Bump transformers source v4.54.0.dev0

config.json

@@ -42,9 +42,5 @@
   "transformers_version": "4.53.0.dev0",
   "use_cache": true,
   "use_pos_enc": true,
-  "vocab_size": 65536,
-  "auto_map": {
-    "AutoConfig": "modeling_lfm2.LFM2Config",
-    "AutoModelForCausalLM": "modeling_lfm2.LFM2ForCausalLM"
-  }
+  "vocab_size": 65536
 }

modeling_lfm2.py

@@ -1,924 +0,0 @@
-from typing import Any, Callable, ClassVar, Optional, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers.cache_utils import DynamicCache
-from transformers.configuration_utils import PretrainedConfig
-from transformers.generation import GenerationMixin
-from transformers.masking_utils import create_causal_mask
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.modeling_layers import GradientCheckpointingLayer
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
-from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
-from transformers.processing_utils import Unpack
-from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, logging
-from transformers.utils.import_utils import is_causal_conv1d_available
-
-if is_causal_conv1d_available():
-    from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
-else:
-    causal_conv1d_fn, causal_conv1d_update = None, None
-
-
-kernel_modules = (causal_conv1d_fn, causal_conv1d_update)
-is_fast_path_available = all(kernel_modules)
-
-logger = logging.get_logger(__name__)
-
-
-# ========================================================
-# Config Class (to be removed) once integrated into
-# `transformers`. For now, allows for dynamic importing.
-# ========================================================s
-# from .configuration_lfm2 import LFM2Config
-
-
-class LFM2Config(PretrainedConfig):
-    model_type = "lfm2"
-    keys_to_ignore_at_inference: ClassVar = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size: int = 65536,
-        hidden_size: int = 2560,
-        num_hidden_layers: int = 32,
-        pad_token_id: int = 0,
-        bos_token_id: int = 1,
-        eos_token_id: int = 2,
-        tie_embedding: bool = True,
-        theta: float = 1000000.0,
-        max_position_embeddings: int = 128_000,
-        use_cache: bool = True,
-        norm_eps: float = 0.00001,
-        initializer_range: float = 0.02,
-        num_attention_heads: int = 32,
-        num_key_value_heads: int = 8,
-        conv_bias: bool = False,
-        conv_dim: int = 2560,
-        conv_L_cache: int = 3,
-        block_dim: int = 2560,
-        block_ff_dim: int = 12288,
-        block_multiple_of: int = 256,
-        block_ffn_dim_multiplier: float = 1.0,
-        block_auto_adjust_ff_dim: bool = True,
-        full_attn_idxs: Optional[list[int]] = None,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.rope_theta = theta
-        self.max_position_embeddings = max_position_embeddings
-        self.use_cache = use_cache
-        self.norm_eps = norm_eps
-        self.initializer_range = initializer_range
-
-        # attn operator config
-        self.num_attention_heads = num_attention_heads
-        self.num_key_value_heads = num_key_value_heads
-        self.full_attn_idxs = full_attn_idxs
-
-        # custom operator config
-        self.conv_bias = conv_bias
-        self.conv_dim = conv_dim
-        self.conv_L_cache = conv_L_cache
-
-        # block config
-        self.block_dim = block_dim
-        self.block_ff_dim = block_ff_dim
-        self.block_multiple_of = block_multiple_of
-        self.block_ffn_dim_multiplier = block_ffn_dim_multiplier
-        self.block_auto_adjust_ff_dim = block_auto_adjust_ff_dim
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_embedding,
-            **kwargs,
-        )
-
-    @property
-    def layers_block_type(self):
-        return ["attention" if i in self.full_attn_idxs else "conv" for i in range(self.num_hidden_layers)]
-
-
-class LFM2RMSNorm(torch.nn.Module):
-    def __init__(self, dim: int, eps: float = 1e-6):
-        super().__init__()
-        self.eps = eps
-        self.weight = nn.Parameter(torch.ones(dim))
-
-    def _norm(self, x):
-        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
-
-    def forward(self, x):
-        output = self._norm(x.float())
-        return output.type_as(x) * self.weight
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors."""
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-class LFM2RotaryEmbedding(nn.Module):
-    def __init__(self, config: LFM2Config, device=None):
-        super().__init__()
-        # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
-            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
-        else:
-            self.rope_type = "default"
-        self.max_seq_len_cached = config.max_position_embeddings
-        self.original_max_seq_len = config.max_position_embeddings
-
-        self.config = config
-        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
-
-        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.original_inv_freq = self.inv_freq
-
-    @torch.no_grad()
-    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
-    def forward(self, x, position_ids):
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
-        position_ids_expanded = position_ids[:, None, :].float()
-
-        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos() * self.attention_scaling
-            sin = emb.sin() * self.attention_scaling
-
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-def eager_attention_forward(
-    module: nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask: Optional[torch.Tensor],
-    scaling: float,
-    dropout: float = 0.0,
-    **kwargs,
-):
-    num_key_value_groups = query.shape[1] // key.shape[1]
-    key_states = repeat_kv(key, num_key_value_groups)
-    value_states = repeat_kv(value, num_key_value_groups)
-
-    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
-    if attention_mask is not None:
-        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-        attn_weights = attn_weights + causal_mask
-    else:
-        seq_len = key_states.shape[-2]
-        causal_mask = torch.triu(
-            torch.full((seq_len, seq_len), float("-inf"), device=attn_weights.device),
-            diagonal=1,
-        )
-        attn_weights = attn_weights + causal_mask
-
-    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
-    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
-    attn_output = torch.matmul(attn_weights, value_states)
-    attn_output = attn_output.transpose(1, 2).contiguous()
-
-    return attn_output, attn_weights
-
-
-class LFM2MLP(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        ff_dim: int,
-        multiple_of: int,
-        auto_adjust_ff_dim: bool,
-        ffn_dim_multiplier: Optional[float],
-    ):
-        super().__init__()
-        if auto_adjust_ff_dim:
-            ff_dim = int(2 * ff_dim / 3)
-            # custom dim factor multiplier
-            if ffn_dim_multiplier is not None:
-                ff_dim = int(ffn_dim_multiplier * ff_dim)
-            ff_dim = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of)
-
-        self.w1 = nn.Linear(dim, ff_dim, bias=False)
-        self.w3 = nn.Linear(dim, ff_dim, bias=False)
-        self.w2 = nn.Linear(ff_dim, dim, bias=False)
-
-    def forward(self, x):
-        return self.w2(F.silu(self.w1(x)) * self.w3(x))
-
-
-class LFM2Cache(DynamicCache):
-    """
-    Attention and conv cache for LFM2.
-
-    It stores the Key and Value states as a list of tensors, one for each layer.
-    Attention layer cache shape: `[batch_size, num_heads, seq_len, head_dim]`.
-    Conv layer cache shape: `[batch_size, conv_dim, L_cache-1]`.
-    """
-
-    def __init__(
-        self,
-        config: LFM2Config,
-        max_batch_size: int,
-        dtype: torch.dtype = torch.float32,
-        device: Union[torch.device, str, None] = None,
-    ):
-        super().__init__()  # initialize key and value cache
-        self.max_batch_size = max_batch_size
-        self.full_attn_idxs = config.full_attn_idxs
-        self.conv_L_cache = config.conv_L_cache
-        self._dtype = dtype
-
-        self.conv_cache: list[torch.Tensor] = []
-        device = torch.device(device) if device is not None else None
-
-        for _ in range(config.num_hidden_layers):
-            conv_state = torch.zeros(
-                self.max_batch_size,
-                config.conv_dim,
-                self.conv_L_cache,
-                dtype=self._dtype,
-                device=device,
-            )
-            torch._dynamo.mark_static_address(conv_state)
-            self.conv_cache.append(conv_state)
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: Optional[dict[str, Any]] = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """
-        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
-
-        Parameters:
-            key_states (`torch.Tensor`):
-                The new key states to cache.
-            value_states (`torch.Tensor`):
-                The new value states to cache.
-            layer_idx (`int`):
-                The index of the layer to cache the states for.
-            cache_kwargs (`Dict[str, Any]`, `optional`):
-                Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
-
-        Return:
-            A tuple containing the updated key and value states.
-        """
-        # Update the number of seen tokens
-        # if layer_idx == 0:
-        if layer_idx == self.full_attn_idxs[0]:
-            self._seen_tokens += key_states.shape[-2]
-
-        # Update the cache
-        if key_states is not None:
-            if len(self.key_cache) <= layer_idx:
-                # There may be skipped layers, fill them with empty lists
-                for _ in range(len(self.key_cache), layer_idx):
-                    self.key_cache.append(torch.tensor([]))
-                    self.value_cache.append(torch.tensor([]))
-                self.key_cache.append(key_states)
-                self.value_cache.append(value_states)
-            elif (
-                not self.key_cache[layer_idx].numel()  # prefers not t.numel() to len(t) == 0 to export the model
-            ):  # fills previously skipped layers; checking for tensor causes errors
-                self.key_cache[layer_idx] = key_states
-                self.value_cache[layer_idx] = value_states
-            else:
-                self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
-                self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        for layer_idx in range(len(self.key_cache)):
-            device = self.key_cache[layer_idx].device
-            self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
-            device = self.value_cache[layer_idx].device
-            self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-            device = self.conv_cache[layer_idx].device
-            self.conv_cache[layer_idx] = self.conv_cache[layer_idx].index_select(0, beam_idx.to(device))
-
-    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.full_attn_idxs[0] if layer_idx not in self.full_attn_idxs else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def reset(self):
-        for layer_idx in range(len(self.conv_cache)):
-            # In-place ops prevent breaking the static address
-            self.conv_cache[layer_idx].zero_()
-
-
-class LFM2Attention(nn.Module):
-    def __init__(self, config: LFM2Config, layer_idx: Optional[int] = None, **kwargs):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        if layer_idx is None:
-            logger.warning_once(
-                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and "
-                "will lead to errors during the forward call if caching is used. Please make sure to provide a "
-                "`layer_idx` when creating this class."
-            )
-        self.head_dim = config.hidden_size // 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.is_causal = True
-
-        self.q_layernorm = LFM2RMSNorm(self.head_dim, eps=config.norm_eps)
-        self.k_layernorm = LFM2RMSNorm(self.head_dim, eps=config.norm_eps)
-
-        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
-        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
-        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
-        self.out_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor],
-        past_key_value: Optional[LFM2Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
-        input_shape = hidden_states.shape[:-1]
-        hidden_shape = (*input_shape, -1, self.head_dim)
-
-        q = self.q_layernorm(self.q_proj(hidden_states).view(*hidden_shape)).transpose(1, 2)
-        k = self.k_layernorm(self.k_proj(hidden_states).view(*hidden_shape)).transpose(1, 2)
-        v = self.v_proj(hidden_states).view(*hidden_shape).transpose(1, 2)
-
-        cos, sin = position_embeddings
-        q, k = apply_rotary_pos_emb(q, k, cos, sin)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            k, v = past_key_value.update(
-                key_states=k, value_states=v, layer_idx=self.layer_idx, cache_kwargs=cache_kwargs
-            )
-
-        attention_interface: Callable = eager_attention_forward
-        if self.config._attn_implementation != "eager":
-            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
-
-        attn_output, attn_weights = attention_interface(
-            self,
-            q,
-            k,
-            v,
-            attention_mask,
-            dropout=0.0,
-            scaling=self.scaling,
-            **kwargs,
-        )
-        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-        output = self.out_proj(attn_output)
-        return output, attn_weights
-
-
-class LFM2ShortConv(nn.Module):
-    def __init__(
-        self,
-        config: LFM2Config,
-        dim: int,
-        layer_idx: int,
-    ):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        self.L_cache = config.conv_L_cache
-        self.bias = config.conv_bias
-
-        self.conv = nn.Conv1d(
-            in_channels=dim,
-            out_channels=dim,
-            kernel_size=self.L_cache,
-            groups=dim,
-            bias=self.bias,
-            padding=self.L_cache - 1,
-        )
-        self.in_proj = nn.Linear(dim, 3 * dim, bias=self.bias)
-        self.out_proj = nn.Linear(dim, dim, bias=self.bias)
-
-    def cuda_kernels_forward(
-        self,
-        x: torch.Tensor,
-        cache_params: Optional[LFM2Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-    ):
-        BCx = self.in_proj(x).transpose(-1, -2)
-        B, C, x = BCx.chunk(3, dim=-2)
-
-        Bx = B * x
-
-        conv_weights = self.conv.weight.view(self.conv.weight.size(0), self.conv.weight.size(2))
-        if cache_params is not None and cache_position[0] > 0:
-            conv_out = causal_conv1d_update(
-                Bx.squeeze(-1),
-                cache_params.conv_cache[self.layer_idx],
-                conv_weights,
-                self.conv.bias,
-                None,
-            )
-            conv_out = conv_out.unsqueeze(-1)
-        else:
-            if cache_params is not None:
-                conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
-                cache_params.conv_cache[self.layer_idx].copy_(conv_state)
-
-            conv_out = causal_conv1d_fn(Bx, conv_weights, self.conv.bias, activation=None)
-
-        y = C * conv_out
-        y = self.out_proj(y.transpose(-1, -2).contiguous())
-        return y
-
-    def slow_forward(
-        self,
-        x: torch.Tensor,
-        cache_params: Optional[LFM2Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-    ):
-        seqlen = x.shape[1]
-        BCx = self.in_proj(x).transpose(-1, -2)
-        B, C, x = BCx.chunk(3, dim=-2)
-
-        Bx = B * x
-
-        if cache_params is not None and cache_position[0] > 0:
-            conv_state = cache_params.conv_cache[self.layer_idx]
-            cache_position = cache_position.clamp(0, self.L_cache - 1)
-            conv_state = conv_state.roll(shifts=-1, dims=-1)
-            conv_state[:, :, cache_position] = Bx.to(device=conv_state.device, dtype=conv_state.dtype)
-            cache_params.conv_cache[self.layer_idx].copy_(conv_state)
-            conv_out = torch.sum(conv_state.to(Bx.device) * self.conv.weight[:, 0, :], dim=-1)
-            if self.bias:
-                conv_out += self.conv.bias
-
-            conv_out = conv_out.unsqueeze(-1)
-        else:
-            if cache_params is not None:
-                conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
-                cache_params.conv_cache[self.layer_idx].copy_(conv_state)
-
-            conv_out = self.conv(Bx)[..., :seqlen]
-
-        y = C * conv_out
-        y = y.transpose(-1, -2).contiguous()
-        y = self.out_proj(y)
-        return y
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        cache_params: Optional[LFM2Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-    ):
-        if is_fast_path_available and "cuda" in x.device.type and not torch._dynamo.is_compiling():
-            return self.cuda_kernels_forward(x, cache_params, cache_position, attention_mask)
-        return self.slow_forward(x, cache_params, cache_position, attention_mask)
-
-
-class LFM2AttentionDecoderLayer(GradientCheckpointingLayer):
-    def __init__(self, config: LFM2Config, layer_idx: int):
-        super().__init__()
-        self.self_attn = LFM2Attention(config, layer_idx)
-        self.feed_forward = LFM2MLP(
-            dim=config.block_dim,
-            ff_dim=config.block_ff_dim,
-            multiple_of=config.block_multiple_of,
-            auto_adjust_ff_dim=config.block_auto_adjust_ff_dim,
-            ffn_dim_multiplier=config.block_ffn_dim_multiplier,
-        )
-        self.operator_norm = LFM2RMSNorm(config.hidden_size, eps=config.norm_eps)
-        self.ffn_norm = LFM2RMSNorm(config.hidden_size, eps=config.norm_eps)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        h, self_attn_weights = self.self_attn(
-            hidden_states=self.operator_norm(hidden_states),
-            position_embeddings=position_embeddings,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            cache_position=cache_position,
-            **kwargs,
-        )
-        h += hidden_states
-        out = h + self.feed_forward.forward(self.ffn_norm(h))
-
-        outputs = (out,)
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        return outputs
-
-
-class LFM2ShortConvDecoderLayer(GradientCheckpointingLayer):
-    def __init__(self, config: LFM2Config, layer_idx: int):
-        super().__init__()
-        self.conv = LFM2ShortConv(
-            config=config,
-            dim=config.conv_dim,
-            layer_idx=layer_idx,
-        )
-        self.feed_forward = LFM2MLP(
-            dim=config.block_dim,
-            ff_dim=config.block_ff_dim,
-            multiple_of=config.block_multiple_of,
-            auto_adjust_ff_dim=config.block_auto_adjust_ff_dim,
-            ffn_dim_multiplier=config.block_ffn_dim_multiplier,
-        )
-        self.operator_norm = LFM2RMSNorm(config.hidden_size, eps=config.norm_eps)
-        self.ffn_norm = LFM2RMSNorm(config.hidden_size, eps=config.norm_eps)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        past_key_value: Optional[LFM2Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = False,
-        **kwargs,
-    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        h = self.conv(
-            self.operator_norm(hidden_states),
-            cache_params=past_key_value,
-            cache_position=cache_position,
-            attention_mask=attention_mask,
-        )
-        self_attn_weights = None
-
-        h += hidden_states
-        out = h + self.feed_forward.forward(self.ffn_norm(h))
-
-        outputs = (out,)
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        return outputs
-
-
-@auto_docstring
-class LFM2PretrainedModel(PreTrainedModel):
-    config_class = LFM2Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules: ClassVar = ["LFM2AttentionDecoderLayer", "LFM2ShortConvDecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_flex_attn = True
-    _supports_cache_class = True
-    _supports_quantized_cache = True
-    _supports_static_cache = True
-    _supports_attention_backend = True
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, (nn.Linear, nn.Conv1d)):
-            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_()
-        elif isinstance(module, LFM2RMSNorm):
-            module.weight.data.fill_(1.0)
-
-
-class LFM2Model(LFM2PretrainedModel):
-    def __init__(self, config: LFM2Config):
-        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.pos_emb = LFM2RotaryEmbedding(config)
-
-        decoder_layers = []
-        for i in range(config.num_hidden_layers):
-            if i in config.full_attn_idxs:
-                decoder_layers.append(LFM2AttentionDecoderLayer(config, layer_idx=i))
-            else:
-                decoder_layers.append(LFM2ShortConvDecoderLayer(config, layer_idx=i))
-        self.layers = nn.ModuleList(decoder_layers)
-
-        self.embedding_norm = LFM2RMSNorm(config.hidden_size, eps=config.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
-
-    @can_return_tuple
-    @auto_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[LFM2Cache] = 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,
-        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
-    ) -> BaseModelOutputWithPast:
-        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 must specify exactly one of input_ids or inputs_embeds")
-
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-            )
-            use_cache = False
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        if use_cache and past_key_values is None:
-            batch_size = inputs_embeds.shape[0]
-            past_key_values = LFM2Cache(
-                config=self.config, max_batch_size=batch_size, dtype=self.dtype, device=self.device
-            )
-
-        if cache_position is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            cache_position = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
-            )
-
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = create_causal_mask(
-            config=self.config,
-            input_embeds=inputs_embeds,
-            attention_mask=attention_mask,
-            cache_position=cache_position,
-            past_key_values=past_key_values,
-        )
-        hidden_states = inputs_embeds
-
-        position_embeddings = self.pos_emb(hidden_states, position_ids)
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        for decoder_layer in self.layers:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            layer_outputs = decoder_layer(
-                hidden_states,
-                attention_mask=causal_mask,
-                position_ids=position_ids,
-                past_key_value=past_key_values,
-                output_attentions=output_attentions,
-                use_cache=use_cache,
-                cache_position=cache_position,
-                position_embeddings=position_embeddings,
-                **flash_attn_kwargs,
-            )
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-        hidden_states = self.embedding_norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        output = BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values if use_cache else None,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-        return output if return_dict else output.to_tuple()
-
-
-class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
-
-
-@auto_docstring
-class LFM2ForCausalLM(LFM2PretrainedModel, GenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config: LFM2Config):
-        super().__init__(config)
-        self.model = LFM2Model(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        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
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[LFM2Cache] = 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,
-        logits_to_keep: Union[int, torch.Tensor] = 0,
-        **kwargs: Unpack[KwargsForCausalLM],
-    ) -> Union[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
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs: BaseModelOutputWithPast = 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,
-            cache_position=cache_position,
-            return_dict=return_dict,
-            **kwargs,
-        )
-
-        hidden_states = outputs.last_hidden_state
-        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        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,) + 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,
-        position_ids=None,
-        use_cache=True,
-        **kwargs,
-    ):
-        # Overwritten -- Support custom LFM2Cache.
-
-        empty_past_kv = past_key_values is None or (
-            isinstance(past_key_values, DynamicCache) and past_key_values._seen_tokens == 0
-        )
-
-        # Omit tokens covered by past_key_values.
-        if not empty_past_kv:
-            # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
-            # Exception 1: when passing input_embeds, input_ids may be missing entries
-            # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
-            # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
-            #              (we can't check exception 3 while compiling)
-            if (
-                inputs_embeds is not None  # Exception 1
-                or cache_position[-1] >= input_ids.shape[1]  # Exception 3
-            ):
-                input_ids = input_ids[:, -cache_position.shape[0] :]
-            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
-                input_ids = input_ids[:, cache_position]
-        else:
-            past_key_values = LFM2Cache(self.config, input_ids.shape[0], dtype=self.dtype, device=self.device)
-
-        # if attention_mask is not None and position_ids is None:
-        #     # create position_ids on the fly for batch generation
-        #     position_ids = attention_mask.long().cumsum(-1) - 1
-        #     position_ids.masked_fill_(attention_mask == 0, 1)
-        #     if not empty_past_kv:
-        #         position_ids = position_ids[:, -input_ids.shape[1] :]
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and empty_past_kv:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids.contiguous()}  # `contiguous()` needed for compilation use cases
-
-        model_inputs.update(
-            {
-                # "position_ids": position_ids,
-                "past_key_values": past_key_values,
-                "use_cache": use_cache,
-                "attention_mask": attention_mask,
-                "cache_position": cache_position,
-            }
-        )
-        return model_inputs
-
-
-__all__ = ["LFM2ForCausalLM", "LFM2Model", "LFM2PretrainedModel"]

requirements.txt

@@ -1,2 +0,0 @@
-transformers==4.53.0.dev0
-tokenizers==0.21.1