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297 lines
10 KiB
Python
297 lines
10 KiB
Python
# coding=utf-8
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# Adapted from https://huggingface.co/mosaicml/mpt-7b/tree/main
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import math
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from typing import Iterable, List, Optional, Tuple
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import torch
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import torch.nn as nn
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from vllm.attention import Attention, AttentionMetadata
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from vllm.distributed import (get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size)
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import SamplerOutput
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from vllm.transformers_utils.configs.mpt import MPTConfig
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def _get_alibi_slopes(
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total_num_heads: int,
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alibi_bias_max: int,
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) -> torch.Tensor:
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next_power_of_2 = 2**math.ceil(math.log2(total_num_heads))
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m = torch.arange(1, next_power_of_2 + 1, dtype=torch.float32)
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m = m.mul(alibi_bias_max / next_power_of_2)
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slopes = 1.0 / torch.pow(2, m)
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if next_power_of_2 != total_num_heads:
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slopes = torch.concat([slopes[1::2], slopes[::2]])[:total_num_heads]
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return slopes
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class MPTAttention(nn.Module):
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def __init__(
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self,
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config: MPTConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.d_model = config.d_model
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self.total_num_heads = config.n_heads
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self.head_dim = self.d_model // self.total_num_heads
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self.clip_qkv = config.attn_config["clip_qkv"]
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self.qk_ln = config.attn_config["qk_ln"]
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self.alibi_bias_max = config.attn_config["alibi_bias_max"]
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if "kv_n_heads" in config.attn_config:
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self.total_num_kv_heads = config.attn_config['kv_n_heads']
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else:
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self.total_num_kv_heads = self.total_num_heads
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assert not config.attn_config["prefix_lm"]
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assert config.attn_config["alibi"]
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# pylint: disable=invalid-name
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self.Wqkv = QKVParallelLinear(
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self.d_model,
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self.d_model // self.total_num_heads,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=not config.no_bias,
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quant_config=quant_config,
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)
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if self.qk_ln:
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self.q_ln = nn.LayerNorm(self.d_model)
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self.k_ln = nn.LayerNorm(self.d_model)
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self.out_proj = RowParallelLinear(
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self.d_model,
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self.d_model,
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bias=not config.no_bias,
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quant_config=quant_config,
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)
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tp_world_size = get_tensor_model_parallel_world_size()
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assert self.total_num_heads % tp_world_size == 0
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self.num_heads = self.total_num_heads // tp_world_size
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if self.total_num_kv_heads >= tp_world_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_world_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_world_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_world_size)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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# Create the alibi slopes and slice them.
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tp_rank = get_tensor_model_parallel_rank()
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head_start = tp_rank * self.num_heads
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head_end = (tp_rank + 1) * self.num_heads
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alibi_slopes = _get_alibi_slopes(self.total_num_heads,
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self.alibi_bias_max)
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alibi_slopes = alibi_slopes[head_start:head_end].tolist()
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self.head_dim = self.d_model // self.total_num_heads
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scaling = self.head_dim**-0.5
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self.attn = Attention(self.num_heads,
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self.head_dim,
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scaling,
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alibi_slopes=alibi_slopes,
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num_kv_heads=self.num_kv_heads)
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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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del position_ids # unused.
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qkv, _ = self.Wqkv(hidden_states)
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if self.clip_qkv is not None:
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qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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if self.qk_ln:
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q = self.q_ln(q)
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k = self.k_ln(k)
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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output, _ = self.out_proj(attn_output)
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return output
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class MPTMLP(nn.Module):
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def __init__(
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self,
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config: MPTConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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hidden_size = config.d_model
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expansion_ratio = config.expansion_ratio
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intermediate_size = expansion_ratio * hidden_size
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self.up_proj = ColumnParallelLinear(
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hidden_size,
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intermediate_size,
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bias=not config.no_bias,
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quant_config=quant_config,
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)
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quant_config = getattr(quant_config, "quant_config", None)
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self.act = get_act_fn("gelu", quant_config, intermediate_size)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=not config.no_bias,
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quant_config=quant_config,
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x, _ = self.up_proj(x)
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x = self.act(x)
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x, _ = self.down_proj(x)
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return x
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class MPTBlock(nn.Module):
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def __init__(
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self,
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config: MPTConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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hidden_size = config.d_model
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self.norm_1 = nn.LayerNorm(hidden_size)
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self.attn = MPTAttention(config, quant_config)
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self.norm_2 = nn.LayerNorm(hidden_size)
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self.ffn = MPTMLP(config, quant_config)
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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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x = self.norm_1(hidden_states)
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x = self.attn(
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position_ids=position_ids,
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hidden_states=x,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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hidden_states = hidden_states + x
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x = self.norm_2(hidden_states)
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x = self.ffn(x)
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hidden_states = hidden_states + x
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return hidden_states
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class MPTModel(nn.Module):
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def __init__(
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self,
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config: MPTConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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assert config.embedding_fraction == 1.0
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assert config.norm_type == "low_precision_layernorm"
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self.wte = VocabParallelEmbedding(
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config.vocab_size,
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config.d_model,
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)
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self.blocks = nn.ModuleList(
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[MPTBlock(config, quant_config) for _ in range(config.n_layers)])
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self.norm_f = nn.LayerNorm(config.d_model)
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if config.no_bias:
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for module in self.modules():
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if hasattr(module, "bias") and isinstance(
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module.bias, nn.Parameter):
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# Remove the bias term in Linear and LayerNorm.
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module.register_parameter("bias", None)
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def forward(
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self,
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input_ids: torch.Tensor,
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position_ids: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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hidden_states = self.wte(input_ids)
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for i in range(len(self.blocks)):
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block = self.blocks[i]
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hidden_states = block(
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position_ids,
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hidden_states,
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kv_caches[i],
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attn_metadata,
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)
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hidden_states = self.norm_f(hidden_states)
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return hidden_states
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class MPTForCausalLM(nn.Module):
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def __init__(
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self,
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config: MPTConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.config = config
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assert config.tie_word_embeddings
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self.quant_config = quant_config
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self.transformer = MPTModel(config, quant_config)
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self.lm_head_weight = self.transformer.wte.weight
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.sampler = Sampler()
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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hidden_states = self.transformer(input_ids, positions, kv_caches,
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attn_metadata)
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return hidden_states
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def compute_logits(self, hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> torch.Tensor:
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logits = self.logits_processor(self.lm_head_weight, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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for name, loaded_weight in weights:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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