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Reimplements the original IvmeConversateV2 architecture as a PreTrainedModel
so it works with AutoModelForCausalLM, .generate(), and safetensors. Math
(RMSNorm, RoPE, SwiGLU, tied embeddings, full causal attention) is unchanged
from the original; adds an optional KV cache for efficient generation.
"""
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, GenerationMixin
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.cache_utils import Cache, DynamicCache
from .configuration_ivme import IvmeConfig
class IvmeRMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
dtype = x.dtype
x = x.float()
rms = torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
out = x * rms
return (out.to(dtype)) * self.weight
def _precompute_rope_freqs(head_dim: int, max_seq_len: int, theta: float, device=None):
assert head_dim % 2 == 0, "RoPE requires an even head_dim"
freqs = 1.0 / (theta ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim))
positions = torch.arange(max_seq_len, device=device).float()
angles = torch.outer(positions, freqs)
return torch.polar(torch.ones_like(angles), angles)
def _apply_rope(x: torch.Tensor, rope_freqs: torch.Tensor) -> torch.Tensor:
B, H, T, D = x.shape
x_complex = torch.view_as_complex(x.float().reshape(B, H, T, D // 2, 2))
freqs = rope_freqs.view(1, 1, T, D // 2)
x_rotated = x_complex * freqs
out = torch.view_as_real(x_rotated).reshape(B, H, T, D)
return out.type_as(x)
class IvmeSelfAttention(nn.Module):
def __init__(self, config: IvmeConfig, layer_idx: int):
super().__init__()
self.layer_idx = layer_idx
hidden_dim = config.hidden_dim
self.n_heads = config.n_heads
self.head_dim = hidden_dim // config.n_heads
self.dropout = config.dropout
self.q_proj = nn.Linear(hidden_dim, hidden_dim, bias=False)
self.k_proj = nn.Linear(hidden_dim, hidden_dim, bias=False)
self.v_proj = nn.Linear(hidden_dim, hidden_dim, bias=False)
self.out_proj = nn.Linear(hidden_dim, hidden_dim, bias=False)
def forward(self, x, rope_freqs, past_key_value=None):
B, T, C = x.shape
q = self.q_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
k = self.k_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
v = self.v_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
q = _apply_rope(q, rope_freqs)
k = _apply_rope(k, rope_freqs)
if past_key_value is not None:
k, v = past_key_value.update(k, v, self.layer_idx)
is_causal = past_key_value is None or k.shape[2] == q.shape[2]
out = F.scaled_dot_product_attention(
q, k, v, is_causal=is_causal,
dropout_p=self.dropout if self.training else 0.0,
)
out = out.transpose(1, 2).contiguous().view(B, T, C)
return self.out_proj(out)
class IvmeSwiGLU(nn.Module):
def __init__(self, config: IvmeConfig):
super().__init__()
hidden_dim = config.hidden_dim
inner_dim = int(hidden_dim * config.ffn_mult * 2 / 3)
inner_dim = ((inner_dim + 7) // 8) * 8
self.gate_proj = nn.Linear(hidden_dim, inner_dim, bias=False)
self.up_proj = nn.Linear(hidden_dim, inner_dim, bias=False)
self.down_proj = nn.Linear(inner_dim, hidden_dim, bias=False)
def forward(self, x):
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
class IvmeBlock(nn.Module):
def __init__(self, config: IvmeConfig, layer_idx: int):
super().__init__()
self.attn_norm = IvmeRMSNorm(config.hidden_dim, eps=config.norm_eps)
self.attn = IvmeSelfAttention(config, layer_idx)
self.ffn_norm = IvmeRMSNorm(config.hidden_dim, eps=config.norm_eps)
self.ffn = IvmeSwiGLU(config)
def forward(self, x, rope_freqs, past_key_value=None):
x = x + self.attn(self.attn_norm(x), rope_freqs, past_key_value=past_key_value)
x = x + self.ffn(self.ffn_norm(x))
return x
class IvmePreTrainedModel(PreTrainedModel):
config_class = IvmeConfig
base_model_prefix = "model"
supports_gradient_checkpointing = False
_no_split_modules = ["IvmeBlock"]
_supports_cache_class = True
_supports_sdpa = True
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
class IvmeModel(IvmePreTrainedModel):
def __init__(self, config: IvmeConfig):
super().__init__(config)
self.tok_embed = nn.Embedding(config.vocab_size, config.hidden_dim)
self.blocks = nn.ModuleList(
[IvmeBlock(config, layer_idx=i) for i in range(config.n_layers)]
)
self.final_norm = IvmeRMSNorm(config.hidden_dim, eps=config.norm_eps)
self.post_init()
def get_input_embeddings(self):
return self.tok_embed
def set_input_embeddings(self, value):
self.tok_embed = value
def forward(self, input_ids, past_key_values=None, use_cache=False, **kwargs):
B, T = input_ids.shape
past_len = 0
if past_key_values is not None and len(past_key_values) > 0:
past_len = past_key_values.get_seq_length()
if past_len + T > self.config.context_len:
raise ValueError(
f"sequence length {past_len + T} exceeds context_len {self.config.context_len}"
)
full_rope_freqs = _precompute_rope_freqs(
self.config.head_dim, self.config.context_len, self.config.rope_theta,
device=input_ids.device,
)
rope_freqs = full_rope_freqs[past_len: past_len + T]
x = self.tok_embed(input_ids)
for block in self.blocks:
x = block(x, rope_freqs, past_key_value=past_key_values)
x = self.final_norm(x)
return x
class IvmeForCausalLM(IvmePreTrainedModel, GenerationMixin):
_tied_weights_keys = {"lm_head.weight": "model.tok_embed.weight"}
def __init__(self, config: IvmeConfig):
super().__init__(config)
self.model = IvmeModel(config)
self.lm_head = nn.Linear(config.hidden_dim, config.vocab_size, bias=False)
self.post_init()
if config.tie_word_embeddings:
self.tie_weights()
def get_input_embeddings(self):
return self.model.tok_embed
def set_input_embeddings(self, value):
self.model.tok_embed = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def forward(
self, input_ids, attention_mask=None, past_key_values=None,
labels=None, use_cache=None, return_dict=True, **kwargs,
):
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
hidden_states = self.model(
input_ids,
past_key_values=past_key_values if use_cache else None,
use_cache=use_cache,
)
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss = F.cross_entropy(
shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1),
ignore_index=-100,
)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=past_key_values if use_cache else None,
)
__all__ = ["IvmeConfig", "IvmeModel", "IvmeForCausalLM"]
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