519 lines
20 KiB
Python
519 lines
20 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Any, Callable, Optional, Union
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import torch
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import torch.nn.functional as F
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from torch import nn
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from ...cache_utils import DynamicCache
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from ...masking_utils import create_causal_mask
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutputWithPast
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, logging
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from ...utils.import_utils import is_causal_conv1d_available
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from ..bamba.modeling_bamba import apply_mask_to_padding_states
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from ..llama.modeling_llama import (
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LlamaAttention,
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LlamaForCausalLM,
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LlamaModel,
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LlamaPreTrainedModel,
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LlamaRMSNorm,
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LlamaRotaryEmbedding,
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apply_rotary_pos_emb,
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eager_attention_forward,
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)
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from .configuration_lfm2 import Lfm2Config
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if is_causal_conv1d_available():
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from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
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else:
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causal_conv1d_fn, causal_conv1d_update = None, None
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kernel_modules = (causal_conv1d_fn, causal_conv1d_update)
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is_fast_path_available = all(kernel_modules)
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logger = logging.get_logger(__name__)
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class Lfm2RMSNorm(LlamaRMSNorm):
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pass
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class Lfm2RotaryEmbedding(LlamaRotaryEmbedding):
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pass
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class Lfm2MLP(nn.Module):
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def __init__(self, config: Lfm2Config):
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super().__init__()
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intermediate_size = config.intermediate_size
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if config.block_auto_adjust_ff_dim:
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intermediate_size = int(2 * intermediate_size / 3)
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# custom dim factor multiplier
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if config.block_ffn_dim_multiplier is not None:
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intermediate_size = int(config.block_ffn_dim_multiplier * intermediate_size)
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intermediate_size = config.block_multiple_of * (
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(intermediate_size + config.block_multiple_of - 1) // config.block_multiple_of
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)
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self.w1 = nn.Linear(config.hidden_size, intermediate_size, bias=False)
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self.w3 = nn.Linear(config.hidden_size, intermediate_size, bias=False)
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self.w2 = nn.Linear(intermediate_size, config.hidden_size, bias=False)
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def forward(self, x):
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return self.w2(F.silu(self.w1(x)) * self.w3(x))
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class Lfm2HybridConvCache(DynamicCache):
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"""
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Attention and conv cache for Lfm2.
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It stores the Key and Value states as a list of tensors, one for each layer.
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Attention layer cache shape: `[batch_size, num_heads, seq_len, head_dim]`.
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Conv layer cache shape: `[batch_size, hidden_size, L_cache-1]`.
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"""
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# Override @property existing in Cache
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max_batch_size = None
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is_compileable = False
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key_cache = None
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value_cache = None
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def __init__(
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self,
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config: Lfm2Config,
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max_batch_size: int,
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dtype: torch.dtype = torch.float32,
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device: Union[torch.device, str, None] = None,
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):
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self.key_cache = []
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self.value_cache = []
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self.max_batch_size = max_batch_size
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self.layer_types = config.layer_types
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self.first_attention_layer = self.layer_types.index("full_attention")
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self.conv_L_cache = config.conv_L_cache
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self._dtype = dtype
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self.conv_cache: list[torch.Tensor] = []
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device = torch.device(device) if device is not None else None
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for _ in range(config.num_hidden_layers):
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conv_state = torch.zeros(
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self.max_batch_size,
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config.hidden_size,
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self.conv_L_cache,
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dtype=self._dtype,
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device=device,
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)
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torch._dynamo.mark_static_address(conv_state)
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self.conv_cache.append(conv_state)
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def update(
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self,
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key_states: torch.Tensor,
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value_states: torch.Tensor,
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layer_idx: int,
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cache_kwargs: Optional[dict[str, Any]] = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""
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Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
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Parameters:
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key_states (`torch.Tensor`):
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The new key states to cache.
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value_states (`torch.Tensor`):
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The new value states to cache.
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layer_idx (`int`):
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The index of the layer to cache the states for.
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cache_kwargs (`Dict[str, Any]`, `optional`):
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Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
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Return:
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A tuple containing the updated key and value states.
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"""
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# Update the cache
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if key_states is not None:
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if len(self.key_cache) <= layer_idx:
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# There may be skipped layers, fill them with empty lists
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for _ in range(len(self.key_cache), layer_idx):
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self.key_cache.append(torch.tensor([]))
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self.value_cache.append(torch.tensor([]))
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self.key_cache.append(key_states)
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self.value_cache.append(value_states)
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elif (
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not self.key_cache[layer_idx].numel() # prefers not t.numel() to len(t) == 0 to export the model
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): # fills previously skipped layers; checking for tensor causes errors
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self.key_cache[layer_idx] = key_states
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self.value_cache[layer_idx] = value_states
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else:
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self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
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self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
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return self.key_cache[layer_idx], self.value_cache[layer_idx]
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def reorder_cache(self, beam_idx: torch.LongTensor):
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"""Reorders the cache for beam search, given the selected beam indices."""
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for layer_idx in range(len(self.key_cache)):
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device = self.key_cache[layer_idx].device
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self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
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device = self.value_cache[layer_idx].device
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self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
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device = self.conv_cache[layer_idx].device
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self.conv_cache[layer_idx] = self.conv_cache[layer_idx].index_select(0, beam_idx.to(device))
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def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
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"""Returns the sequence length of the cached states. A layer index can be optionally passed."""
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# take any layer that contains cache and not empty tensor
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layer_idx = self.first_attention_layer if self.layer_types[layer_idx] != "full_attention" else layer_idx
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if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
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return 0
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return self.key_cache[layer_idx].shape[-2]
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def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> tuple[int, int]:
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"""
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Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
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the given layer at `layer_idx`.
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The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns (i.e. sliding_window, chunk_size),
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for each layer.
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"""
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full_mask_kv_offset = 0
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query_length = cache_position.shape[0]
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past_seen_tokens = self.get_seq_length()
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kv_length = query_length + past_seen_tokens
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return kv_length, full_mask_kv_offset
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def crop(self, max_length: int):
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"""Crop the cache to the given length"""
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if max_length < 0:
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max_length = self.get_seq_length() - abs(max_length)
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if self.get_seq_length() <= max_length:
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return
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for idx in range(len(self.key_cache)):
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if self.key_cache[idx].numel():
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self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
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self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
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def __getitem__(self, layer_idx: int) -> tuple[torch.Tensor, torch.Tensor]:
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return self.key_cache[layer_idx], self.value_cache[layer_idx]
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def to_legacy_cache(self) -> tuple[tuple[torch.Tensor], tuple[torch.Tensor]]:
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raise NotImplementedError("Lfm2HybridConvCache does not have a legacy cache equivalent.")
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@classmethod
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def from_legacy_cache(cls, past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None) -> "DynamicCache":
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raise NotImplementedError("Lfm2HybridConvCache does not have a legacy cache equivalent.")
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def reset(self):
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for layer_idx in range(len(self.conv_cache)):
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# In-place ops prevent breaking the static address
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self.conv_cache[layer_idx].zero_()
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class Lfm2Attention(LlamaAttention):
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def __init__(self, config: Lfm2Config, layer_idx: int):
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super().__init__(config, layer_idx)
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self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
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self.out_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
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self.q_layernorm = Lfm2RMSNorm(self.head_dim, eps=config.norm_eps)
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self.k_layernorm = Lfm2RMSNorm(self.head_dim, eps=config.norm_eps)
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del self.o_proj
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del self.attention_dropout
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Lfm2HybridConvCache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_layernorm(self.q_proj(hidden_states).view(*hidden_shape)).transpose(1, 2)
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key_states = self.k_layernorm(self.k_proj(hidden_states).view(*hidden_shape)).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(*hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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output = self.out_proj(attn_output)
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return output, attn_weights
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class Lfm2ShortConv(nn.Module):
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def __init__(
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self,
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config: Lfm2Config,
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layer_idx: int,
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):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.L_cache = config.conv_L_cache
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self.bias = config.conv_bias
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self.conv = nn.Conv1d(
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in_channels=config.hidden_size,
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out_channels=config.hidden_size,
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kernel_size=self.L_cache,
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groups=config.hidden_size,
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bias=self.bias,
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padding=self.L_cache - 1,
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)
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self.in_proj = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=self.bias)
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self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=self.bias)
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def cuda_kernels_forward(
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self,
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x: torch.Tensor,
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past_key_value: Optional[Lfm2HybridConvCache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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):
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x = apply_mask_to_padding_states(x, attention_mask)
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BCx = self.in_proj(x).transpose(-1, -2)
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B, C, x = BCx.chunk(3, dim=-2)
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Bx = B * x
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conv_weights = self.conv.weight.view(self.conv.weight.size(0), self.conv.weight.size(2))
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if past_key_value is not None and cache_position[0] > 0:
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conv_out = causal_conv1d_update(
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Bx.squeeze(-1),
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past_key_value.conv_cache[self.layer_idx],
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conv_weights,
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self.conv.bias,
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None,
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)
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conv_out = conv_out.unsqueeze(-1)
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else:
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if past_key_value is not None:
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conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
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past_key_value.conv_cache[self.layer_idx].copy_(conv_state)
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conv_out = causal_conv1d_fn(Bx, conv_weights, self.conv.bias, activation=None)
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y = C * conv_out
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y = self.out_proj(y.transpose(-1, -2).contiguous())
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return y
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def slow_forward(
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self,
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x: torch.Tensor,
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past_key_value: Optional[Lfm2HybridConvCache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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):
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seqlen = x.shape[1]
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x = apply_mask_to_padding_states(x, attention_mask)
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BCx = self.in_proj(x).transpose(-1, -2)
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B, C, x = BCx.chunk(3, dim=-2)
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Bx = B * x
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if past_key_value is not None and cache_position[0] > 0:
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conv_state = past_key_value.conv_cache[self.layer_idx]
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cache_position = cache_position.clamp(0, self.L_cache - 1)
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conv_state = conv_state.roll(shifts=-1, dims=-1)
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conv_state[:, :, cache_position] = Bx.to(device=conv_state.device, dtype=conv_state.dtype)
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past_key_value.conv_cache[self.layer_idx].copy_(conv_state)
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conv_out = torch.sum(conv_state.to(Bx.device) * self.conv.weight[:, 0, :], dim=-1)
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if self.bias:
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conv_out += self.conv.bias
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conv_out = conv_out.unsqueeze(-1)
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else:
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if past_key_value is not None:
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conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
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past_key_value.conv_cache[self.layer_idx].copy_(conv_state)
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conv_out = self.conv(Bx)[..., :seqlen]
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y = C * conv_out
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y = y.transpose(-1, -2).contiguous()
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y = self.out_proj(y)
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return y
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def forward(
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self,
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hidden_states: torch.Tensor,
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past_key_value: Optional[Lfm2HybridConvCache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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):
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if is_fast_path_available and "cuda" in hidden_states.device.type and not torch._dynamo.is_compiling():
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return self.cuda_kernels_forward(hidden_states, past_key_value, cache_position, attention_mask)
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return self.slow_forward(hidden_states, past_key_value, cache_position, attention_mask)
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class Lfm2DecoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: Lfm2Config, layer_idx: int):
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super().__init__()
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self.is_attention_layer = config.layer_types[layer_idx] == "full_attention"
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if self.is_attention_layer:
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self.self_attn = Lfm2Attention(config, layer_idx)
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else:
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self.conv = Lfm2ShortConv(config, layer_idx)
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self.feed_forward = Lfm2MLP(config)
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self.operator_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
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self.ffn_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[tuple[torch.Tensor]] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> torch.Tensor:
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residual = hidden_states
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if self.is_attention_layer:
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hidden_states, _ = self.self_attn(
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hidden_states=self.operator_norm(hidden_states),
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position_embeddings=position_embeddings,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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cache_position=cache_position,
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**kwargs,
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)
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else:
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hidden_states = self.conv(
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hidden_states=self.operator_norm(hidden_states),
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past_key_value=past_key_value,
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cache_position=cache_position,
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attention_mask=attention_mask,
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)
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hidden_states = hidden_states + residual
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hidden_states = hidden_states + self.feed_forward(self.ffn_norm(hidden_states))
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return hidden_states
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class Lfm2PreTrainedModel(LlamaPreTrainedModel):
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_can_compile_fullgraph = False
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class Lfm2Model(LlamaModel):
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def __init__(self, config: Lfm2Config):
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super().__init__(config)
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self.pos_emb = Lfm2RotaryEmbedding(config)
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self.embedding_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
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del self.norm
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del self.rotary_emv
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Lfm2HybridConvCache] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> BaseModelOutputWithPast:
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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if use_cache and past_key_values is None:
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batch_size = inputs_embeds.shape[0]
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past_key_values = Lfm2HybridConvCache(
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config=self.config, max_batch_size=batch_size, dtype=self.dtype, device=self.device
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)
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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0)
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causal_mask = create_causal_mask(
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config=self.config,
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input_embeds=inputs_embeds,
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attention_mask=attention_mask,
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cache_position=cache_position,
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past_key_values=past_key_values,
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position_ids=position_ids,
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)
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hidden_states = inputs_embeds
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position_embeddings = self.pos_emb(hidden_states, position_ids)
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# decoder layers
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for decoder_layer in self.layers[: self.config.num_hidden_layers]:
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hidden_states = decoder_layer(
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hidden_states,
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attention_mask=causal_mask,
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position_ids=position_ids,
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past_key_value=past_key_values,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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hidden_states = self.embedding_norm(hidden_states)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=past_key_values,
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)
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class Lfm2ForCausalLM(LlamaForCausalLM):
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pass
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__all__ = ["Lfm2ForCausalLM", "Lfm2Model", "Lfm2PreTrainedModel"]
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