM(config)
|
|
model.load(weights)
|
|
return model
|
|
|
|
@classmethod
|
|
def from_checkpoint(cls,
|
|
ckpt_dir: str,
|
|
rank: Optional[int] = None,
|
|
config: Optional["PretrainedConfig"] = None):
|
|
return super().from_checkpoint(
|
|
ckpt_dir,
|
|
rank,
|
|
config,
|
|
preprocess_weights_hook=
|
|
update_weights_following_modelopt_optimization,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Tensor,
|
|
position_ids: Optional[Tensor] = None,
|
|
use_cache: bool = False,
|
|
last_token_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
kv_cache_params: Optional[KeyValueCacheParams] = None,
|
|
attention_params: Optional[AttentionParams] = None,
|
|
hidden_states: Optional[Tensor] = None,
|
|
prompt_embedding_table: Optional[Tensor] = None,
|
|
prompt_tasks: Optional[Tensor] = None,
|
|
prompt_vocab_size: Optional[Tensor] = None,
|
|
lora_params: Optional[LoraParams] = None,
|
|
spec_decoding_params=None,
|
|
):
|
|
# fill attention params.
|
|
attention_params = Attention.fill_attention_params(
|
|
self, attention_params)
|
|
|
|
model_out = self.transformer.forward(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
use_cache=use_cache,
|
|
attention_mask=attention_mask,
|
|
kv_cache_params=kv_cache_params,
|
|
attention_params=attention_params,
|
|
lora_params=lora_params,
|
|
hidden_states=hidden_states,
|
|
prompt_embedding_table=prompt_embedding_table,
|
|
prompt_tasks=prompt_tasks,
|
|
prompt_vocab_size=prompt_vocab_size,
|
|
spec_decoding_params=spec_decoding_params)
|
|
hidden_states = model_out.hidden_states
|
|
|
|
if self.config.mapping.is_last_pp_rank():
|
|
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.config.logits_dtype)
|
|
else:
|
|
hidden_states.mark_output("hidden_states_output", self.config.dtype)
|
|
|
|
if use_cache and not default_net().plugin_config.paged_kv_cache:
|
|
presents = model_out.present_kvs
|
|
for i, present in zip(self.transformer.layers.attention_layer_list,
|
|
presents):
|
|
present.mark_output(f"present_key_value_{i}",
|
|
self.config.kv_dtype)
|
|
if self.config.mapping.is_last_pp_rank():
|
|
return (lm_logits, presents, hidden_states)
|
|
return (hidden_states, presents)
|
|
else:
|
|
if self.config.mapping.is_last_pp_rank():
|
|
return lm_logits, hidden_states
|
|
return hidden_states
|
|
|
|
def prepare_attention_inputs(
|
|
self,
|
|
*,
|
|
max_batch_size: int,
|
|
max_beam_width: int,
|
|
max_input_len: int,
|
|
max_seq_len: int,
|
|
num_kv_heads: int,
|
|
head_size: int,
|
|
num_layers: int,
|
|
kv_dtype: str,
|
|
kv_cache_type: KVCacheType,
|
|
num_profiles: int = 1,
|
|
enable_ctx_gen_opt_profiles: bool = False,
|
|
remove_input_padding: bool = False,
|
|
use_gpt_attention_plugin: bool = False,
|
|
paged_kv_cache: bool = False,
|
|
tokens_per_block: int = 32,
|
|
mapping: Mapping = Mapping(),
|
|
use_cache: bool = True,
|
|
streamingllm: bool = False,
|
|
attn_layer_idx: Optional[List[int]] = None,
|
|
opt_batch_size: Optional[int] = None,
|
|
num_kv_heads_per_layer: Optional[List[int]] = None):
|
|
|
|
if attn_layer_idx is None:
|
|
attn_layer_idx, num_kv_heads_per_layer = [], []
|
|
for layer_idx in range(self.config.num_hidden_layers):
|
|
layer_config = self.config.get_layer_config(layer_idx)
|
|
if layer_config.is_attention_layer:
|
|
attn_layer_idx.append(layer_idx)
|
|
num_kv_heads_per_layer.append(
|
|
layer_config.attention.num_key_value_heads)
|
|
|
|
attention_inputs = super().prepare_attention_inputs(
|
|
max_batch_size=max_batch_size,
|
|
max_beam_width=max_beam_width,
|
|
max_input_len=max_input_len,
|
|
max_seq_len=max_seq_len,
|
|
num_kv_heads=num_kv_heads,
|
|
head_size=head_size,
|
|
num_layers=num_layers,
|
|
kv_dtype=kv_dtype,
|
|
num_profiles=num_profiles,
|
|
kv_cache_type=kv_cache_type,
|
|
enable_ctx_gen_opt_profiles=enable_ctx_gen_opt_profiles,
|
|
remove_input_padding=remove_input_padding,
|
|
use_gpt_attention_plugin=use_gpt_attention_plugin,
|
|
tokens_per_block=tokens_per_block,
|
|
mapping=mapping,
|
|
streamingllm=streamingllm,
|
|
attn_layer_idx=attn_layer_idx,
|
|
opt_batch_size=opt_batch_size,
|
|
num_kv_heads_per_layer=num_kv_heads_per_layer)
|
|
|
|
return attention_inputs
|