mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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274 lines
11 KiB
Python
274 lines
11 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 typing import Optional
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from ..._utils import pad_vocab_size
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from ...functional import Tensor, recv, send
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from ...layers import (MOE, Attention, AttentionMaskType, ColumnLinear,
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Embedding, MoeConfig, PositionEmbeddingType, RmsNorm)
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from ...lora_helper import LoraConfig, use_lora
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from ...mapping import Mapping
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from ...module import Module
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from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
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PretrainedConfig, QuantConfig)
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class GrokDecoderLayer(Module):
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def __init__(self, config: PretrainedConfig, 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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self.input_layernorm = RmsNorm(normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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dtype=config.dtype)
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layers_range = config.mapping.pp_layers(config.num_hidden_layers)
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local_layer_idx = layer_idx - layers_range[0]
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self.attention = Attention(
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local_layer_idx=local_layer_idx,
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hidden_size=config.hidden_size,
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attention_head_size=config.head_size,
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num_attention_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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max_position_embeddings=config.max_position_embeddings,
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dtype=config.dtype,
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attention_mask_type=AttentionMaskType.causal,
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bias=config.attn_bias,
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position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
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rotary_embedding_base=config.rotary_base,
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rotary_embedding_scaling=config.rotary_scaling,
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tp_group=config.mapping.tp_group,
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tp_size=config.mapping.tp_size,
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tp_rank=config.mapping.tp_rank,
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quant_mode=config.quant_mode,
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max_attn_value=config.max_attn_value)
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mlp_hidden_size = config.hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
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self.post_attn_layernorm = RmsNorm(normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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dtype=config.dtype)
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self.post_mlp_layernorm = RmsNorm(normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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dtype=config.dtype)
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mlp_kwargs = {}
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assert config.moe_num_experts > 1, "Grok model is a MoE model."
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ClsMLP = MOE
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moe_config = MoeConfig(
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num_experts=config.moe_num_experts,
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top_k=config.moe_top_k,
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normalization_mode=config.moe_normalization_mode).validate()
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mlp_kwargs = {
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"moe_config": moe_config,
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"mapping": config.mapping,
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}
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self.mlp = ClsMLP(hidden_size=config.hidden_size,
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ffn_hidden_size=mlp_hidden_size,
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hidden_act=config.hidden_act,
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dtype=config.dtype,
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bias=config.mlp_bias,
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tp_group=config.mapping.tp_group,
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tp_size=config.mapping.tp_size,
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quant_mode=config.quant_mode,
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**mlp_kwargs)
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self.post_layernorm = RmsNorm(normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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dtype=config.dtype)
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def forward(self,
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hidden_states,
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attention_mask=None,
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use_cache=False,
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spec_decoding_params=None,
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kv_cache_params=None,
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attention_params=None,
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lora_layer_params=None):
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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attention_output = self.attention(
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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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if use_cache:
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attention_output, presents = attention_output
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attention_output = self.post_attn_layernorm(attention_output)
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hidden_states = residual + attention_output
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residual_attn = hidden_states
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# regular llama/mixtral layers
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hidden_states = self.post_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states,
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lora_layer_params=lora_layer_params)
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hidden_states = self.post_mlp_layernorm(hidden_states)
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hidden_states = residual_attn + hidden_states
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if use_cache:
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return (hidden_states, presents)
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return hidden_states
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class GrokModel(Module):
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def __init__(self, config: PretrainedConfig) -> None:
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super().__init__()
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self.mapping = config.mapping
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if self.mapping.is_first_pp_rank():
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self.vocab_embedding = Embedding(config.vocab_size,
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config.hidden_size,
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dtype=config.dtype)
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self.layers = DecoderLayerList(GrokDecoderLayer, config)
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self.embedding_multiplier_scale = config.embedding_multiplier_scale
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if self.mapping.is_last_pp_rank():
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self.ln_f = RmsNorm(normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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dtype=config.dtype)
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def forward(self,
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input_ids,
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position_ids=None,
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use_cache=False,
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attention_mask=None,
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spec_decoding_params=None,
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kv_cache_params=None,
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attention_params=None,
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hidden_states=None,
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prompt_embedding_table: Optional[Tensor] = None,
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prompt_tasks: Optional[Tensor] = None,
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prompt_vocab_size: Optional[Tensor] = None,
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lora_params=None):
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ptuning_args = [
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prompt_embedding_table, prompt_tasks, prompt_vocab_size
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] if prompt_embedding_table is not None else []
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if self.mapping.is_first_pp_rank():
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hidden_states = self.vocab_embedding(input_ids, *ptuning_args)
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hidden_states *= 78.38367176906169
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else:
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hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())
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hidden_states = self.layers.forward(
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hidden_states,
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use_cache=use_cache,
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attention_mask=attention_mask,
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kv_cache_params=kv_cache_params,
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attention_params=attention_params,
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lora_params=lora_params,
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spec_decoding_params=spec_decoding_params)
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if use_cache:
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hidden_states, presents = hidden_states
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if self.mapping.is_last_pp_rank():
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hidden_states = self.ln_f(hidden_states)
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else:
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hidden_states = send(hidden_states, self.mapping.next_pp_rank())
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if use_cache:
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return (hidden_states, tuple(presents))
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return hidden_states
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class GrokForCausalLM(DecoderModelForCausalLM):
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def __init__(self, config: PretrainedConfig):
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self.check_config(config)
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transformer = GrokModel(config)
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vocab_size_padded = pad_vocab_size(config.vocab_size,
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config.mapping.tp_size)
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if config.mapping.is_last_pp_rank():
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lm_head = ColumnLinear(config.hidden_size,
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vocab_size_padded,
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bias=False,
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dtype=config.dtype,
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tp_group=config.mapping.tp_group,
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tp_size=config.mapping.tp_size,
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gather_output=True)
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else:
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lm_head = None
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self.quant_mode = config.quant_mode
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self.mapping = config.mapping
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super().__init__(config, transformer, lm_head)
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def check_config(self, config):
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config.set_if_not_exist('mlp_bias', False)
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config.set_if_not_exist('attn_bias', False)
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config.set_if_not_exist('rotary_base', 10000.0)
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config.set_if_not_exist('rotary_scaling', None)
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config.set_if_not_exist('moe_num_experts', 0)
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config.set_if_not_exist('moe_top_k', 0)
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config.set_if_not_exist('moe_normalization_mode',
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MoeConfig.ExpertScaleNormalizationMode.NONE)
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@classmethod
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def from_hugging_face(cls,
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hf_model_dir,
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dtype='float16',
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mapping: Optional[Mapping] = None,
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**kwargs):
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from . import convert
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if mapping is None:
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mapping = Mapping()
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grok = convert.from_hugging_face(
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cls,
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hf_model_dir,
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dtype,
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mapping=mapping,
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quantization=kwargs.get('quantization', QuantConfig()),
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override_fields=kwargs.get('override_fields', {}),
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skip_loading_weights=kwargs.get('skip_loading_weights', False),
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preloaded_model=kwargs.get('preloaded_model', None))
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return grok
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def default_plugin_config(self, **kwargs):
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plugin_config = super().default_plugin_config(**kwargs)
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if self.quant_mode.is_int4_weight_only_per_group():
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plugin_config.set_weight_only_groupwise_quant_matmul_plugin()
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return plugin_config
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@classmethod
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def quantize(
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cls,
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hf_model_dir,
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output_dir,
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quant_config: QuantConfig,
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*,
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dtype='float16',
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mapping: Optional[Mapping] = None,
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calib_batches=512,
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calib_batch_size=1,
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random_seed=1234,
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tokenizer_max_seq_length=2048,
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**kwargs,
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):
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pass
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def use_lora(self, lora_config: LoraConfig):
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use_lora(self, lora_config)
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