mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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308 lines
12 KiB
Python
308 lines
12 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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import math
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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, cast, recv, send
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from ...layers import (Attention, AttentionMaskType, AttentionParams,
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ColumnLinear, Embedding, GatedMLP, KeyValueCacheParams,
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LoraParams, PositionEmbeddingType, RmsNorm)
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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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from .weight import load_from_hf_gemma
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class GemmaDecoderLayer(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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num_attention_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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attention_head_size=config.head_size,
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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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quant_mode=config.quant_mode,
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)
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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.mlp = GatedMLP(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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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: Tensor,
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attention_mask: Optional[Tensor] = None,
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spec_decoding_packed_mask: Optional[Tensor] = None,
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spec_decoding_position_offsets: Optional[Tensor] = None,
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use_cache: bool = False,
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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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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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spec_decoding_packed_mask=
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spec_decoding_packed_mask, # For Medusa support
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spec_decoding_position_offsets=spec_decoding_position_offsets,
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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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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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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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hidden_states = self.mlp(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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if use_cache:
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return (hidden_states, presents)
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return hidden_states
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class GemmaModel(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(GemmaDecoderLayer, config)
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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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self.hidden_size = config.hidden_size
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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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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 = cast(hidden_states * math.sqrt(self.hidden_size),
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hidden_states.dtype)
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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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)
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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 GemmaForCausalLM(DecoderModelForCausalLM):
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def __init__(self, config: PretrainedConfig):
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self.check_config(config)
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transformer = GemmaModel(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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try:
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import modelopt
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major, minor, patch = modelopt.__version__.split(".")
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major = int(major)
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minor = int(minor)
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patch = int(patch)
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if major == 0 and minor == 11 and patch < 1:
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# modelopt=0.11.0 won't force this field to True, this is a hot fix
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# TODO: can remove after modelop=0.11.1 is out
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# TRT LLM forces the embedding table to be shared for gemma.
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config.share_embedding_table = True
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assert config.share_embedding_table, "Gemma only supports share_embedding_table"
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except:
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# Not find modelopt, assume not use modelopt quantized model
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assert config.share_embedding_table, "Gemma only supports share_embedding_table"
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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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@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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import transformers
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from transformers import GemmaConfig
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from ...models.modeling_utils import PretrainedConfig
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cfg = GemmaConfig.from_pretrained(hf_model_dir)
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num_kv_heads = cfg.num_key_value_heads if hasattr(cfg, "num_key_value_heads") \
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else cfg.num_attention_heads
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quantization = kwargs.get('quantization', QuantConfig())
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if mapping is None:
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mapping = Mapping()
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cfg.mapping = mapping
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cfg.dtype = dtype
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cfg.norm_epsilon = cfg.rms_norm_eps
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config = {
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'architecture': cfg.architectures[0],
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'dtype': cfg.dtype,
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'logits_dtype': 'float32',
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'num_hidden_layers': cfg.num_hidden_layers,
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'num_attention_heads': cfg.num_attention_heads,
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'head_size': cfg.head_dim,
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'hidden_size': cfg.hidden_size,
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'intermediate_size': cfg.intermediate_size,
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'num_key_value_heads': num_kv_heads,
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'vocab_size': cfg.vocab_size,
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'position_embedding_type': 'rope_gpt_neox',
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'max_position_embeddings': cfg.max_position_embeddings,
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'hidden_act': cfg.hidden_act,
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'rotary_base': getattr(cfg, 'rotary_base', 10000.0),
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'rotary_scaling': getattr(cfg, 'rotary_scaling', None),
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'norm_epsilon': cfg.rms_norm_eps,
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'quantization': quantization.asdict(),
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'mapping': {
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'world_size': mapping.world_size,
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'tp_size': mapping.world_size,
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},
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'use_parallel_embedding': kwargs.get("use_parallel_embedding",
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False),
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'embedding_sharding_dim': kwargs.get("embedding_sharding_dim", 0),
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'use_fused_mlp': kwargs.get("use_fused_mlp", False),
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}
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assert not quantization.quant_mode.has_any_quant()
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tllm_llama = GemmaForCausalLM(PretrainedConfig.from_dict(config))
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hf_model = transformers.GemmaForCausalLM
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hf_llama = hf_model.from_pretrained(
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hf_model_dir,
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device_map={
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"model": "cpu",
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"lm_head": "cpu",
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"embed_tokens": "cpu",
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"layers": "cpu",
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"norm": "cpu",
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}, # Load to CPU memory
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torch_dtype='auto',
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)
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weights = load_from_hf_gemma(
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tllm_llama,
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hf_llama,
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mapping=mapping,
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dtype=dtype,
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# TODO: these shall be outside from_hugging_face too.
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use_gemm_woq_plugin=kwargs.get("use_gemm_woq_plugin", False),
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)
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del hf_llama
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tllm_llama.load(weights)
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return tllm_llama
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def check_config(self, config):
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config.set_if_not_exist('use_parallel_embedding', False)
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config.set_if_not_exist('embedding_sharding_dim', 0)
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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('use_fused_mlp', False)
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