mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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284 lines
12 KiB
Python
Executable File
284 lines
12 KiB
Python
Executable File
# 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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import torch
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from ..._utils import pad_vocab_size, torch_dtype_to_str
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from ...functional import Tensor, non_gated_version, recv, send
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from ...layers import (AttentionMaskType, ColumnLinear, DeepseekV2Attention,
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Embedding, GatedMLP, MoeConfig, PositionEmbeddingType,
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RmsNorm, SharedMoE)
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from ...mapping import Mapping
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from ...module import Module
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from ...plugin import init_all_reduce_helper
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from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
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PretrainedConfig)
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from .convert import convert_deepseekv2, create_trt_config_from_hf
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class DeepseekV2DecoderLayer(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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### Input layernorm in Deepseek v2 is same as Llama
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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 = DeepseekV2Attention(
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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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q_lora_rank=config.q_lora_rank,
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kv_lora_rank=config.kv_lora_rank,
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qk_nope_head_dim=config.qk_nope_head_dim,
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qk_rope_head_dim=config.qk_rope_head_dim,
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v_head_dim=config.v_head_dim,
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max_position_embeddings=config.max_position_embeddings,
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eps=config.norm_epsilon,
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attention_mask_type=AttentionMaskType.causal,
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dtype=config.dtype,
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position_embedding_type=PositionEmbeddingType.learned_absolute,
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rotary_embedding_base=config.rotary_base,
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rotary_embedding_scaling=None,
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rotary_embedding_beta_fast=config.rotary_scaling['beta_fast'],
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rotary_embedding_beta_slow=config.rotary_scaling['beta_slow'],
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rotary_embedding_mscale=config.rotary_scaling['mscale'],
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rotary_embedding_mscale_all_dim=config.
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rotary_scaling['mscale_all_dim'],
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rotary_embedding_origin_max_position=config.
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rotary_scaling['original_max_position_embeddings'],
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rotary_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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ClsMLP = GatedMLP
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### Added deepseek MoE and shared_experts
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### First decoder layer: MLA + dense MLP + input_layernorm(RMSNorm) + post_attention_layernorm(RMSNorm)
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### Rest decoder layer: MLA + MoE MLP + MoE Gate + shared_experts(MLP) + input_layernorm(RMSNorm) + post_attention_layernorm(RMSNorm)
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### Added MLA in co-testing phase, use standard attention for MoE testing
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### Distinguish dense MLP and MoE MLP
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# dense_config = DenseConfig(intermediate_size=config.intermediate_size)
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moe_config = MoeConfig(num_experts=config.moe_num_experts,
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moe_intermediate_size=config.moe_inter_size,
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num_shared_experts=config.moe_num_shared_experts,
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top_k=config.moe_top_k,
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normalization_mode=config.moe_renorm_mode,
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device_limited_n_group=config.moe_n_group,
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device_limited_topk_group=config.moe_topk_group,
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device_limited_routed_scaling_factor=config.
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moe_routed_scaling_factor)
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# layer_config = LayerMLPConfig(config=[dense_config, moe_config], moe_layer_idx_min=0,
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# moe_layer_idx_max=config.num_hidden_layers,
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# total_num_layers=config.num_hidden_layers)
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mlp_kwargs = {}
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if config.moe_num_experts > 0 and layer_idx > 0:
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mlp_hidden_size = moe_config.num_shared_experts * moe_config.moe_intermediate_size
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hidden_act = config.hidden_act
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ClsMLP = SharedMoE
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mlp_kwargs = {"moe_config": moe_config, "mapping": config.mapping}
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else:
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ClsMLP = GatedMLP
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mlp_hidden_size = config.intermediate_size
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hidden_act = non_gated_version(
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config.hidden_act) # back to non gated for dense layers
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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=hidden_act,
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dtype=config.dtype,
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bias=False,
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tp_group=config.mapping.tp_group,
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tp_size=config.mapping.tp_size,
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**mlp_kwargs)
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### Pose layernorm in Deepseek v2 is same as Llama
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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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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=hidden_states,
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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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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_attn = 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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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 DeepseekV2Model(Module):
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def __init__(self, config: PretrainedConfig) -> None:
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super().__init__()
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init_all_reduce_helper() # enable use_customer_all_reduce
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self.dtype = config.dtype
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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(DeepseekV2DecoderLayer, 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.head_num = config.num_attention_heads
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self.head_size = config.qk_nope_head_dim + config.qk_rope_head_dim
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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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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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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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kv_cache_params=kv_cache_params,
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attention_params=attention_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 DeepseekV2ForCausalLM(DecoderModelForCausalLM):
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def __init__(self, config: PretrainedConfig):
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transformer = DeepseekV2Model(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.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,
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model_dir,
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dtype: str = 'auto',
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mapping: Optional[Mapping] = None,
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override_fields={},
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**kwargs):
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assert hf_model is not None
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if mapping is None:
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mapping = Mapping()
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config = create_trt_config_from_hf(model_dir,
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dtype,
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mapping=mapping,
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override_fields=override_fields)
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print(config)
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pretrained_config = PretrainedConfig.from_dict(config)
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pretrained_config.set_rank(mapping.rank) # TODO:remove this hack
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if dtype == 'auto':
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dtype = getattr(config, 'torch_dtype', None)
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if dtype is None:
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dtype = 'float16'
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if isinstance(dtype, torch.dtype):
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dtype = torch_dtype_to_str(dtype)
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if dtype == 'float32': # should remove "float32"
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dtype = 'float16'
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if dtype == 'bfloat16' and torch.cuda.get_device_properties(
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0).major < 8:
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logger.warning(
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"Pre SM 80 GPUs do not support bfloat16, fallback to float16")
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dtype = 'float16'
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deepseek = cls.from_config(pretrained_config)
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weights = convert_deepseekv2(
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hf_model,
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config,
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mapping,
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dtype=dtype,
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use_parallel_embedding=config.get('use_parallel_embedding', False),
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sharding_dim=config.get('embedding_sharding_dim', 0),
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share_embedding_table=config.get('share_embedding_table', False))
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#check_share_embedding(weights, config)
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deepseek.load(weights)
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return deepseek
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