150 lines
5.4 KiB
Python
150 lines
5.4 KiB
Python
# coding=utf-8
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# Copyright 2024 The Kyutai and HuggingFace Inc. teams. All rights reserved.
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#
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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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import math
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from typing import Optional
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint
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from ...utils import logging
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from ..gemma.modeling_gemma import GemmaForCausalLM, GemmaForSequenceClassification, GemmaForTokenClassification
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from ..granite.modeling_granite import GraniteAttention
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from ..llama.modeling_llama import LlamaDecoderLayer, LlamaMLP, LlamaModel, LlamaPreTrainedModel, LlamaRotaryEmbedding
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from .configuration_helium import HeliumConfig
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logger = logging.get_logger(__name__)
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class HeliumRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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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.to(torch.float32) * 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 HeliumRotaryEmbedding(LlamaRotaryEmbedding):
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pass
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class HeliumMLP(LlamaMLP):
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pass
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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[..., 0::2]
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x2 = x[..., 1::2]
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return torch.stack((-x2, x1), dim=-1).flatten(-2)
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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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# Interleave them instead of usual shape
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cos = cos[..., : cos.shape[-1] // 2].repeat_interleave(2, dim=-1)
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sin = sin[..., : sin.shape[-1] // 2].repeat_interleave(2, dim=-1)
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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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class HeliumAttention(GraniteAttention):
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def __init__(self, config: HeliumConfig, layer_idx: Optional[int] = None):
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super().__init__(config, layer_idx)
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self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
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self.scaling = 1 / math.sqrt(self.head_dim)
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class HeliumDecoderLayer(LlamaDecoderLayer):
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def __init__(self, config: HeliumConfig, layer_idx: Optional[int] = None):
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super().__init__()
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self.mlp = HeliumMLP(config)
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self.input_layernorm = HeliumRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = HeliumRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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class HeliumPreTrainedModel(LlamaPreTrainedModel):
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pass
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class HeliumModel(HeliumPreTrainedModel, LlamaModel):
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def __init__(self, config: HeliumConfig):
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super().__init__(config)
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self.layers = nn.ModuleList(
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[HeliumDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
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)
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self.norm = HeliumRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.rotary_emb = HeliumRotaryEmbedding(config)
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self.gradient_checkpointing = False
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# Initialize weights and apply final processing
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self.post_init()
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class HeliumForCausalLM(GemmaForCausalLM):
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pass
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class HeliumForSequenceClassification(GemmaForSequenceClassification):
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pass
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class HeliumForTokenClassification(GemmaForTokenClassification):
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pass
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__all__ = [
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"HeliumPreTrainedModel",
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"HeliumModel",
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"HeliumForCausalLM",
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"HeliumForSequenceClassification",
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"HeliumForTokenClassification",
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]
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