Upload SmalLmForCausalLM

README.md

@@ -2,6 +2,7 @@
 library_name: transformers
 tags:
 - generated_from_trainer
+- smallm
 model-index:
 - name: smallm_350
   results: []

config.json

@@ -4,6 +4,10 @@
   ],
   "attention_bias": false,
   "attention_dropout": 0.1,
+  "auto_map": {
+    "AutoConfig": "config.SmalLmConfig",
+    "AutoModelForCausalLM": "model.SmalLmForCausalLM"
+  },
   "balancing_coef": 0.0001,
   "bos_token_id": 1,
   "embedding_dropout": 0.0,
@@ -38,7 +42,7 @@
   "sliding_window_attention": true,
   "sliding_window_context": 1024,
   "sliding_window_period": 4,
-  "static_residual": false,
+  "static_residual": true,
   "token_experts": 3,
   "torch_dtype": "float32",
   "transformers_version": "4.50.3",

config.py

@@ -0,0 +1,134 @@
+import logging
+from transformers import PretrainedConfig
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+class SmalLmConfig(PretrainedConfig):
+    """
+    Base config for all SmalLm models
+
+    Raises:
+        ValueError: Positional_bias_type must be in suported types
+        ValueError: In case of rope positional_bias_type head_size can't be anything
+    """
+    model_type = "smallm"
+
+    def __init__(
+        self,
+        # global model params
+        hidden_size: int = 512,
+        intermediate_size: int = 2048,
+        mlp_bias: bool = False,
+        num_hidden_layers: int = 27,
+        rms_norm_eps: float = 1e-6,
+        rms_affine: bool = False,
+        initializer_range: float = 0.02,
+        output_hidden_states: bool = False,
+        output_attentions: bool = False,
+        use_cache: bool = True,
+        sliding_window_attention: bool = True,
+        sliding_window_context: int = 1024,
+        sliding_window_period: int = 4,
+        embedding_dropout: float = 0.0,
+        layer_dropout: float = 0.1,
+        max_seq_len: int = 2048,
+        original_seq_len: int | None = None,
+        tie_word_embeddings: bool = True,
+        # attention params
+        num_attention_heads: int = 9,
+        num_kv_heads: int = 3,
+        head_size: Optional[int] = None,
+        attention_dropout: float = 0.1,
+        positional_bias_type: str = "rope",
+        high_rotations: int = 32,
+        low_rotations: int = 1,
+        attention_bias: bool = False,
+        rope_base: int = 100000,
+        # MoE params
+        use_moe: bool = True,
+        moe_period: int = 3,
+        expert_size: int = 256,
+        shared_experts: int = 2,
+        routed_experts: int = 16,
+        token_experts: int = 4,
+        noisy_experts: bool = False,
+        moe_bias: bool = False,
+        balancing_coef: float = 1e-4,
+        no_moe_layers: int = 5,
+        # extra params
+        vocab_size: int = 60000,
+        bos_token_id: int = 1,
+        eos_token_id: int = 0,
+        pad_token_id: int = 0,
+        static_residual: bool = False,
+        **kwargs,
+    ):
+        if positional_bias_type not in ["alibi", "rope"]:
+            raise ValueError(
+                f"positional_bias_type must be 'alibi' or 'rope', got {positional_bias_type}"
+            )
+        self.static_residual = not static_residual
+        self.no_moe_layers = no_moe_layers
+        self.moe_bias = moe_bias
+        self.balancing_coef = balancing_coef
+        self.noisy_experts = noisy_experts
+        self.high_rotations = high_rotations
+        self.low_rotations = low_rotations
+        self.positional_bias_type = positional_bias_type
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.mlp_bias = mlp_bias
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_kv_heads = num_kv_heads
+        self.attention_dropout = attention_dropout
+        self.rms_norm_eps = rms_norm_eps
+        self.max_seq_len = max_seq_len
+        self.use_cache = use_cache
+        self.initializer_range = initializer_range
+        self.embedding_dropout = embedding_dropout
+        self.rms_affine = rms_affine
+        self.output_hidden_states = output_hidden_states
+        self.output_attentions = output_attentions
+        self.layer_dropout = layer_dropout
+        self.use_moe = use_moe
+        self.moe_period = moe_period
+        self.expert_size = expert_size
+        self.shared_experts = shared_experts
+        self.routed_experts = routed_experts
+        self.token_experts = token_experts
+        self.intermediate_size = intermediate_size
+        self.attention_bias = attention_bias
+        self.rope_base = rope_base
+        self.head_size = head_size if head_size else hidden_size // num_attention_heads
+        self.original_seq_len = (
+            original_seq_len if original_seq_len is not None else max_seq_len
+        )
+
+        self.sliding_window_attention = sliding_window_attention
+        self.sliding_window_context = sliding_window_context
+        self.sliding_window_period = sliding_window_period
+        if sliding_window_attention and sliding_window_context > max_seq_len:
+            logger.warning(
+                f"sliding_window_context more than max_seq_len, \
+                    set sliding_window_context to {max_seq_len}"
+            )
+            self.sliding_window_context = max_seq_len
+        if not sliding_window_attention:
+            self.sliding_window_context = max_seq_len
+
+        if self.head_size % 2 != 0 and self.positional_bias_type == "rope":
+            raise ValueError("Head size should divided by 2")
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+__all__ = ["SmalLmConfig"]

model.py

@@ -0,0 +1,882 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from transformers import PreTrainedModel, GenerationMixin
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from .config import SmalLmConfig
+from typing import Optional
+import logging
+from einops import rearrange
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from einops._torch_specific import allow_ops_in_compiled_graph
+
+allow_ops_in_compiled_graph()
+from transformers.utils import is_flash_attn_2_available
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_varlen_func
+    from flash_attn.bert_padding import unpad_input, pad_input
+
+
+logger = logging.getLogger(__name__)
+
+
+class SwiGLU(nn.Module):
+    def __init__(
+        self, input_size: int, hidden_size: int, bias: bool = False, *args, **kwargs
+    ):
+        super().__init__(*args, **kwargs)
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.up_proj = nn.Linear(input_size, hidden_size * 2, bias=bias)
+        self.down_proj = nn.Linear(hidden_size, input_size, bias=bias)
+
+    def forward(self, x):
+        up_gate = self.up_proj(x)
+        up, gate = rearrange(up_gate, "... (d span) -> span ... d", d=self.hidden_size)
+        down = F.silu(gate) * up
+        return self.down_proj(down)
+
+
+class Router(nn.Module):
+    """
+    Router for distribution of tokens by experts in MoE
+    """
+    def __init__(self, config: SmalLmConfig, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.config = config
+        self.experts_to_select = self.config.token_experts - self.config.shared_experts
+        self.gate = nn.Linear(config.hidden_size, config.routed_experts, bias=False)
+        self.gate_noise = (
+            nn.Linear(config.hidden_size, config.routed_experts, bias=False)
+            if config.noisy_experts is True
+            else None
+        )
+        self.bias_coef = config.balancing_coef
+        self.register_buffer(
+            "bias", torch.zeros(config.routed_experts), persistent=True
+        )
+        self.register_buffer(
+            "expert_counts", torch.zeros(config.routed_experts), persistent=False
+        )
+
+    def forward(self, x: torch.Tensor) -> tuple[torch.Tensor]:
+        # calculating with fp32 for stability
+        # num_tokens n_shared_experts
+        gate_logits = self.gate(x)
+        if self.gate_noise is not None:
+            gate_logits_noise = F.softplus(self.gate_noise(x))
+            gate_logits_noise = torch.randn_like(gate_logits_noise) * gate_logits_noise
+            gate_logits = gate_logits + gate_logits_noise
+
+        gate_weights = gate_logits.sigmoid()
+        original_weights = gate_weights
+
+        gate_weights = gate_weights + self.bias
+
+        _, top_experts_idx = torch.topk(gate_weights, self.experts_to_select, dim=-1)
+        counts = torch.bincount(
+            top_experts_idx.flatten(), minlength=self.config.routed_experts
+        ).detach()
+        if self.training:
+            self.expert_counts += counts
+        top_experts_weights = original_weights.gather(1, top_experts_idx)
+        top_experts_weights = top_experts_weights / top_experts_weights.sum(
+            dim=-1, keepdim=True
+        )
+        return top_experts_idx, top_experts_weights.type_as(x), counts.tolist()
+
+    def update_bias(self):
+        mean = self.expert_counts.float().mean()
+        delta = self.bias_coef * torch.sign(mean - self.expert_counts)
+        self.bias += delta
+        self.expert_counts.zero_()
+
+
+class MoE(nn.Module):
+    """
+    MoE experts, contains shared and routed experts,
+    like DeepSeek MoE, also use Auxiliary-Loss-Free Load Balancing
+    ref: https://arxiv.org/abs/2408.15664
+    """
+    def __init__(self, config: SmalLmConfig, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.config = config
+        self.shared_experts = SwiGLU(
+            config.hidden_size,
+            config.shared_experts * config.expert_size,
+            config.moe_bias,
+        )
+        self.routed_experts = nn.ModuleList(
+            [
+                SwiGLU(config.hidden_size, config.expert_size, config.moe_bias)
+                for _ in range(config.routed_experts)
+            ]
+        )
+        self.router = Router(config)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shape = x.size()
+        x = x.view(-1, self.config.hidden_size)
+        experts_idx, experts_weights, counts = self.router(x)
+        out = torch.zeros_like(x)
+        for i, expert in enumerate(self.routed_experts):
+            if counts[i] == 0:
+                continue
+            idx, pos = torch.where(experts_idx == i)
+            out[idx] += expert(x[idx]) * experts_weights[idx, pos, None]
+        shared_out = self.shared_experts(x)
+        return (out + shared_out).view(shape)
+
+
+def build_alibi_bias(config: SmalLmConfig) -> torch.Tensor:
+    """
+    Build ALiBi bias for specified number of heads
+    ref: https://arxiv.org/abs/2108.12409v2
+
+    Returns:
+        Tensor with ALiBi biases, shape: [num heads]
+    """
+    bias = (
+        2**-8
+        / config.num_attention_heads
+        * torch.arange(1, config.num_attention_heads + 1).float()
+    )
+    return bias
+
+
+def calc_rotation(num_rotaitions, dim, base, seq_len) -> torch.Tensor:
+    """
+    In terms of wavelength calculate the position for a specific rotation frequence
+    """
+    return (
+        dim
+        * torch.log(torch.tensor(seq_len).float() / (num_rotaitions * 2 * torch.pi))
+        / torch.log(torch.tensor(base))
+    )
+
+
+def get_ramp_interpolation(min_idx, max_idx, thetas_dim, eps=1e-6) -> torch.Tensor:
+    """
+    Ramp interpolation function to maintain high frequencies and expand low frequencies
+    """
+    if min_idx == max_idx:
+        max_idx += eps
+    mult = (torch.arange(thetas_dim) - min_idx) / (max_idx - min_idx)
+    mult = torch.clamp(mult, 0, 1)
+    return 1 - mult
+
+
+def build_rope_bias(config: SmalLmConfig) -> torch.Tensor:
+    """
+    Build RoPE bias for specified dimension and maximum sequence length
+    uses complex space for simplicity and convenience
+    ref: https://arxiv.org/abs/2104.09864v5
+    Also use NTK-by-parts interpolation method
+    ref: https://arxiv.org/abs/2309.00071
+    good explanation: https://blog.eleuther.ai/yarn/
+
+    Args:
+        config (SmalLmConfig): base model config
+
+    Returns:
+        torch.Tensor: Complex values for rotations, shape: [seq_len, head_size]
+    """
+    dim = config.head_size
+
+    theta = 1.0 / (config.rope_base ** (torch.arange(0, dim, 2).float() / dim))
+
+    # neural tangent kernel by part korrection
+    if config.max_seq_len > config.original_seq_len:
+        scale = config.max_seq_len / config.original_seq_len
+        # from idea that lambda = 2pi / theta_i and also lambda = seq_len / num_rotations, lambda - wavelen
+        low_interpolation_idx = max(
+            0,
+            torch.ceil(
+                calc_rotation(
+                    config.high_rotations,
+                    dim,
+                    config.rope_base,
+                    config.original_seq_len,
+                )
+            ).item(),
+        )
+        high_interpolation_idx = min(
+            dim - 1,
+            torch.floor(
+                calc_rotation(
+                    config.low_rotations, dim, config.rope_base, config.original_seq_len
+                )
+            ).item(),
+        )
+        interpolation_mult = get_ramp_interpolation(
+            low_interpolation_idx, high_interpolation_idx, dim // 2
+        )
+        theta = (1 - interpolation_mult) * theta / scale + interpolation_mult * theta
+
+    seq_idx = torch.arange(config.max_seq_len)
+    seq_theta = torch.outer(seq_idx, theta)
+    bias = torch.polar(torch.ones_like(seq_theta), seq_theta)
+    return bias
+
+
+def apply_rope_bias(x: torch.Tensor, precompute_bias: torch.Tensor) -> torch.Tensor:
+    """
+    Apply rope bias in complex space
+
+    Args:
+        x (torch.Tensor): input embeddings for head
+        precompute_bias (torch.Tensor): precomputed rope bias
+
+    Returns:
+        torch.Tensor: rotated embeddings
+    """
+    ini_dtype = x.dtype
+    # for numerical stability convert to fp32
+    x = rearrange(x.float(), "b n s (d i) -> b n s d i", i=2).contiguous()
+    x = torch.view_as_complex(x)
+    x = x * precompute_bias
+    x = torch.view_as_real(x)
+    x = rearrange(x, "b n s d i -> b n s (d i)")
+    return x.to(ini_dtype)
+
+
+def flash_attention_forward(
+    module: nn.Module,
+    x: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor,
+    alibi_slope: Optional[torch.Tensor],
+) -> torch.Tensor:
+    query = rearrange(query, "b n s d -> b s n d")
+    key = rearrange(key, "b n s d -> b s n d")
+    value = rearrange(value, "b n s d -> b s n d")
+    query, idx_q, cu_seqlens_q, max_seqlen_q, _ = unpad_input(query, attention_mask)
+    key, _, cu_seqlens_k, max_seqlen_k, _ = unpad_input(key, attention_mask)
+    value, _, _, _, _ = unpad_input(value, attention_mask)
+
+    key = key.contiguous()
+    value = value.contiguous()
+    query = query.contiguous()
+
+    attention_probs = flash_attn_varlen_func(
+        query,
+        key,
+        value,
+        cu_seqlens_q=cu_seqlens_q,
+        cu_seqlens_k=cu_seqlens_k,
+        max_seqlen_q=max_seqlen_q,
+        max_seqlen_k=max_seqlen_k,
+        dropout_p=module.config.attention_dropout if module.training else 0.0,
+        causal=True,
+        alibi_slopes=alibi_slope if module.config.attention_bias == "alibi" else None,
+    )
+    attention_probs = pad_input(attention_probs, idx_q, x.size(0), x.size(1))
+    out = rearrange(attention_probs, "b s n d -> b s (n d)")
+    return out, None
+
+
+def sdpa_attention_forward(
+    module: nn.Module,
+    x: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor,
+    alibi_slope: Optional[torch.Tensor],
+) -> torch.Tensor:
+    is_causal = attention_mask is None and query.size(-2) > 1
+
+    attention_probs = F.scaled_dot_product_attention(
+        query,
+        key,
+        value,
+        attn_mask=attention_mask,
+        enable_gqa=True,
+        is_causal=is_causal,
+        dropout_p=module.config.attention_dropout if module.training else 0.0,
+    )
+    out = rearrange(attention_probs, "b n s d -> b s (n d)")
+
+    return out, None
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    x: torch.Tensor,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor,
+    alibi_slope: Optional[torch.Tensor],
+) -> torch.Tensor:
+    query = rearrange(
+        query,
+        "b (kv group) s d -> b kv group s d",
+        kv=module.config.num_kv_heads,
+        group=module.head_per_group,
+    )
+    key = rearrange(key, "b kv s d -> b kv 1 s d")
+    value = rearrange(value, "b kv s d -> b kv 1 s d")
+    attention_weights = query @ key.transpose(-1, -2)
+    attention_probs = F.dropout(
+        attention_weights / torch.sqrt(torch.tensor(value.size(-1), device=x.device)),
+        p=module.config.attention_dropout if module.training else 0.0,
+    )
+    if alibi_slope is not None:
+        alibi_slope = rearrange(
+            alibi_slope,
+            "b n s s -> b kv group s s",
+            kv=module.config.num_kv_heads,
+            group=module.head_per_group,
+        )
+        attention_probs = attention_probs + alibi_slope
+    elif alibi_slope is None and attention_mask is not None:
+        attention_mask = attention_mask.expand(
+            -1, module.config.num_attention_heads, -1, -1
+        )
+        attention_mask = rearrange(
+            attention_mask,
+            "b (kv group) s1 s2 -> b kv group s1 s2",
+            kv=module.config.num_kv_heads,
+            group=module.head_per_group,
+        )
+        attention_probs = attention_probs + attention_mask
+    attention_probs = F.softmax(attention_probs, dim=-1)
+    attention_probs = attention_probs @ value
+    out = rearrange(attention_probs, "b kv group s d -> b s (kv group d)")
+    return out, attention_weights
+
+
+ALL_ATTENTION_FUNCTIONS = {
+    "eager": eager_attention_forward,
+    "sdpa": sdpa_attention_forward,
+    "flash_attention_2": flash_attention_forward,
+}
+
+
+class CausalSelfAttention(nn.Module):
+    """
+    Scaled dot product attention with supports different implementations
+    currently available: sdpa, flash, native torch
+    """
+    def __init__(self, config: SmalLmConfig, layer_idx: int, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if config.num_attention_heads % config.num_kv_heads != 0:
+            raise ValueError("Num attention heads should divided by num kv heads")
+
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_per_group = config.num_attention_heads // config.num_kv_heads
+        self.q_proj = nn.Linear(
+            config.hidden_size,
+            config.head_size * config.num_attention_heads,
+            bias=config.attention_bias,
+        )
+        self.kv_proj = nn.Linear(
+            config.hidden_size,
+            config.head_size * config.num_kv_heads * 2,
+            bias=config.attention_bias,
+        )
+        self.out_proj = nn.Linear(
+            config.head_size * config.num_attention_heads,
+            config.hidden_size,
+            bias=config.attention_bias,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: torch.Tensor,
+        past_key_values: Optional[Cache | torch.FloatTensor],
+        cache_position: Optional[torch.LongTensor],
+        bias: torch.Tensor,
+    ):
+        q = self.q_proj(x)
+        kv = self.kv_proj(x)
+        q = rearrange(q, "b s (n d) -> b n s d", n=self.config.num_attention_heads)
+        k, v = rearrange(kv, "b s (n d q) -> q b n s d", q=2, d=self.config.head_size)
+
+        if self.config.positional_bias_type == "rope":
+            k = apply_rope_bias(k, bias)
+            q = apply_rope_bias(q, bias)
+
+        if past_key_values is not None:
+            # for static cache
+            cach_kwargs = {"cache_position": cache_position}
+            k, v = past_key_values.update(
+                key_states=k,
+                value_states=v,
+                layer_idx=self.layer_idx,
+                cache_kwargs=cach_kwargs,
+            )
+
+        attention_interface = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[
+                self.config._attn_implementation
+            ]
+
+        out, attention_weights = attention_interface(
+            self,
+            x,
+            q,
+            k,
+            v,
+            attention_mask,
+            bias if self.config.positional_bias_type == "alibi" else None,
+        )
+
+        out = self.out_proj(out)
+        return out, attention_weights
+
+
+class WeightedResidual(nn.Module):
+    """
+    Weighted residual connection, possibly learn skip weight
+    """
+    def __init__(self, config: SmalLmConfig, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.weight = nn.Parameter(
+            torch.ones(config.hidden_size), requires_grad=config.static_residual
+        )
+
+    def forward(self, short, long):
+        return self.weight * short + long
+
+
+class Block(nn.Module):
+    def __init__(self, config: SmalLmConfig, layer_idx: int, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.attn_norm = nn.RMSNorm(
+            config.hidden_size,
+            eps=config.rms_norm_eps,
+            elementwise_affine=config.rms_affine,
+        )
+        self.ffn_norm = nn.RMSNorm(
+            config.hidden_size,
+            eps=config.rms_norm_eps,
+            elementwise_affine=config.rms_affine,
+        )
+        self.dropout1 = nn.Dropout(config.layer_dropout)
+        self.dropout2 = nn.Dropout(config.layer_dropout)
+        self.attention = CausalSelfAttention(config, layer_idx)
+        self.mlp = (
+            MoE(config)
+            if (
+                config.use_moe
+                and layer_idx % config.moe_period == 0
+                and layer_idx > config.no_moe_layers
+            )
+            else SwiGLU(config.hidden_size, config.intermediate_size, config.mlp_bias)
+        )
+        self.attention_residual = WeightedResidual(config)
+        self.ffn_residual = WeightedResidual(config)
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        attention_mask: torch.Tensor,
+        past_key_values: Optional[Cache | torch.FloatTensor],
+        output_attentions: bool,
+        cache_position: Optional[torch.LongTensor],
+        bias: torch.Tensor,
+    ) -> tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
+        identity = inputs_embeds
+
+        # attention block
+        out = self.attn_norm(inputs_embeds)
+        out, attention_probs = self.attention(
+            out, attention_mask, past_key_values, cache_position, bias
+        )
+        out = self.dropout1(out)
+        identity = self.attention_residual(identity, out)
+
+        # swiglu / MoE block
+        out = self.dropout2(self.mlp(self.ffn_norm(identity)))
+        out = self.ffn_residual(identity, out)
+        if output_attentions:
+            return out, attention_probs
+        return (out,)
+
+
+class SmalLmPreTrainedModel(PreTrainedModel):
+    config_class = SmalLmConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Block"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_sdpa = True
+    _supports_flash_attn_2 = True
+
+    def __init__(self, *inputs, **kwargs):
+        super().__init__(*inputs, **kwargs)
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+            module.weight.data[self.pad_idx].zero_()
+
+
+class SmalLmModel(SmalLmPreTrainedModel):
+    def __init__(self, config: SmalLmConfig, *args, **kwargs):
+        super().__init__(config, *args, **kwargs)
+        self.config = config
+        self.pad_idx = config.pad_token_id
+        self.pad_token_id = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.config = config
+        precompute_bias = (
+            build_alibi_bias(config)
+            if config.positional_bias_type == "alibi"
+            else build_rope_bias(config)
+        )
+        self.register_buffer("precompute_bias", precompute_bias, persistent=False)
+        # не забыть про sharing weights на output голове self.embedding.weight = self.output.weight
+        self.embedding = nn.Embedding(
+            self.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
+        )
+        self.embedding_dropout = nn.Dropout(config.embedding_dropout)
+        self.layers = nn.ModuleList(
+            [Block(config, idx) for idx in range(1, config.num_hidden_layers + 1)]
+        )
+        self.out_norm = nn.RMSNorm(
+            config.hidden_size,
+            eps=config.rms_norm_eps,
+            elementwise_affine=config.rms_affine,
+        )
+
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embedding
+
+    def set_input_embeddings(self, value):
+        self.embedding = value
+
+    def forward(
+        self,
+        # input options
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        # output options
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        # cache options
+        use_cache: Optional[bool] = None,
+        past_key_values: Optional[Cache | torch.FloatTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> tuple | BaseModelOutputWithPast:
+        # check additional parameters
+        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 (False if self.training else self.config.use_cache)
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.return_dict
+        )
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You must specify only input_ids or inputs_embeds, not both"
+            )
+
+        if self.training and use_cache:
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embedding(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        # calculating position for StaticCache
+        if cache_position is None:
+            last_position = (
+                past_key_values.get_seq_length() if past_key_values is not None else 0
+            )
+            cache_position = torch.arange(
+                last_position,
+                last_position + inputs_embeds.size(1),
+                device=inputs_embeds.device,
+            )
+
+        causal_mask = self._get_causal_masks(
+            attention_mask, inputs_embeds, past_key_values, cache_position
+        )
+        if self.config.positional_bias_type == "rope":
+            end_pos = (
+                inputs_embeds.size(1)
+                if past_key_values is None
+                else cache_position[-1] + 1
+            )
+            start_pos = 0 if past_key_values is None else cache_position[0]
+            bias = self.precompute_bias[start_pos:end_pos]
+
+        elif self.config.positional_bias_type == "alibi":
+            if self.config._attn_implementation == "flash_attention_2":
+                bias = self.precompute_bias
+            else:
+                i = torch.arange(
+                    (
+                        inputs_embeds.size(1)
+                        if past_key_values is None
+                        else cache_position[-1] + 1
+                    ),
+                    device=inputs_embeds.device,
+                )
+                bias = i[:, None] - i[None, :]
+                bias = torch.tril(bias).expand(
+                    inputs_embeds.size(0), self.config.num_attention_heads, -1, -1
+                ) * rearrange(self.precompute_bias, "n -> 1 n 1 1")
+                if causal_mask is not None:
+                    causal_mask = causal_mask + bias
+                else:
+                    causal_mask = bias
+
+        hidden_state = inputs_embeds
+        hidden_states = [hidden_state] if output_hidden_states else None
+        attentions = [] if output_attentions else None
+        for idx, layer in enumerate(self.layers, 1):
+            if self.gradient_checkpointing:
+                # for details see:
+                # https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3107
+                # https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3149
+                layer_out = self._gradient_checkpointing_func(
+                    layer.__call__,
+                    hidden_state,
+                    causal_mask,
+                    past_key_values,
+                    output_attentions,
+                    cache_position,
+                    bias,
+                )
+            else:
+                layer_out = layer(
+                    hidden_state,
+                    causal_mask,
+                    past_key_values,
+                    output_attentions,
+                    cache_position,
+                    bias,
+                )
+            hidden_state = layer_out[0]
+            if output_hidden_states:
+                hidden_states.append(hidden_state)
+            if output_attentions:
+                attentions.append(layer_out[1])
+
+        hidden_state = self.out_norm(hidden_state)
+        out = BaseModelOutputWithPast(
+            last_hidden_state=hidden_state,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=tuple(hidden_states) if hidden_states is not None else None,
+            attentions=tuple(attentions) if attentions is not None else None,
+        )
+        return out if return_dict else out.to_tuple()
+
+    def _get_causal_masks(
+        self,
+        attention_mask: Optional[torch.Tensor],
+        inputs_embeds: torch.Tensor,
+        past_key_values: Optional[torch.Tensor],
+        cache_position: Optional[torch.Tensor],
+    ):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is None:
+                attention_mask = torch.ones(
+                    (inputs_embeds.size(0), inputs_embeds.size(1)),
+                    device=inputs_embeds.device,
+                ).long()
+            return attention_mask
+        dtype, device = inputs_embeds.dtype, inputs_embeds.device
+        past_token = (
+            past_key_values.get_seq_length() if past_key_values is not None else 0
+        )
+        if attention_mask is not None and torch.all(attention_mask == 0.0):
+            return None
+        if AttentionMaskConverter._ignore_causal_mask_sdpa(
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            past_key_values_length=past_token,
+            is_training=self.training,
+        ):
+            return None
+
+        sequence_length = inputs_embeds.size(1)
+        target_length = (
+            attention_mask.size(-1)
+            if isinstance(attention_mask, torch.Tensor)
+            else past_token + sequence_length + 1
+        )
+
+        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask=attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            device=device,
+            cache_position=cache_position,
+            batch_size=inputs_embeds.size(0),
+        )
+
+        min_dtype = torch.finfo(dtype).min
+        causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+        return causal_mask
+
+    @staticmethod
+    def _prepare_4d_causal_attention_mask_with_cache_position(
+        attention_mask: Optional[torch.Tensor],
+        sequence_length: int,
+        target_length: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        cache_position: Optional[torch.Tensor],
+        batch_size: int,
+    ):
+        if attention_mask is not None and attention_mask.dim() == 4:
+            causal_mask = attention_mask
+        else:
+            min_dtype = torch.finfo(dtype).min
+            causal_mask = torch.full(
+                (sequence_length, target_length),
+                fill_value=min_dtype,
+                dtype=dtype,
+                device=device,
+            )
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(
+                target_length, device=device
+            ) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()
+                mask_length = attention_mask.shape[-1]
+                padding_mask = (
+                    causal_mask[:, :, :, :mask_length]
+                    + attention_mask[:, None, None, :]
+                )
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[
+                    :, :, :, :mask_length
+                ].masked_fill(padding_mask, min_dtype)
+        return causal_mask
+
+
+class SmalLmForCausalLM(SmalLmPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: SmalLmConfig, *args, **kwargs):
+        super().__init__(config, *args, **kwargs)
+        self.config = config
+        self.model = SmalLmModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.post_init()
+
+    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 options
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        # output options
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        # cache options
+        use_cache: Optional[bool] = None,
+        past_key_values: Optional[Cache | torch.FloatTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        # generation options
+        labels: Optional[torch.Tensor] = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs,
+    ) -> tuple | CausalLMOutputWithPast:
+        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.return_dict
+        )
+
+        model_outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            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,
+            **kwargs,
+        )
+
+        hidden_states = model_outputs[0]
+        slice_indices = (
+            slice(-logits_to_keep, None)
+            if isinstance(logits_to_keep, int)
+            else logits_to_keep
+        )
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+        if not return_dict:
+            output = (logits, model_outputs[1:])
+            return (loss, output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=model_outputs.past_key_values,
+            hidden_states=model_outputs.hidden_states,
+            attentions=model_outputs.attentions,
+        )
+
+
+__all__ = ["SmalLmForCausalLM", "SmalLmModel", "SmalLmPreTrainedModel"]