1064 lines
43 KiB
Python
1064 lines
43 KiB
Python
"""
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Modified MIT License
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Software Copyright© 2025 IQuest Research
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Our only modification is that, if the Software (or any derivative works
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thereof) is used for any of your commercial products or services, you shall
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prominently display "IQuest Coder" on the user interface of such product or
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service.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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"""
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from typing import Callable, List, Optional, Tuple, Union
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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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from transformers.activations import ACT2FN
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from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
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from transformers.generation import GenerationMixin
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from transformers.modeling_attn_mask_utils import AttentionMaskConverter
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from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
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from transformers.modeling_layers import GradientCheckpointingLayer
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutputWithPast,
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TokenClassifierOutput,
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)
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from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from transformers.processing_utils import Unpack
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from transformers.utils import (
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auto_docstring,
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can_return_tuple,
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is_torch_flex_attn_available,
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logging,
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)
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from .configuration_iquestcoder import IQuestCoderConfig
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if is_torch_flex_attn_available():
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from torch.nn.attention.flex_attention import BlockMask
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from transformers.integrations.flex_attention import make_flex_block_causal_mask
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logger = logging.get_logger(__name__)
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# =============================================================================
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# Helper Functions
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# =============================================================================
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def rotate_half(x: torch.Tensor) -> torch.Tensor:
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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(
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q: torch.Tensor,
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k: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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position_ids: Optional[torch.Tensor] = None,
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unsqueeze_dim: int = 1,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q: The query tensor.
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k: The key tensor.
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cos: The cosine part of the rotary embedding.
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sin: The sine part of the rotary embedding.
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position_ids: Deprecated and unused.
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unsqueeze_dim: The dimension along which to unsqueeze cos and sin.
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Returns:
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Tuple of query and key tensors rotated using the Rotary Position Embedding.
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"""
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# Borrowed from OLMo: preserve original dtypes for numerical stability
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q_dtype, k_dtype = 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_dtype), k_embed.to(k_dtype)
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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Expands key/value heads for Grouped Query Attention.
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
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The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to
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(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,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Standard eager attention implementation."""
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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 = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = F.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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# =============================================================================
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# Model Components
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# =============================================================================
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class IQuestCoderRMSNorm(nn.Module):
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"""Root Mean Square Layer Normalization.
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RMSNorm is computationally simpler than LayerNorm while achieving similar
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performance. It normalizes the input by its RMS value.
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"""
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def __init__(self, hidden_size: int, eps: float = 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: torch.Tensor) -> torch.Tensor:
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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) -> str:
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return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
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class IQuestCoderRotaryEmbedding(nn.Module):
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"""Rotary Position Embedding (RoPE).
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Implements rotary positional embeddings as described in the RoFormer paper.
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Supports various RoPE scaling methods for extended context lengths.
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"""
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def __init__(self, config: IQuestCoderConfig, 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 config.rope_scaling is not None:
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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
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def forward(self, x: torch.Tensor, position_ids: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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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):
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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 IQuestCoderMLP(nn.Module):
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"""Feed-forward network with SwiGLU activation.
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Uses the gated linear unit variant with SiLU activation for improved
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performance compared to standard FFN.
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"""
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def __init__(self, config: IQuestCoderConfig):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# SwiGLU: down_proj(act_fn(gate_proj(x)) * up_proj(x))
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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class IQuestCoderAttention(nn.Module):
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"""Multi-headed attention with support for Grouped Query Attention (GQA).
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Features:
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- Grouped Query Attention for memory efficiency
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- Optional QKV clipping for training stability (from OLMo)
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- Optional sliding window attention (from Qwen2)
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- Rotary Position Embeddings
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"""
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def __init__(self, config: IQuestCoderConfig, 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.attention_dropout = config.attention_dropout
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self.is_causal = True
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# Projection layers
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self.q_proj = nn.Linear(
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config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
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)
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self.k_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.v_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.o_proj = nn.Linear(
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config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
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)
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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: Unpack[FlashAttentionKwargs],
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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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# Compute Q, K, V projections
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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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# [OLMo Feature] Optional QKV clipping for training stability
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if self.config.clip_qkv is not None:
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query_states = query_states.clamp(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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key_states = key_states.clamp(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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value_states = value_states.clamp(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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# Reshape to (batch, heads, seq_len, head_dim)
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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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# Apply rotary position embeddings
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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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# Update KV cache if provided
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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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# [Qwen2 Feature] Sliding window attention
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sliding_window = None
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if (
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self.config.use_sliding_window
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and getattr(self.config, "sliding_window", None) is not None
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and self.layer_idx >= self.config.max_window_layers
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):
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sliding_window = self.config.sliding_window
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# Select attention implementation
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
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logger.warning_once(
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"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. "
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'Falling back to eager attention. This warning can be removed using the argument '
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'`attn_implementation="eager"` when loading the model.'
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)
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else:
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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# Compute attention
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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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sliding_window=sliding_window,
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**kwargs,
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)
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# Reshape and project output
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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 IQuestCoderDecoderLayer(GradientCheckpointingLayer):
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"""Transformer decoder layer with pre-normalization.
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Architecture: Pre-RMSNorm -> Attention -> Residual -> Pre-RMSNorm -> MLP -> Residual
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"""
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def __init__(self, config: IQuestCoderConfig, layer_idx: int):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = IQuestCoderAttention(config=config, layer_idx=layer_idx)
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self.mlp = IQuestCoderMLP(config)
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self.input_layernorm = IQuestCoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = IQuestCoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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# Warn if sliding window is enabled but not properly supported
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if config.use_sliding_window and config._attn_implementation != "flash_attention_2":
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logger.warning_once(
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f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
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"unexpected results may be encountered."
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)
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def forward(
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self,
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hidden_states: 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[Cache] = None,
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output_attentions: Optional[bool] = False,
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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
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# Pre-norm + Self Attention
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states, self_attn_weights = self.self_attn(
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hidden_states=hidden_states,
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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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output_attentions=output_attentions,
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use_cache=use_cache,
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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 = residual + hidden_states
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# Pre-norm + MLP
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (self_attn_weights,)
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return outputs
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# =============================================================================
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# Base Model
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# =============================================================================
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|
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@auto_docstring
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|
class IQuestCoderPreTrainedModel(PreTrainedModel):
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"""Base class for IQuestCoder models."""
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|
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config_class = IQuestCoderConfig
|
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["IQuestCoderDecoderLayer"]
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_skip_keys_device_placement = ["past_key_values"]
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_supports_flash_attn_2 = True
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_supports_sdpa = True
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_supports_flex_attn = True
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_supports_cache_class = True
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_supports_quantized_cache = True
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_supports_static_cache = True
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_supports_attention_backend = True
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def _init_weights(self, module: nn.Module):
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std = self.config.initializer_range
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if isinstance(module, nn.Linear):
|
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module.weight.data.normal_(mean=0.0, std=std)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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elif isinstance(module, IQuestCoderRMSNorm):
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module.weight.data.fill_(1.0)
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|
@auto_docstring
|
|
class IQuestCoderModel(IQuestCoderPreTrainedModel):
|
|
"""
|
|
IQuestCoder Model outputting raw hidden-states without any specific head on top.
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|
|
|
This model is compatible with LLaMA weights while incorporating features from OLMo and Qwen2.
|
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"""
|
|
|
|
def __init__(self, config: IQuestCoderConfig):
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super().__init__(config)
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
|
|
|
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
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self.layers = nn.ModuleList(
|
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[IQuestCoderDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
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|
)
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self.norm = IQuestCoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.rotary_emb = IQuestCoderRotaryEmbedding(config=config)
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self.gradient_checkpointing = False
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|
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# Initialize weights and apply final processing
|
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self.post_init()
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|
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def get_input_embeddings(self) -> nn.Embedding:
|
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return self.embed_tokens
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|
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|
def set_input_embeddings(self, value: nn.Embedding):
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self.embed_tokens = value
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|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
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,
|
|
past_key_values: Optional[Cache] = None,
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|
inputs_embeds: Optional[torch.FloatTensor] = None,
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|
use_cache: Optional[bool] = None,
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|
output_attentions: Optional[bool] = None,
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|
output_hidden_states: Optional[bool] = None,
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|
cache_position: Optional[torch.LongTensor] = None,
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**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
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|
) -> BaseModelOutputWithPast:
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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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 self.gradient_checkpointing and self.training and use_cache:
|
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
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|
)
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use_cache = False
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|
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if not isinstance(past_key_values, (type(None), Cache)):
|
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raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
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|
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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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past_key_values = DynamicCache()
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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 = self._update_causal_mask(
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attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
|
)
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hidden_states = inputs_embeds
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# Create position embeddings to be shared across the decoder layers
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position_embeddings = self.rotary_emb(hidden_states, position_ids)
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|
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# Decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
|
|
|
|
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
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if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
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|
|
|
layer_outputs = decoder_layer(
|
|
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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|
output_attentions=output_attentions,
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|
use_cache=use_cache,
|
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cache_position=cache_position,
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|
position_embeddings=position_embeddings,
|
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**flash_attn_kwargs,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
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|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
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# Add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
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|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
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past_key_values=past_key_values if use_cache else None,
|
|
hidden_states=all_hidden_states,
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|
attentions=all_self_attns,
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|
)
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|
|
|
def _update_causal_mask(
|
|
self,
|
|
attention_mask: Union[torch.Tensor, "BlockMask"],
|
|
input_tensor: torch.Tensor,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Cache,
|
|
output_attentions: bool = False,
|
|
):
|
|
if self.config._attn_implementation == "flash_attention_2":
|
|
if attention_mask is not None and past_key_values is not None:
|
|
is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
|
|
if is_padding_right:
|
|
raise ValueError(
|
|
"You are attempting to perform batched generation with padding_side='right'. "
|
|
"This may lead to unexpected behaviour for Flash Attention version of IQuestCoder. "
|
|
"Make sure to call `tokenizer.padding_side = 'left'` before tokenizing the input."
|
|
)
|
|
if attention_mask is not None and 0.0 in attention_mask:
|
|
return attention_mask
|
|
return None
|
|
|
|
if self.config._attn_implementation == "flex_attention":
|
|
if isinstance(attention_mask, torch.Tensor):
|
|
attention_mask = make_flex_block_causal_mask(attention_mask)
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|
return attention_mask
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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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using_static_cache = isinstance(past_key_values, StaticCache)
|
|
using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
|
|
|
|
if (
|
|
self.config._attn_implementation == "sdpa"
|
|
and not (using_static_cache or using_sliding_window_cache)
|
|
and not output_attentions
|
|
):
|
|
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
|
attention_mask,
|
|
inputs_embeds=input_tensor,
|
|
past_key_values_length=past_seen_tokens,
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|
sliding_window=self.config.sliding_window if self.config.use_sliding_window else None,
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|
is_training=self.training,
|
|
):
|
|
return None
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|
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|
dtype = input_tensor.dtype
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|
min_dtype = torch.finfo(dtype).min
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|
sequence_length = input_tensor.shape[1]
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|
|
|
if using_sliding_window_cache or using_static_cache:
|
|
target_length = past_key_values.get_max_cache_shape()
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|
else:
|
|
target_length = (
|
|
attention_mask.shape[-1]
|
|
if isinstance(attention_mask, torch.Tensor)
|
|
else past_seen_tokens + sequence_length + 1
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|
)
|
|
|
|
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask,
|
|
sequence_length=sequence_length,
|
|
target_length=target_length,
|
|
dtype=dtype,
|
|
cache_position=cache_position,
|
|
batch_size=input_tensor.shape[0],
|
|
config=self.config,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
if (
|
|
self.config._attn_implementation == "sdpa"
|
|
and attention_mask is not None
|
|
and attention_mask.device.type in ["cuda", "xpu", "npu"]
|
|
and not output_attentions
|
|
):
|
|
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
|
|
|
return causal_mask
|
|
|
|
@staticmethod
|
|
def _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask: torch.Tensor,
|
|
sequence_length: int,
|
|
target_length: int,
|
|
dtype: torch.dtype,
|
|
cache_position: torch.Tensor,
|
|
batch_size: int,
|
|
config: IQuestCoderConfig,
|
|
past_key_values: Cache,
|
|
):
|
|
"""Creates a causal 4D mask from a 2D mask, or returns the 4D mask if already provided."""
|
|
if attention_mask is not None and attention_mask.dim() == 4:
|
|
causal_mask = attention_mask
|
|
else:
|
|
min_dtype = torch.finfo(dtype).min
|
|
causal_mask = torch.full(
|
|
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
|
|
)
|
|
diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
|
|
-1, 1
|
|
)
|
|
|
|
# [Qwen2 Feature] Handle sliding window mask
|
|
if getattr(config, "use_sliding_window", False) and config.sliding_window is not None:
|
|
if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
|
|
sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= (
|
|
cache_position.reshape(-1, 1) - config.sliding_window
|
|
)
|
|
diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
|
|
|
|
causal_mask *= diagonal_attend_mask
|
|
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
|
|
|
if attention_mask is not None:
|
|
causal_mask = causal_mask.clone()
|
|
if attention_mask.shape[-1] > target_length:
|
|
attention_mask = attention_mask[:, :target_length]
|
|
mask_length = attention_mask.shape[-1]
|
|
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
|
|
causal_mask.device
|
|
)
|
|
padding_mask = padding_mask == 0
|
|
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
|
padding_mask, min_dtype
|
|
)
|
|
|
|
return causal_mask
|
|
|
|
|
|
# =============================================================================
|
|
# Model Heads
|
|
# =============================================================================
|
|
|
|
@auto_docstring
|
|
class IQuestCoderForCausalLM(IQuestCoderPreTrainedModel, GenerationMixin):
|
|
"""IQuestCoder Model with a language modeling head on top for causal LM."""
|
|
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
_tp_plan = {"lm_head": "colwise_rep"}
|
|
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
|
|
|
def __init__(self, config: IQuestCoderConfig):
|
|
super().__init__(config)
|
|
self.model = IQuestCoderModel(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 get_input_embeddings(self) -> nn.Embedding:
|
|
return self.model.embed_tokens
|
|
|
|
def set_input_embeddings(self, value: nn.Embedding):
|
|
self.model.embed_tokens = value
|
|
|
|
def get_output_embeddings(self) -> nn.Linear:
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings: nn.Linear):
|
|
self.lm_head = new_embeddings
|
|
|
|
def set_decoder(self, decoder: IQuestCoderModel):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self) -> IQuestCoderModel:
|
|
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,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**kwargs
|
|
) -> CausalLMOutputWithPast:
|
|
r"""
|
|
Args:
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
|
|
Example:
|
|
```python
|
|
>>> from transformers import AutoTokenizer
|
|
>>> from modeling_iquestcoder import IQuestCoderForCausalLM
|
|
|
|
>>> model = IQuestCoderForCausalLM.from_pretrained("path/to/IQuestCoder")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("path/to/IQuestCoder")
|
|
|
|
>>> 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."
|
|
```
|
|
"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
|
|
# Decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs: BaseModelOutputWithPast = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
cache_position=cache_position,
|
|
**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,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The IQuestCoder Model transformer with a sequence classification head on top (linear layer).
|
|
|
|
[`IQuestCoderForSequenceClassification`] uses the last token in order to do the classification, as other causal
|
|
models (e.g. GPT-2) do.
|
|
|
|
Since it does classification on the last token, it requires to know the position of the last token. If a
|
|
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row.
|
|
If no `pad_token_id` is defined, it simply takes the last value in each row of the batch.
|
|
"""
|
|
)
|
|
class IQuestCoderForSequenceClassification(IQuestCoderPreTrainedModel):
|
|
"""IQuestCoder Model with a sequence classification head."""
|
|
|
|
def __init__(self, config: IQuestCoderConfig):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.model = IQuestCoderModel(config)
|
|
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Embedding:
|
|
return self.model.embed_tokens
|
|
|
|
def set_input_embeddings(self, value: nn.Embedding):
|
|
self.model.embed_tokens = value
|
|
|
|
@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,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
) -> SequenceClassifierOutputWithPast:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss),
|
|
If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
transformer_outputs: BaseModelOutputWithPast = self.model(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
)
|
|
hidden_states = transformer_outputs.last_hidden_state
|
|
logits = self.score(hidden_states)
|
|
|
|
if input_ids is not None:
|
|
batch_size = input_ids.shape[0]
|
|
else:
|
|
batch_size = inputs_embeds.shape[0]
|
|
|
|
if self.config.pad_token_id is None and batch_size != 1:
|
|
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
|
if self.config.pad_token_id is None:
|
|
last_non_pad_token = -1
|
|
elif input_ids is not None:
|
|
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
|
|
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
|
|
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
|
|
else:
|
|
last_non_pad_token = -1
|
|
logger.warning_once(
|
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
|
)
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
|
|
|
|
return SequenceClassifierOutputWithPast(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class IQuestCoderForTokenClassification(IQuestCoderPreTrainedModel):
|
|
"""IQuestCoder Model with a token classification head."""
|
|
|
|
def __init__(self, config: IQuestCoderConfig):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.model = IQuestCoderModel(config)
|
|
if getattr(config, "classifier_dropout", None) is not None:
|
|
classifier_dropout = config.classifier_dropout
|
|
elif getattr(config, "hidden_dropout", None) is not None:
|
|
classifier_dropout = config.hidden_dropout
|
|
else:
|
|
classifier_dropout = 0.1
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.score = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Embedding:
|
|
return self.model.embed_tokens
|
|
|
|
def set_input_embeddings(self, value: nn.Embedding):
|
|
self.model.embed_tokens = value
|
|
|
|
@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,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
) -> TokenClassifierOutput:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss),
|
|
If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
outputs: BaseModelOutputWithPast = self.model(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
)
|
|
sequence_output = outputs.last_hidden_state
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.score(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits, labels, self.config)
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class IQuestCoderForQuestionAnswering(IQuestCoderPreTrainedModel):
|
|
"""IQuestCoder Model with a span classification head for extractive question-answering."""
|
|
|
|
base_model_prefix = "transformer"
|
|
|
|
def __init__(self, config: IQuestCoderConfig):
|
|
super().__init__(config)
|
|
self.transformer = IQuestCoderModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> nn.Embedding:
|
|
return self.transformer.embed_tokens
|
|
|
|
def set_input_embeddings(self, value: nn.Embedding):
|
|
self.transformer.embed_tokens = value
|
|
|
|
@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,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
**kwargs,
|
|
) -> QuestionAnsweringModelOutput:
|
|
outputs: BaseModelOutputWithPast = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
)
|
|
|
|
sequence_output = outputs.last_hidden_state
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"IQuestCoderPreTrainedModel",
|
|
"IQuestCoderModel",
|
|
"IQuestCoderForCausalLM",
|
|
"IQuestCoderForSequenceClassification",
|
|
"IQuestCoderForTokenClassification",
|
|
"IQuestCoderForQuestionAnswering",
|
|
]
|
|
|