chatglm.py

# coding=utf-8
# Adapted from
# https://github.com/THUDM/ChatGLM2-6B
"""Inference-only ChatGLM model compatible with THUDM weights."""
from typing import List, Optional, Tuple

import torch
from torch import nn
from torch.nn import LayerNorm

from vllm.model_executor.input_metadata import InputMetadata
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (LinearMethodBase,
                                               MergedColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
    VocabParallelEmbedding, ParallelLMHead)
from vllm.model_executor.parallel_utils.parallel_state import (
    get_tensor_model_parallel_world_size)
from vllm.model_executor.weight_utils import (default_weight_loader,
                                              hf_model_weights_iterator)
from vllm.sequence import SamplerOutput
from vllm.transformers_utils.configs import ChatGLMConfig

KVCache = Tuple[torch.Tensor, torch.Tensor]


class GLMAttention(nn.Module):

    def __init__(
        self,
        config,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.hidden_size = config.hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = config.num_attention_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.multi_query_attention = config.multi_query_attention
        self.total_num_kv_heads = (config.multi_query_group_num
                                   if config.multi_query_attention else
                                   config.num_attention_heads)
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = config.hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5

        self.query_key_value = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=config.add_bias_linear or config.add_qkv_bias,
            linear_method=linear_method,
        )
        self.dense = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            config.hidden_size,
            bias=config.add_bias_linear,
            linear_method=linear_method,
        )

        self.attn = PagedAttentionWithRoPE(
            self.num_heads,
            self.head_dim,
            self.scaling,
            rotary_dim=self.head_dim // 2,
            num_kv_heads=self.num_kv_heads,
            is_neox_style=False,
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
        cache_event: Optional[torch.cuda.Event],
    ) -> torch.Tensor:
        qkv, _ = self.query_key_value(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        key_cache, value_cache = kv_cache

        context_layer = self.attn(
            position_ids,
            q,
            k,
            v,
            key_cache,
            value_cache,
            input_metadata,
            cache_event,
        )

        attn_output, _ = self.dense(context_layer)

        return attn_output


class GLMMLP(nn.Module):
    """MLP.

    MLP will take the input with h hidden state, project it to 4*h
    hidden dimension, perform nonlinear transformation, and project the
    state back into h hidden dimension.
    """

    def __init__(
        self,
        config,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()

        self.add_bias = config.add_bias_linear

        # Project to 4h.
        self.dense_h_to_4h = MergedColumnParallelLinear(
            config.hidden_size,
            [config.ffn_hidden_size] * 2,
            bias=config.add_bias_linear,
            linear_method=linear_method,
        )

        self.activation_func = SiluAndMul()

        # Project back to h.
        self.dense_4h_to_h = RowParallelLinear(
            config.ffn_hidden_size,
            config.hidden_size,
            bias=config.add_bias_linear,
            linear_method=linear_method,
        )

    def forward(self, hidden_states):
        # [s, b, 4hp]
        intermediate_parallel, _ = self.dense_h_to_4h(hidden_states)
        intermediate_parallel = self.activation_func(intermediate_parallel)
        # [s, b, h]
        output, _ = self.dense_4h_to_h(intermediate_parallel)
        return output


class GLMBlock(nn.Module):
    """A single transformer layer.

    Transformer layer takes input with size [s, b, h] and returns an
    output of the same size.
    """

    def __init__(
        self,
        config,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.apply_residual_connection_post_layernorm = (
            config.apply_residual_connection_post_layernorm)

        self.fp32_residual_connection = config.fp32_residual_connection

        layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
        # Layernorm on the input data.
        self.input_layernorm = layer_norm_func(config.hidden_size,
                                               eps=config.layernorm_epsilon)

        # Self attention.
        self.self_attention = GLMAttention(config, linear_method)
        self.hidden_dropout = config.hidden_dropout

        # Layernorm on the attention output
        self.post_attention_layernorm = layer_norm_func(
            config.hidden_size, eps=config.layernorm_epsilon)

        # MLP
        self.mlp = GLMMLP(config, linear_method)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
        cache_event: Optional[torch.cuda.Event],
    ) -> torch.Tensor:
        # hidden_states: [num_tokens, h]
        # Layer norm at the beginning of the transformer layer.
        layernorm_output = self.input_layernorm(hidden_states)
        # Self attention.
        attention_output = self.self_attention(
            hidden_states=layernorm_output,
            position_ids=position_ids,
            kv_cache=kv_cache,
            input_metadata=input_metadata,
            cache_event=cache_event,
        )

        # Residual connection.
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = hidden_states

        layernorm_input = residual + attention_output

        # Layer norm post the self attention.
        layernorm_output = self.post_attention_layernorm(layernorm_input)

        # Second residual connection.
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = layernorm_input

        output = self.mlp(layernorm_output) + residual

        return output


class GLMTransformer(nn.Module):
    """Transformer class."""

    def __init__(
        self,
        config,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.post_layer_norm = config.post_layer_norm

        # Number of layers.
        self.num_layers = config.num_layers

        # Transformer layers.
        self.layers = nn.ModuleList(
            [GLMBlock(config, linear_method) for i in range(self.num_layers)])

        if self.post_layer_norm:
            layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
            # Final layer norm before output.
            self.final_layernorm = layer_norm_func(
                config.hidden_size, eps=config.layernorm_epsilon)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: torch.Tensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ) -> torch.Tensor:
        for i in range(self.num_layers):
            cache_event = None if cache_events is None else cache_events[i]
            layer = self.layers[i]
            hidden_states = layer(
                hidden_states=hidden_states,
                position_ids=position_ids,
                kv_cache=kv_caches[i],
                input_metadata=input_metadata,
                cache_event=cache_event,
            )
        # Final layer norm.
        if self.post_layer_norm:
            hidden_states = self.final_layernorm(hidden_states)

        return hidden_states


class ChatGLMModel(nn.Module):

    def __init__(
        self,
        config,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()

        self.embedding = VocabParallelEmbedding(config.padded_vocab_size,
                                                config.hidden_size)

        self.num_layers = config.num_layers
        self.multi_query_group_num = config.multi_query_group_num
        self.kv_channels = config.kv_channels
        self.encoder = GLMTransformer(config, linear_method)

        self.output_layer = ParallelLMHead(config.padded_vocab_size,
                                           config.hidden_size)

    def forward(
        self,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ):
        inputs_embeds = self.embedding(input_ids)

        # Run encoder.
        hidden_states = self.encoder(
            hidden_states=inputs_embeds,
            position_ids=position_ids,
            kv_caches=kv_caches,
            input_metadata=input_metadata,
            cache_events=cache_events,
        )

        return hidden_states


class ChatGLMForCausalLM(nn.Module):

    def __init__(
        self,
        config: ChatGLMConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.config: ChatGLMConfig = config
        self.linear_method = linear_method
        self.transformer = ChatGLMModel(config, linear_method)
        self.lm_head_weight = self.transformer.output_layer.weight
        self.sampler = Sampler(config.padded_vocab_size)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ) -> SamplerOutput:
        hidden_states = self.transformer(input_ids, positions, kv_caches,
                                         input_metadata, cache_events)
        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
                                   input_metadata)
        return next_tokens

    def load_weights(self,
                     model_name_or_path: str,
                     cache_dir: Optional[str] = None,
                     load_format: str = "auto",
                     revision: Optional[str] = None):
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        for name, loaded_weight in hf_model_weights_iterator(
                model_name_or_path, cache_dir, load_format, revision):
            if "rotary_pos_emb.inv_freq" in name:
                continue
            if "word_embeddings" in name:
                name = name.replace(".word_embeddings", "")
            param = params_dict[name]
            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)