chatglm.py 16.3 KB
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# coding=utf-8
# Adapted from
# https://github.com/THUDM/ChatGLM2-6B
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"""Inference-only ChatGLM model compatible with THUDM weights."""
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from typing import Iterable, List, Optional, Tuple
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import torch
from torch import nn
from torch.nn import LayerNorm
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import os
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import re
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig, LoRAConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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                                               QKVParallelLinear,
                                               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 (
    QuantizationConfig)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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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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    ParallelLMHead, 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 IntermediateTensors, SamplerOutput
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from vllm.transformers_utils.configs import ChatGLMConfig
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from vllm import _custom_ops as ops
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from vllm.model_executor.utils import pad_weight, gemm_bank_conf
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from .interfaces import SupportsLoRA

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class GLMAttention(nn.Module):

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    def __init__(
        self,
        config,
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        cache_config: Optional[CacheConfig] = None,
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        quant_config: Optional[QuantizationConfig] = None,
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    ):
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        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)
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        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)
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        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

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

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        # https://huggingface.co/THUDM/chatglm3-6b-32k/blob/e210410255278dd9d74463cf396ba559c0ef801c/modeling_chatglm.py#L141
        rope_ratio = getattr(config, "rope_ratio", 1.0)
        max_positions = getattr(config, "seq_length", 8192)
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        self.rotary_emb = get_rope(
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            self.head_dim,
            rotary_dim=self.head_dim // 2,
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            max_position=max_positions,
            base=10000 * rope_ratio,
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            is_neox_style=False,
        )
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        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              self.scaling,
                              num_kv_heads=self.num_kv_heads,
                              cache_config=cache_config,
                              quant_config=quant_config)
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        self.quant_method = None
        if quant_config is not None:
            self.quant_method=quant_config.get_name()
            self.quant_config=quant_config
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    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: torch.Tensor,
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        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
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    ) -> torch.Tensor:
        qkv, _ = self.query_key_value(hidden_states)
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        if os.environ.get('FA_PAD') == '1' and self.quant_method is None:
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            qkv = qkv[...,:-32]
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        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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        q, k = self.rotary_emb(position_ids, q, k)
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        context_layer = self.attn(
            q,
            k,
            v,
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            kv_cache,
            attn_metadata,
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        )
        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.
    """

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    def __init__(
        self,
        config,
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        quant_config: Optional[QuantizationConfig] = None,
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    ):
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        super().__init__()

        self.add_bias = config.add_bias_linear

        # Project to 4h.
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        self.dense_h_to_4h = MergedColumnParallelLinear(
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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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            quant_config=quant_config,
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        )

        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,
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            quant_config=quant_config,
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        )

    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,
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        cache_config: Optional[CacheConfig] = None,
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        quant_config: Optional[QuantizationConfig] = None,
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    ):
        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.
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        self.self_attention = GLMAttention(config, cache_config, quant_config)
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        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
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        self.mlp = GLMMLP(config, quant_config)
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    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: torch.Tensor,
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        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
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    ) -> 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,
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            attn_metadata=attn_metadata,
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        )

        # 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."""

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    def __init__(
        self,
        config,
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        cache_config: Optional[CacheConfig] = None,
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        quant_config: Optional[QuantizationConfig] = None,
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    ):
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        super().__init__()
        self.post_layer_norm = config.post_layer_norm

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

        # Transformer layers.
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        self.layers = nn.ModuleList([
            GLMBlock(config, cache_config, quant_config)
            for i in range(self.num_layers)
        ])
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        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,
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        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
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    ) -> torch.Tensor:
        for i in range(self.num_layers):
            layer = self.layers[i]
            hidden_states = layer(
                hidden_states=hidden_states,
                position_ids=position_ids,
                kv_cache=kv_caches[i],
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                attn_metadata=attn_metadata,
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            )
        # Final layer norm.
        if self.post_layer_norm:
            hidden_states = self.final_layernorm(hidden_states)

        return hidden_states


class ChatGLMModel(nn.Module):

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    def __init__(
        self,
        config,
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        cache_config: Optional[CacheConfig] = None,
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        quant_config: Optional[QuantizationConfig] = None,
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    ):
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        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
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        self.encoder = GLMTransformer(config, cache_config, quant_config)
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        self.output_layer = ParallelLMHead(config.padded_vocab_size,
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                                           config.hidden_size,
                                           quant_config=quant_config)
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    def forward(
        self,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
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        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
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    ) -> torch.Tensor:
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        inputs_embeds = self.embedding(input_ids)

        # Run encoder.
        hidden_states = self.encoder(
            hidden_states=inputs_embeds,
            position_ids=position_ids,
            kv_caches=kv_caches,
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            attn_metadata=attn_metadata,
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        )
        return hidden_states


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class ChatGLMForCausalLM(nn.Module, SupportsLoRA):
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    packed_modules_mapping = {
        "query_key_value": ["query_key_value"],
        "dense_h_to_4h": ["dense_h_to_4h"]
    }
    # LoRA specific attributes
    supported_lora_modules = [
        "query_key_value",
        "dense",
        "dense_h_to_4h",
        "dense_4h_to_h",
    ]
    embedding_modules = {}
    embedding_padding_modules = []
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    def __init__(
        self,
        config: ChatGLMConfig,
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        cache_config: Optional[CacheConfig] = None,
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        quant_config: Optional[QuantizationConfig] = None,
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        lora_config: Optional[LoRAConfig] = None,
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    ):
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        super().__init__()
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        self.config = config
        self.lora_config = lora_config

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        self.quant_config = quant_config
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        self.max_position_embeddings = getattr(config, "max_sequence_length",
                                               8192)
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        self.transformer = ChatGLMModel(config, cache_config, quant_config)
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        self.lm_head = self.transformer.output_layer
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        self.logits_processor = LogitsProcessor(config.padded_vocab_size)
        self.sampler = Sampler()
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        self.quant_method = None
        if quant_config is not None:
            self.quant_method=quant_config.get_name()
            self.quant_config=quant_config

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        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
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        self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
        self.use_fa_pad = os.environ.get('FA_PAD') == '1'
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    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
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        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
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        intermediate_tensors: Optional[IntermediateTensors] = None,
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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,
                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
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        logits = self.logits_processor(self.lm_head, hidden_states,
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                                       sampling_metadata)
        return logits

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    def sample(
        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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            if "rotary_pos_emb.inv_freq" in name:
                continue
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            if "word_embeddings" in name:
                name = name.replace(".word_embeddings", "")
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            # Skip loading extra bias for GPTQ models.
            if name.endswith(".bias") and name not in params_dict:
                continue
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            param = params_dict[name]
            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)
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        if self.use_llama_nn and self.quant_method is None:
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            lay_key_words = [
                "self_attention.query_key_value.weight",
                "self_attention.dense.weight",
                "mlp.dense_h_to_4h.weight",
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                "mlp.dense_4h_to_h.weight",
                "lm_head.weight"
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            ]
            combined_words = "|".join(lay_key_words)
            
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            lay_qkv_words = ["self_attention.query_key_value.weight"]   
            qkv_words = "|".join(lay_qkv_words)  
            
            lay_qkv_bias_words = ["self_attention.query_key_value.bias"]   
            qkv_bias_words = "|".join(lay_qkv_bias_words)
            
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            for layername, weight in params_dict.items():
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                if self.use_fa_pad and (re.findall(qkv_bias_words, layername)):
                    weight.data = pad_weight(weight.data, 32)
                    
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                matches = re.findall(combined_words, layername)
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                if matches:  
                    if self.use_gemm_pad and gemm_bank_conf(weight.data.shape[0]):
                        weight.data = pad_weight(weight.data, 32)  
                        
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                    if self.use_fa_pad and (re.findall(qkv_words, layername)):
                        if not gemm_bank_conf(weight.data.shape[0]):
                            weight.data = pad_weight(weight.data, 32)
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                    _weight = torch.zeros_like(weight.data)
                    ori_shape =_weight.shape
                    
                    ops.trans_w16_gemm(_weight, weight.data, _weight.shape[0], _weight.shape[1])
                    weight.data.copy_(_weight)
                    
                    weight.data=weight.data.reshape(ori_shape[1], -1)