HuatuoGPT-3-7B-Pangu/modular_openpangu_dense.py

# coding=utf-8
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All rights reserved.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Callable, Optional, Tuple

import torch
from torch import nn

try:
    import torch_npu
    from torch_npu.contrib import transfer_to_npu
    if "910" in torch.npu.get_device_name():
        NPU_ATTN_INFR = True
        print("[INFO] torch_npu detected. Using NPU fused infer attention.")
    else:
        NPU_ATTN_INFR = False
except ImportError:
    NPU_ATTN_INFR = False

from transformers.cache_utils import Cache
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
from transformers.processing_utils import Unpack
from transformers.utils import logging
from transformers.models.llama.modeling_llama import (
    LlamaAttention,
    LlamaDecoderLayer,
    LlamaForCausalLM,
    LlamaForSequenceClassification,
    LlamaMLP,
    LlamaModel,
    apply_rotary_pos_emb,
    eager_attention_forward,
)
from .configuration_openpangu_dense import PanguEmbeddedConfig


logger = logging.get_logger(__name__)


class PanguEmbeddedMLP(LlamaMLP):
    def __init__(self, config):
        super().__init__(config)
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)


class PanguEmbeddedAttention(LlamaAttention):
    def __init__(self, config: PanguEmbeddedConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_heads = config.num_attention_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = True

        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.bias)
        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias)
        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.bias)
        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.bias)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor],
        past_key_value: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
            # sin and cos are specific to RoPE models; cache_position needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
        
        if not self.training and NPU_ATTN_INFR:
            q_len = input_shape[1]
            if attention_mask is not None:
                attention_mask = ~attention_mask.bool()
            elif q_len > 1:
                attention_mask = torch.triu(torch.ones([q_len, q_len]), diagonal=1).bool().unsqueeze(0).unsqueeze(0).to(query_states.device)

            attn_output, _ = torch_npu.npu_fused_infer_attention_score(
                query_states, key_states, value_states,
                num_heads=self.num_heads, num_key_value_heads=self.num_key_value_heads,
                input_layout="BNSD", atten_mask=attention_mask, scale=self.scaling)
            attn_output = attn_output.transpose(1, 2)
            attn_weights = None
        else:
            attn_output, attn_weights = attention_interface(
                self,
                query_states,
                key_states,
                value_states,
                attention_mask,
                dropout=0.0 if not self.training else self.attention_dropout,
                scaling=self.scaling,
                **kwargs,
            )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights
            

class PanguEmbeddedDecoderLayer(LlamaDecoderLayer):
    pass
    

class PanguEmbeddedModel(LlamaModel):
    pass


class PanguEmbeddedForCausalLM(LlamaForCausalLM):
    pass