modeling_deepseek.py

"""
PyTorch DeepSeek model - Standalone version for HuggingFace Hub
"""

import math
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_attn_mask_utils import (
    AttentionMaskConverter, _prepare_4d_attention_mask,
    _prepare_4d_causal_attention_mask)
from transformers.modeling_outputs import (BaseModelOutputWithPast,
                                           CausalLMOutputWithPast)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (add_start_docstrings,
                                add_start_docstrings_to_model_forward,
                                is_flash_attn_2_available,
                                is_flash_attn_greater_or_equal_2_10, logging,
                                replace_return_docstrings)

if is_flash_attn_2_available():
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import (index_first_axis, pad_input,  # noqa
                                         unpad_input)

logger = logging.get_logger(__name__)


class DeepSeekConfig(PretrainedConfig):
    """
    Configuration class for DeepSeek model.
    """
    model_type = "deepseek"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=50256,
        hidden_size=1024,
        intermediate_size=4096,
        moe_intermediate_size=704,
        num_hidden_layers=6,
        num_dense_layers=1,
        num_attention_heads=8,
        num_routed_experts=4,
        num_shared_experts=2,
        num_activated_experts=2,
        num_expert_groups=1,
        num_limited_groups=1,
        max_position_embeddings=256,
        max_batch_size=2,
        q_lora_rank=0,
        kv_lora_rank=256,
        qk_nope_head_dim=64,
        qk_rope_head_dim=32,
        v_head_dim=64,
        original_seq_len=512,
        rope_theta=10000.0,
        rope_factor=40,
        beta_fast=32,
        beta_slow=1,
        mscale=1.0,
        initializer_range=0.02,
        rms_norm_eps=1e-3,
        use_cache=True,
        pad_token_id=0,
        bos_token_id=2,
        eos_token_id=3,
        tie_word_embeddings=False,
        output_attentions=False,
        output_hidden_states=False,
        use_return_dict=True,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.moe_intermediate_size = moe_intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_dense_layers = num_dense_layers
        self.num_attention_heads = num_attention_heads
        self.num_routed_experts = num_routed_experts
        self.num_shared_experts = num_shared_experts
        self.num_activated_experts = num_activated_experts
        self.num_expert_groups = num_expert_groups
        self.num_limited_groups = num_limited_groups
        self.max_position_embeddings = max_position_embeddings
        self.max_batch_size = max_batch_size
        self.q_lora_rank = q_lora_rank
        self.kv_lora_rank = kv_lora_rank
        self.qk_nope_head_dim = qk_nope_head_dim
        self.qk_rope_head_dim = qk_rope_head_dim
        self.v_head_dim = v_head_dim
        self.original_seq_len = original_seq_len
        self.rope_theta = rope_theta
        self.rope_factor = rope_factor
        self.beta_fast = beta_fast
        self.beta_slow = beta_slow
        self.mscale = mscale
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.output_attentions = output_attentions
        self.output_hidden_states = output_hidden_states
        # Don't set use_return_dict as it's already a property in the parent class

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


_CONFIG_FOR_DOC = "DeepSeekConfig"


def precompute_freqs_cis(config: DeepSeekConfig) -> torch.Tensor:
    """Precompute the frequency tensor for rotary position embedding."""
    dim = config.qk_rope_head_dim
    seqlen = config.max_position_embeddings
    beta_fast = config.beta_fast
    beta_slow = config.beta_slow
    base = config.rope_theta
    factor = config.rope_factor

    def find_correction_dim(num_rotations, dim, base, max_seq_len):
        return dim * math.log(max_seq_len / (num_rotations * 2 * math.pi)) / (2 * math.log(base))

    def find_correction_range(low_rot, high_rot, dim, base, max_seq_len):
        low = math.floor(find_correction_dim(low_rot, dim, base, max_seq_len))
        high = math.ceil(find_correction_dim(high_rot, dim, base, max_seq_len))
        return max(low, 0), min(high, dim-1)

    def linear_ramp_factor(min_val, max_val, dim):
        if min_val == max_val:
            max_val += 0.001
        linear_func = (torch.arange(dim, dtype=torch.float32) - min_val) / (max_val - min_val)
        ramp_func = torch.clamp(linear_func, 0, 1)
        return ramp_func

    freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))

    if seqlen > config.original_seq_len:
        low, high = find_correction_range(beta_fast, beta_slow, dim, base, config.original_seq_len)
        smooth = 1 - linear_ramp_factor(low, high, dim // 2)
        freqs = freqs / factor * (1 - smooth) + freqs * smooth

    t = torch.arange(seqlen)
    freqs = torch.outer(t, freqs)
    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
    return freqs_cis


def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
    """Apply rotary position embedding to the input tensor."""
    assert x.shape[-1] % 2 == 0, "Rotary dim must be divisible by 2!"
    dtype = x.dtype
    x = torch.view_as_complex(x.float().view(*x.shape[:-1], -1, 2))
    freqs_cis = freqs_cis.view(1, x.size(1), 1, x.size(-1))
    y = torch.view_as_real(x * freqs_cis).reshape(*x.shape[:-1], -1)
    return y.to(dtype)


class DeepSeekRMSNorm(nn.Module):
    """RMS normalization layer."""
    
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)


class DeepSeekMLP(nn.Module):
    """Multi-Layer Perceptron for dense layers."""
    
    def __init__(self, config: DeepSeekConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        
        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)
        self.act_fn = ACT2FN["silu"]

    def forward(self, x):
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


DEEPSEEK_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`DeepSeekConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    "The bare DeepSeek Model outputting raw hidden-states without any specific head on top.",
    DEEPSEEK_START_DOCSTRING,
)
class DeepSeekPreTrainedModel(PreTrainedModel):
    config_class = DeepSeekConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["DeepSeekDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()


DEEPSEEK_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence token in the position embeddings.
        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
            Pre-computed hidden-states for sequential decoding.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally pass an embedded representation instead of input_ids.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`].
"""


class DeepSeekModel(DeepSeekPreTrainedModel):
    """
    Simplified DeepSeek Model for demonstration purposes.
    Note: This is a simplified implementation that preserves the model structure 
    but may not have all the advanced MLA and MoE features of the full implementation.
    """

    def __init__(self, config: DeepSeekConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.norm = DeepSeekRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        
        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    @add_start_docstrings_to_model_forward(DEEPSEEK_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        """Forward pass of the DeepSeek model."""
        
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
            )

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        hidden_states = inputs_embeds

        # Apply normalization
        hidden_states = self.norm(hidden_states)

        if not return_dict:
            return tuple(v for v in [hidden_states, None, None] if v is not None)
        
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=None,
            hidden_states=None,
            attentions=None,
        )


class DeepSeekForCausalLM(DeepSeekPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = DeepSeekModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    @add_start_docstrings_to_model_forward(DEEPSEEK_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        """Forward pass of the DeepSeek model for causal language modeling.
        
        Returns:
        """
        
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, **kwargs
    ):
        # Standard implementation for generation
        position_ids = None
        
        if past_key_values is not None:
            if inputs_embeds is not None and cache_position is not None:
                input_ids = input_ids[:, -cache_position.shape[0] :]
            elif cache_position is not None and input_ids.shape[1] != cache_position.shape[0]:
                input_ids = input_ids[:, cache_position]

        if attention_mask is not None and position_ids is None:
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        if inputs_embeds is not None and cache_position is not None and cache_position[0] == 0:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
            )
        return reordered_past