409 lines
14 KiB
Python
409 lines
14 KiB
Python
# coding=utf-8
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# Adapted from
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# https://github.com/THUDM/ChatGLM2-6B
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"""Inference-only ChatGLM model compatible with THUDM weights.
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The input of the model is flattened to a 1D tensor of tokens. The model uses
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InputMetadata to extract the original 2D shape of the input.
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"""
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from typing import Dict, List, Optional, Tuple
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import torch
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from torch import nn
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from torch.nn import LayerNorm
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from vllm.model_executor.input_metadata import InputMetadata
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from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.weight_utils import (
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hf_model_weights_iterator,
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load_tensor_parallel_weights,
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)
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from vllm.model_executor.parallel_utils.parallel_state import (
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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from vllm.model_executor.parallel_utils.layers import VocabParallelEmbedding
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from vllm.model_executor.parallel_utils.layers import (
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ColumnParallelLinear,
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RowParallelLinear,
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)
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from vllm.sequence import SequenceOutputs
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from vllm.transformers_utils.configs import ChatGLMConfig
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class GLMAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.multi_query_attention = config.multi_query_attention
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self.total_num_kv_heads = (config.multi_query_group_num
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if config.multi_query_attention else
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config.num_attention_heads)
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assert self.total_num_kv_heads % tp_size == 0
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self.num_kv_heads = self.total_num_kv_heads // tp_size
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self.head_dim = config.hidden_size // self.total_num_heads
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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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self.scaling = self.head_dim**-0.5
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self.query_key_value = ColumnParallelLinear(
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config.hidden_size,
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(self.total_num_heads + 2 * self.total_num_kv_heads) *
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self.head_dim,
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bias=config.add_qkv_bias,
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gather_output=False,
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)
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self.dense = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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config.hidden_size,
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bias=config.add_bias_linear,
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input_is_parallel=True,
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)
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self.attn = PagedAttentionWithRoPE(
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self.num_heads,
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self.head_dim,
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self.scaling,
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rotary_dim=self.head_dim // 2,
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num_kv_heads=self.num_kv_heads,
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is_neox_style=False,
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# is_glm_style=True
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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qkv, _ = self.query_key_value(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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key_cache, value_cache = kv_cache
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context_layer = self.attn(
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position_ids,
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q,
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k,
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v,
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key_cache,
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value_cache,
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input_metadata,
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cache_event,
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)
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attn_output, _ = self.dense(context_layer)
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return attn_output
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class GLMMLP(nn.Module):
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"""MLP.
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MLP will take the input with h hidden state, project it to 4*h
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hidden dimension, perform nonlinear transformation, and project the
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state back into h hidden dimension.
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"""
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def __init__(self, config):
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super().__init__()
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self.add_bias = config.add_bias_linear
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# Project to 4h.
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self.dense_h_to_4h = ColumnParallelLinear(
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config.hidden_size,
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config.ffn_hidden_size * 2,
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bias=config.add_bias_linear,
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gather_output=False,
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)
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self.activation_func = SiluAndMul()
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# Project back to h.
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self.dense_4h_to_h = RowParallelLinear(
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config.ffn_hidden_size,
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config.hidden_size,
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bias=config.add_bias_linear,
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input_is_parallel=True,
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)
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def forward(self, hidden_states):
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# [s, b, 4hp]
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intermediate_parallel, _ = self.dense_h_to_4h(hidden_states)
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intermediate_parallel = self.activation_func(intermediate_parallel)
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# [s, b, h]
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output, _ = self.dense_4h_to_h(intermediate_parallel)
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return output
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class GLMBlock(nn.Module):
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"""A single transformer layer.
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Transformer layer takes input with size [s, b, h] and returns an
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output of the same size.
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"""
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def __init__(
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self,
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config,
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):
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super().__init__()
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self.apply_residual_connection_post_layernorm = (
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config.apply_residual_connection_post_layernorm)
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self.fp32_residual_connection = config.fp32_residual_connection
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layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
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# Layernorm on the input data.
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self.input_layernorm = layer_norm_func(config.hidden_size,
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eps=config.layernorm_epsilon)
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# Self attention.
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self.self_attention = GLMAttention(config)
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self.hidden_dropout = config.hidden_dropout
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# Layernorm on the attention output
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self.post_attention_layernorm = layer_norm_func(
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config.hidden_size, eps=config.layernorm_epsilon)
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# MLP
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self.mlp = GLMMLP(config)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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# hidden_states: [num_tokens, h]
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# Layer norm at the beginning of the transformer layer.
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layernorm_output = self.input_layernorm(hidden_states)
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# Self attention.
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attention_output = self.self_attention(
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hidden_states=layernorm_output,
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position_ids=position_ids,
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kv_cache=kv_cache,
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input_metadata=input_metadata,
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cache_event=cache_event,
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)
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# Residual connection.
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = hidden_states
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layernorm_input = residual + attention_output
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# Layer norm post the self attention.
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layernorm_output = self.post_attention_layernorm(layernorm_input)
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# Second residual connection.
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = layernorm_input
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output = self.mlp(layernorm_output) + residual
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return output
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class GLMTransformer(nn.Module):
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"""Transformer class."""
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def __init__(self, config):
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super().__init__()
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self.post_layer_norm = config.post_layer_norm
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# Number of layers.
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self.num_layers = config.num_layers
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# Transformer layers.
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self.layers = nn.ModuleList(
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[GLMBlock(config) for i in range(self.num_layers)])
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if self.post_layer_norm:
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layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
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# Final layer norm before output.
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self.final_layernorm = layer_norm_func(
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config.hidden_size, eps=config.layernorm_epsilon)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> torch.Tensor:
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for i in range(self.num_layers):
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if cache_events is None:
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cache_event = None
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else:
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cache_event = cache_events[i]
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layer = self.layers[i]
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hidden_states = layer(
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hidden_states=hidden_states,
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position_ids=position_ids,
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kv_cache=kv_caches[i],
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input_metadata=input_metadata,
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cache_event=cache_event,
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)
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# Final layer norm.
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if self.post_layer_norm:
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states
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class ChatGLMModel(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.embedding = VocabParallelEmbedding(config.padded_vocab_size,
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config.hidden_size)
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self.num_layers = config.num_layers
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self.multi_query_group_num = config.multi_query_group_num
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self.kv_channels = config.kv_channels
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self.encoder = GLMTransformer(config)
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self.output_layer = ColumnParallelLinear(
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config.hidden_size,
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config.padded_vocab_size,
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bias=False,
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gather_output=False,
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params_dtype=config.torch_dtype,
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)
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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[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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):
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inputs_embeds = self.embedding(input_ids)
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# Run encoder.
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hidden_states = self.encoder(
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hidden_states=inputs_embeds,
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position_ids=position_ids,
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kv_caches=kv_caches,
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input_metadata=input_metadata,
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cache_events=cache_events,
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)
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return hidden_states
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class ChatGLMForCausalLM(nn.Module):
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def __init__(self, config: ChatGLMConfig):
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super().__init__()
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self.config: ChatGLMConfig = config
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self.transformer = ChatGLMModel(config)
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self.lm_head_weight = self.transformer.output_layer.weight
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self.sampler = Sampler(config.padded_vocab_size)
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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[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> Dict[int, SequenceOutputs]:
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hidden_states = self.transformer(input_ids, positions, kv_caches,
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input_metadata, cache_events)
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next_tokens = self.sampler(self.lm_head_weight, hidden_states,
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input_metadata)
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return next_tokens
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_column_parallel_weights = [
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"output_layer.weight",
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"embedding.weight",
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]
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_row_parallel_weights = ["dense_4h_to_h", "self_attention.dense"]
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def load_weights(
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self,
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model_name_or_path: str,
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cache_dir: Optional[str] = None,
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load_format: str = "auto",
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revision: Optional[str] = None,
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):
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tp_rank = get_tensor_model_parallel_rank()
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tp_size = get_tensor_model_parallel_world_size()
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q_proj_shard_size = self.config.hidden_size // tp_size
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kv_proj_shard_size = (self.config.hidden_size //
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self.config.num_attention_heads *
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self.config.multi_query_group_num // tp_size)
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mlp_hidden_shard_size = self.config.ffn_hidden_size // tp_size
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state_dict = self.state_dict()
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, load_format, revision):
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if "word_embeddings" in name:
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name = name.replace(".word_embeddings", "")
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if name in state_dict:
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param = state_dict[name]
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if "query_key_value" in name:
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q_offset = q_proj_shard_size * tp_rank
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k_offset = (q_proj_shard_size * tp_size +
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kv_proj_shard_size * tp_rank)
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v_offset = (q_proj_shard_size * tp_size +
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kv_proj_shard_size * (tp_size + tp_rank))
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wq = loaded_weight[q_offset:q_offset + q_proj_shard_size]
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wk = loaded_weight[k_offset:k_offset + kv_proj_shard_size]
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wv = loaded_weight[v_offset:v_offset + kv_proj_shard_size]
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loaded_weight = torch.cat([wq, wk, wv], dim=0)
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param.data.copy_(loaded_weight)
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continue
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if "dense_h_to_4h" in name:
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w_gate = loaded_weight[mlp_hidden_shard_size *
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tp_rank:mlp_hidden_shard_size *
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(tp_rank + 1)]
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w_proj = loaded_weight[mlp_hidden_shard_size *
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(tp_size +
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tp_rank):mlp_hidden_shard_size *
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(tp_size + tp_rank + 1)]
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loaded_weight = torch.cat([w_gate, w_proj], dim=0)
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param.data.copy_(loaded_weight)
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continue
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load_tensor_parallel_weights(
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param,
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loaded_weight,
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name,
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self._column_parallel_weights,
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self._row_parallel_weights,
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tp_rank,
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)
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elif name == "transformer.rotary_pos_emb.inv_freq":
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continue
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else:
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print("Warning never found tensor's name:", name)
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