756 lines
31 KiB
Python
756 lines
31 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/lfm2/modular_lfm2.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_lfm2.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# 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 Cache, DynamicCache
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from ...generation import GenerationMixin
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from ...integrations import use_kernel_forward_from_hub
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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, CausalLMOutputWithPast
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
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from ...utils.generic import check_model_inputs
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from ...utils.import_utils import is_causal_conv1d_available
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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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@use_kernel_forward_from_hub("RMSNorm")
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class Lfm2RMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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Lfm2RMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
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class Lfm2RotaryEmbedding(nn.Module):
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def __init__(self, config: Lfm2Config, device=None):
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super().__init__()
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# BC: "rope_type" was originally "type"
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if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
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self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
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else:
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self.rope_type = "default"
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self.max_seq_len_cached = config.max_position_embeddings
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self.original_max_seq_len = config.max_position_embeddings
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self.config = config
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self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.original_inv_freq = self.inv_freq
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@torch.no_grad()
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@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
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def forward(self, x, position_ids):
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos() * self.attention_scaling
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sin = emb.sin() * self.attention_scaling
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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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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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`, *optional*):
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Deprecated and unused.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class Lfm2Attention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: Lfm2Config, layer_idx: int):
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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.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.scaling = self.head_dim**-0.5
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self.is_causal = True
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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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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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)
|
|
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
|
output = self.out_proj(attn_output)
|
|
return output, attn_weights
|
|
|
|
|
|
def apply_mask_to_padding_states(hidden_states, attention_mask):
|
|
"""
|
|
Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66
|
|
"""
|
|
if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1:
|
|
dtype = hidden_states.dtype
|
|
hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
|
|
|
|
return hidden_states
|
|
|
|
|
|
kernel_modules = (causal_conv1d_fn, causal_conv1d_update)
|
|
is_fast_path_available = all(kernel_modules)
|
|
|
|
|
|
class Lfm2ShortConv(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: Lfm2Config,
|
|
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=config.hidden_size,
|
|
out_channels=config.hidden_size,
|
|
kernel_size=self.L_cache,
|
|
groups=config.hidden_size,
|
|
bias=self.bias,
|
|
padding=self.L_cache - 1,
|
|
)
|
|
self.in_proj = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=self.bias)
|
|
self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=self.bias)
|
|
|
|
def cuda_kernels_forward(
|
|
self,
|
|
x: torch.Tensor,
|
|
past_key_value: Optional[Lfm2HybridConvCache] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
):
|
|
x = apply_mask_to_padding_states(x, attention_mask)
|
|
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 past_key_value is not None and cache_position[0] > 0:
|
|
conv_out = causal_conv1d_update(
|
|
Bx.squeeze(-1),
|
|
past_key_value.conv_cache[self.layer_idx],
|
|
conv_weights,
|
|
self.conv.bias,
|
|
None,
|
|
)
|
|
conv_out = conv_out.unsqueeze(-1)
|
|
else:
|
|
if past_key_value is not None:
|
|
conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
|
|
past_key_value.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,
|
|
past_key_value: Optional[Lfm2HybridConvCache] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
):
|
|
seqlen = x.shape[1]
|
|
|
|
x = apply_mask_to_padding_states(x, attention_mask)
|
|
BCx = self.in_proj(x).transpose(-1, -2)
|
|
B, C, x = BCx.chunk(3, dim=-2)
|
|
|
|
Bx = B * x
|
|
|
|
if past_key_value is not None and cache_position[0] > 0:
|
|
conv_state = past_key_value.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)
|
|
past_key_value.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 past_key_value is not None:
|
|
conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
|
|
past_key_value.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,
|
|
hidden_states: torch.Tensor,
|
|
past_key_value: Optional[Lfm2HybridConvCache] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
):
|
|
if is_fast_path_available and "cuda" in hidden_states.device.type and not torch._dynamo.is_compiling():
|
|
return self.cuda_kernels_forward(hidden_states, past_key_value, cache_position, attention_mask)
|
|
return self.slow_forward(hidden_states, past_key_value, cache_position, attention_mask)
|
|
|
|
|
|
class Lfm2DecoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: Lfm2Config, layer_idx: int):
|
|
super().__init__()
|
|
self.is_attention_layer = config.layer_types[layer_idx] == "full_attention"
|
|
|
|
if self.is_attention_layer:
|
|
self.self_attn = Lfm2Attention(config, layer_idx)
|
|
else:
|
|
self.conv = Lfm2ShortConv(config, layer_idx)
|
|
self.feed_forward = Lfm2MLP(config)
|
|
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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
) -> torch.Tensor:
|
|
residual = hidden_states
|
|
if self.is_attention_layer:
|
|
hidden_states, _ = 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,
|
|
)
|
|
else:
|
|
hidden_states = self.conv(
|
|
hidden_states=self.operator_norm(hidden_states),
|
|
past_key_value=past_key_value,
|
|
cache_position=cache_position,
|
|
attention_mask=attention_mask,
|
|
)
|
|
hidden_states = hidden_states + residual
|
|
hidden_states = hidden_states + self.feed_forward(self.ffn_norm(hidden_states))
|
|
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring
|
|
class Lfm2PreTrainedModel(PreTrainedModel):
|
|
config: Lfm2Config
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["Lfm2DecoderLayer"]
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
_can_compile_fullgraph = False
|
|
_supports_attention_backend = True
|
|
_can_record_outputs = {
|
|
"hidden_states": Lfm2DecoderLayer,
|
|
"attentions": Lfm2Attention,
|
|
}
|
|
|
|
|
|
@auto_docstring
|
|
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.layers = nn.ModuleList(
|
|
[Lfm2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.rotary_emb = Lfm2RotaryEmbedding(config=config)
|
|
self.gradient_checkpointing = False
|
|
self.pos_emb = Lfm2RotaryEmbedding(config)
|
|
self.embedding_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@check_model_inputs
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Lfm2HybridConvCache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> BaseModelOutputWithPast:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
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 = Lfm2HybridConvCache(
|
|
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,
|
|
position_ids=position_ids,
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
position_embeddings = self.pos_emb(hidden_states, position_ids)
|
|
|
|
# decoder layers
|
|
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
|
hidden_states = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.embedding_norm(hidden_states)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class Lfm2ForCausalLM(Lfm2PreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
_tp_plan = {"lm_head": "colwise_rep"}
|
|
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
|
|
|
def __init__(self, config):
|
|
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)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> CausalLMOutputWithPast:
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, Lfm2ForCausalLM
|
|
|
|
>>> model = Lfm2ForCausalLM.from_pretrained("meta-lfm2/Lfm2-2-7b-hf")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("meta-lfm2/Lfm2-2-7b-hf")
|
|
|
|
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
|
```"""
|
|
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,
|
|
cache_position=cache_position,
|
|
**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)
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = ["Lfm2ForCausalLM", "Lfm2Model", "Lfm2PreTrainedModel"]
|