172 lines
6.9 KiB
Python
172 lines
6.9 KiB
Python
from typing import Callable, Optional
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from ...cache_utils import Cache
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
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from ...utils import logging
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from ..llama.modeling_llama import (
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LlamaAttention,
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LlamaDecoderLayer,
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LlamaForCausalLM,
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LlamaMLP,
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LlamaModel,
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LlamaRotaryEmbedding,
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eager_attention_forward,
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rotate_half,
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)
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from .configuration_olmo import OlmoConfig
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logger = logging.get_logger(__name__)
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class OlmoLayerNorm(nn.Module):
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"""LayerNorm but with no learnable weight or bias."""
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def __init__(self, hidden_size: int) -> None:
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super().__init__()
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self.normalized_shape = (hidden_size,)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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orig_dtype = hidden_states.dtype
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return F.layer_norm(hidden_states.to(dtype=torch.float32), self.normalized_shape, None, None, eps=1e-5).to(
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orig_dtype
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)
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class OlmoMLP(LlamaMLP):
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def __init__(self, config):
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super().__init__(config)
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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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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q_type, k_type = q.dtype, k.dtype
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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.to(q_type), k_embed.to(k_type)
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class OlmoAttention(LlamaAttention):
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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[Cache] = 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_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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if self.config.clip_qkv is not None:
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query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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query_states = query_states.view(hidden_shape).transpose(1, 2)
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key_states = key_states.view(hidden_shape).transpose(1, 2)
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value_states = value_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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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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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 if not self.training else self.attention_dropout,
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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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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class OlmoDecoderLayer(LlamaDecoderLayer):
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def __init__(self, config: OlmoConfig, layer_idx: int):
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super().__init__(config, layer_idx)
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self.input_layernorm = OlmoLayerNorm(config.hidden_size)
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self.post_attention_layernorm = OlmoLayerNorm(config.hidden_size)
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self.self_attn = OlmoAttention(config=config, layer_idx=layer_idx)
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# This is identical to LlamaRotaryEmbedding except the output cos and sin are returned
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# as float32 rather than the input type.
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class OlmoRotaryEmbedding(LlamaRotaryEmbedding):
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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, sin
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class OlmoModel(LlamaModel):
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def __init__(self, config: OlmoConfig):
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super().__init__(config)
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self.layers = nn.ModuleList(
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[OlmoDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
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)
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self.norm = OlmoLayerNorm(config.hidden_size)
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class OlmoForCausalLM(LlamaForCausalLM):
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pass
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__all__ = [
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"OlmoForCausalLM",
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"OlmoModel",
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"OlmoPreTrainedModel", # noqa: F822
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]
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