mirror of
https://github.com/kvcache-ai/ktransformers.git
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1235 lines
55 KiB
Python
1235 lines
55 KiB
Python
# coding=utf-8
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from functools import partial
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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.functional as F
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from torch import nn
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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_outputs import (
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MoeCausalLMOutputWithPast,
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MoeModelOutputWithPast
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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 LossKwargs, can_return_tuple, is_torch_flex_attn_available, logging
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from .configuration_smallthinker import SmallthinkerConfig
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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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class SmallthinkerHierarchicalMLP(nn.Module):
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def __init__(self, config: SmallthinkerConfig):
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super().__init__()
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self.config = config
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self.hidden_dim = config.hidden_size
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self.ffn_dim = config.moe_ffn_hidden_size
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self.moe_enable_secondary_experts = config.moe_enable_secondary_experts
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if self.moe_enable_secondary_experts:
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self.num_secondary_experts = config.moe_num_secondary_experts
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self.secondary_expert_size = config.moe_secondary_expert_size
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self.secondary_gate = nn.Linear(self.hidden_dim, self.num_secondary_experts, bias=False)
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self.up = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
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self.gate = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
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self.down = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
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def forward(self, secondary_gate_input: torch.Tensor, hidden_states: torch.Tensor):
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if self.moe_enable_secondary_experts:
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secondary_gate_logits = F.sigmoid(self.secondary_gate(secondary_gate_input)) > 0.5
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secondary_gate_mask = secondary_gate_logits.unsqueeze(-1)
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current_hidden_states = self.up(hidden_states) * F.relu(self.gate(hidden_states))
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activated_output = current_hidden_states
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batch_size, intermediate_size = activated_output.shape
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if self.moe_enable_secondary_experts:
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num_groups = intermediate_size // self.secondary_expert_size
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activated_output = activated_output.view(batch_size, num_groups, self.secondary_expert_size)
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output = activated_output * secondary_gate_mask
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else:
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output = activated_output
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current_hidden_states = output.view(batch_size, -1)
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current_hidden_states = self.down(current_hidden_states)
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return current_hidden_states
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class SmallthinkerMoeBlock(nn.Module):
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def __init__(self, config: SmallthinkerConfig):
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super().__init__()
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self.hidden_dim = config.hidden_size
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self.num_primary_experts = config.moe_num_primary_experts
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self.enable_early_router = config.moe_enable_early_router
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self.moe_primary_router_apply_softmax = config.moe_primary_router_apply_softmax
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self.num_active_primary_experts = config.moe_num_active_primary_experts
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self.primary_router = nn.Linear(self.hidden_dim, self.num_primary_experts, bias=False)
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self.experts = nn.ModuleList([SmallthinkerHierarchicalMLP(config) for _ in range(self.num_primary_experts)])
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def forward(self, router_input: torch.Tensor, hidden_states: torch.Tensor) -> torch.Tensor:
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batch_size, sequence_length, hidden_dim = hidden_states.shape
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# Flatten the tokens into (bs * sl, hidden_dim)
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hidden_states = hidden_states.view(-1, hidden_dim)
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router_input = router_input.view(-1, hidden_dim)
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# Primary router logits: (bs * sl, n_experts)
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if self.enable_early_router:
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router_logits = self.primary_router(router_input)
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else:
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router_logits = self.primary_router(hidden_states)
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router_logits, selected_experts = torch.topk(router_logits, self.num_active_primary_experts, dim=-1)
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if self.moe_primary_router_apply_softmax:
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routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
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else:
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routing_weights = F.sigmoid(router_logits)
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routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
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routing_weights = routing_weights.to(hidden_states.dtype)
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# Prepare the final tensor
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final_hidden_states = torch.zeros(
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(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
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)
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# One hot encode the selected experts to create an expert mask
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# this will be used to easily index which expert is going to be sollicitated
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expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_primary_experts).permute(2, 1, 0)
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expert_hitted = (expert_mask.sum(dim=(-1, -2)) > 0).nonzero(as_tuple=True)[0].tolist()
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for expert_idx in expert_hitted:
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expert_layer = self.experts[expert_idx]
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idx, top_x = torch.where(expert_mask[expert_idx])
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# Index the correct hidden states and compute the expert hidden state for
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# the current expert. We need to make sure to multiply the output hidden
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# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
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# current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
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# current_router_input = router_input[None, top_x].reshape(-1, hidden_dim)
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current_state = hidden_states[top_x].reshape(-1, hidden_dim)
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current_router_input = router_input[top_x].reshape(-1, hidden_dim)
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current_hidden_states = expert_layer(current_router_input, current_state) * routing_weights[top_x, idx, None]
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# However `index_add_` only support torch tensors for indexing so we'll use the `top_x` tensor here.
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final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
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final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
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return final_hidden_states, router_logits
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class SmallthinkerDenseMlpBlock(nn.Module):
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def __init__(self, config: SmallthinkerConfig):
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super().__init__()
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hidden_dim = config.hidden_size
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ffn_dim = config.dense_ffn_hidden_size
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self.up = nn.Linear(hidden_dim, ffn_dim, bias=False)
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self.gate = nn.Linear(hidden_dim, ffn_dim, bias=False)
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self.down = nn.Linear(ffn_dim, hidden_dim, bias=False)
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# Offer unified interface for SmallthinkerMoeBlock and SmallthinkerDenseMlpBlock, though router_input is not used here
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def forward(self, router_input: torch.Tensor, hidden_states: torch.Tensor) -> torch.Tensor:
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current_hidden_states = self.up(hidden_states) * F.relu(self.gate(hidden_states))
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current_hidden_states = self.down(current_hidden_states)
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return current_hidden_states, None
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class SmallthinkerRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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SmallthinkerRMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.hidden_size = hidden_size
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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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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,
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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 SmallthinkerAttention(nn.Module):
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def __init__(self, config: SmallthinkerConfig, 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 # For KVCache management
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self.head_dim = config.head_dim
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self.num_attention_heads = config.num_attention_heads
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self.num_key_value_heads = config.num_key_value_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.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
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self.sliding_window = config.sliding_window_size if config.sliding_window_layout[layer_idx] else None
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self.use_qk_norm = config.use_qk_norm
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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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if self.use_qk_norm:
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raise NotImplementedError("use_qk_norm is not implemented yet")
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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).view(hidden_shape).transpose(1, 2)
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key_states = 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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if 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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else:
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cos, sin = None, None
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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 == "sdpa":
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raise NotImplementedError("SDPA impl is buggy for now. NEVER TRY TO USE IT.")
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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`. Falling back to "
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'eager attention. This warning can be removed using the argument `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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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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sliding_window=self.sliding_window, # main diff with Llama
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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 SmallthinkerDecoderLayer(nn.Module):
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def __init__(self, config: SmallthinkerConfig, 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 = SmallthinkerAttention(config, layer_idx)
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self.block_sparse_moe = SmallthinkerMoeBlock(config) if config.moe_layer_layout[layer_idx] else SmallthinkerDenseMlpBlock(config)
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self.input_layernorm = SmallthinkerRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = SmallthinkerRMSNorm(config.hidden_size, eps=config.rms_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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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: Optional[bool] = False,
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output_router_logits: 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, # necessary, but kept here for BC
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
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"""
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Args:
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hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
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attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
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`(batch, sequence_length)` where padding elements are indicated by 0.
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past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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output_router_logits (`bool`, *optional*):
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Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
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should not be returned during inference.
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use_cache (`bool`, *optional*):
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If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
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(see `past_key_values`).
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cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
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Indices depicting the position of the input sequence tokens in the sequence.
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kwargs (`dict`, *optional*):
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Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
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into the model
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"""
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# print(f"hidden states, shape {hidden_states.shape}: {hidden_states}") # debug print
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residual = hidden_states
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router_input = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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# Self Attention
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hidden_states, self_attn_weights = self.self_attn(
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hidden_states=hidden_states,
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position_embeddings=position_embeddings,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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**kwargs,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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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, router_logits = self.block_sparse_moe(router_input, hidden_states)
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hidden_states = residual + hidden_states # SYNC after_moe_residual_value=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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if output_router_logits:
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outputs += (router_logits,)
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return outputs
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|
class SmallthinkerRotaryEmbedding(nn.Module):
|
|
def __init__(self, config: SmallthinkerConfig, device=None):
|
|
super().__init__()
|
|
# BC: "rope_type" was originally "type"
|
|
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
|
|
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
|
else:
|
|
self.rope_type = "default"
|
|
self.max_seq_len_cached = config.max_position_embeddings
|
|
self.original_max_seq_len = config.max_position_embeddings
|
|
|
|
self.config = config
|
|
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
|
|
|
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
|
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
|
self.original_inv_freq = self.inv_freq
|
|
|
|
@torch.no_grad()
|
|
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
|
|
def forward(self, x, position_ids):
|
|
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
|
|
position_ids_expanded = position_ids[:, None, :].float()
|
|
|
|
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
|
|
with torch.autocast(device_type=device_type, enabled=False): # Force float32
|
|
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
|
emb = torch.cat((freqs, freqs), dim=-1)
|
|
cos = emb.cos() * self.attention_scaling
|
|
sin = emb.sin() * self.attention_scaling
|
|
|
|
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
|
|
|
|
|
class SmallthinkerPreTrainedModel(PreTrainedModel):
|
|
config_class = SmallthinkerConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["SmallthinkerDecoderLayer"]
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_supports_flash_attn_2 = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
_supports_cache_class = True
|
|
_supports_quantized_cache = True
|
|
_supports_static_cache = False # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
|
|
_supports_attention_backend = True
|
|
|
|
def _init_weights(self, module):
|
|
std = self.config.initializer_range
|
|
if isinstance(module, nn.Linear):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, SmallthinkerRMSNorm):
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
# @auto_docstring
|
|
class SmallthinkerModel(SmallthinkerPreTrainedModel):
|
|
def __init__(self, config: SmallthinkerConfig):
|
|
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(
|
|
[SmallthinkerDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = SmallthinkerRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = SmallthinkerRotaryEmbedding(config=config)
|
|
self.gradient_checkpointing = False
|
|
self.rope_layout = config.rope_layout
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.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[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_router_logits: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
|
|
) -> MoeModelOutputWithPast:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_router_logits = (
|
|
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
|
)
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache()
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
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)
|
|
|
|
# print("atten mask:", attention_mask) # debug print
|
|
|
|
causal_mask = self._update_causal_mask(
|
|
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
|
)
|
|
|
|
# print("causal mask:", causal_mask) # debug print
|
|
hidden_states = inputs_embeds
|
|
# create position embeddings to be shared across the decoder layers
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
all_router_logits = () if output_router_logits else None
|
|
|
|
for layer_idx, decoder_layer in enumerate(self.layers):
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
partial(decoder_layer.__call__, **flash_attn_kwargs),
|
|
hidden_states,
|
|
causal_mask,
|
|
position_ids,
|
|
past_key_values,
|
|
output_attentions,
|
|
output_router_logits,
|
|
use_cache,
|
|
cache_position,
|
|
position_embeddings if self.rope_layout[layer_idx] else None,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
output_attentions=output_attentions,
|
|
output_router_logits=output_router_logits,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings if self.rope_layout[layer_idx] else None,
|
|
**flash_attn_kwargs,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
if output_router_logits:
|
|
all_router_logits += (layer_outputs[-1],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
return MoeModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
router_logits=all_router_logits,
|
|
)
|
|
|
|
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 Smallthinker. 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)
|
|
return attention_mask
|
|
|
|
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
|
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
|
# to infer the attention mask.
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
using_static_cache = isinstance(past_key_values, StaticCache)
|
|
using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
|
|
|
|
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
|
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,
|
|
sliding_window=self.config.sliding_window,
|
|
is_training=self.training,
|
|
):
|
|
return None
|
|
|
|
dtype = input_tensor.dtype
|
|
min_dtype = torch.finfo(dtype).min
|
|
sequence_length = input_tensor.shape[1]
|
|
# SlidingWindowCache or StaticCache
|
|
if using_sliding_window_cache or using_static_cache:
|
|
target_length = past_key_values.get_max_cache_shape()
|
|
# DynamicCache or no cache
|
|
else:
|
|
target_length = (
|
|
attention_mask.shape[-1]
|
|
if isinstance(attention_mask, torch.Tensor)
|
|
else past_seen_tokens + sequence_length + 1
|
|
)
|
|
|
|
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
|
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
|
|
):
|
|
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
|
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
|
# Details: https://github.com/pytorch/pytorch/issues/110213
|
|
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: SmallthinkerConfig,
|
|
past_key_values: Cache,
|
|
):
|
|
"""
|
|
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
|
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
|
|
|
|
Args:
|
|
attention_mask (`torch.Tensor`):
|
|
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
|
|
sequence_length (`int`):
|
|
The sequence length being processed.
|
|
target_length (`int`):
|
|
The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
|
|
dtype (`torch.dtype`):
|
|
The dtype to use for the 4D attention mask.
|
|
cache_position (`torch.Tensor`):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
batch_size (`torch.Tensor`):
|
|
Batch size.
|
|
config (`SmallthinkerConfig`):
|
|
The model's configuration class
|
|
past_key_values (`Cache`):
|
|
The cache class that is being used currently to generate
|
|
"""
|
|
if attention_mask is not None and attention_mask.dim() == 4:
|
|
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
|
|
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
|
|
)
|
|
if config.get_text_config().sliding_window is not None:
|
|
# if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
|
|
# the check is needed to verify is current checkpoint was trained with sliding window or not
|
|
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.get_text_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() # copy to contiguous memory for in-place edit
|
|
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
|
|
|
|
|
|
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
|
|
|
|
|
|
def load_balancing_loss_func(
|
|
gate_logits: Union[torch.Tensor, Tuple[torch.Tensor], None],
|
|
num_experts: Optional[int] = None,
|
|
top_k=2,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, int]:
|
|
r"""
|
|
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
|
|
|
|
See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
|
|
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
|
|
experts is too unbalanced.
|
|
|
|
Args:
|
|
gate_logits:
|
|
Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
|
|
shape [batch_size X sequence_length, num_experts].
|
|
num_experts:
|
|
Number of experts
|
|
top_k:
|
|
The number of experts to route per-token, can be also interpreted as the `top-k` routing
|
|
parameter.
|
|
attention_mask (`torch.Tensor`, *optional*):
|
|
The attention_mask used in forward function
|
|
shape [batch_size X sequence_length] if not None.
|
|
|
|
Returns:
|
|
The auxiliary loss.
|
|
"""
|
|
if gate_logits is None or not isinstance(gate_logits, tuple):
|
|
return 0
|
|
|
|
if isinstance(gate_logits, tuple):
|
|
compute_device = gate_logits[0].device
|
|
concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
|
|
|
|
routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
|
|
|
|
_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
|
|
|
|
expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
|
|
|
|
if attention_mask is None:
|
|
# Compute the percentage of tokens routed to each experts
|
|
tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
|
|
|
|
# Compute the average probability of routing to these experts
|
|
router_prob_per_expert = torch.mean(routing_weights, dim=0)
|
|
else:
|
|
batch_size, sequence_length = attention_mask.shape
|
|
num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
|
|
|
|
# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
|
|
expert_attention_mask = (
|
|
attention_mask[None, :, :, None, None]
|
|
.expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
|
|
.reshape(-1, top_k, num_experts)
|
|
.to(compute_device)
|
|
)
|
|
|
|
# Compute the percentage of tokens routed to each experts
|
|
tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
|
|
expert_attention_mask, dim=0
|
|
)
|
|
|
|
# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
|
|
router_per_expert_attention_mask = (
|
|
attention_mask[None, :, :, None]
|
|
.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
|
|
.reshape(-1, num_experts)
|
|
.to(compute_device)
|
|
)
|
|
|
|
# Compute the average probability of routing to these experts
|
|
router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
|
|
router_per_expert_attention_mask, dim=0
|
|
)
|
|
|
|
overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
|
|
return overall_loss * num_experts
|
|
|
|
|
|
# @auto_docstring
|
|
class SmallthinkerForCausalLM(SmallthinkerPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = SmallthinkerModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
# Handle tie / untie word embeddings
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
# self.num_experts = config.num_local_experts
|
|
# self.num_experts_per_tok = config.num_experts_per_tok
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.embed_tokens = value
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head = new_embeddings
|
|
|
|
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[List[torch.FloatTensor]] = 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,
|
|
output_router_logits: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**kwargs: Unpack[KwargsForCausalLM],
|
|
) -> MoeCausalLMOutputWithPast:
|
|
r"""
|
|
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, SmallthinkerForCausalLM
|
|
|
|
>>> model = SmallthinkerForCausalLM.from_pretrained("mistralai/Smallthinker-8x7B-v0.1")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Smallthinker-8x7B-v0.1")
|
|
|
|
>>> 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_router_logits = (
|
|
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
|
)
|
|
|
|
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: MoeModelOutputWithPast = 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,
|
|
output_router_logits=output_router_logits,
|
|
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, labels, self.vocab_size, **kwargs)
|
|
|
|
aux_loss = None
|
|
if output_router_logits:
|
|
aux_loss = load_balancing_loss_func(
|
|
outputs.router_logits,
|
|
self.num_experts,
|
|
self.num_experts_per_tok,
|
|
attention_mask,
|
|
)
|
|
if labels is not None:
|
|
loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
|
|
|
|
return MoeCausalLMOutputWithPast(
|
|
loss=loss,
|
|
aux_loss=aux_loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
router_logits=outputs.router_logits,
|
|
)
|
|
|
|
# No such functions for now
|
|
# #@auto_docstring(
|
|
# custom_intro="""
|
|
# The Smallthinker Model transformer with a sequence classification head on top (linear layer).
|
|
|
|
# [`SmallthinkerForSequenceClassification`] 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. Since it cannot guess the
|
|
# padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
|
# each row of the batch).
|
|
# """
|
|
# )
|
|
# class SmallthinkerForSequenceClassification(SmallthinkerPreTrainedModel):
|
|
# def __init__(self, config):
|
|
# super().__init__(config)
|
|
# self.num_labels = config.num_labels
|
|
# self.model = SmallthinkerModel(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):
|
|
# return self.model.embed_tokens
|
|
|
|
# def set_input_embeddings(self, value):
|
|
# 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:
|
|
# # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
|
|
# 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 SmallthinkerForTokenClassification(SmallthinkerPreTrainedModel):
|
|
# def __init__(self, config):
|
|
# super().__init__(config)
|
|
# self.num_labels = config.num_labels
|
|
# self.model = SmallthinkerModel(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):
|
|
# return self.model.embed_tokens
|
|
|
|
# def set_input_embeddings(self, value):
|
|
# 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 SmallthinkerForQuestionAnswering(SmallthinkerPreTrainedModel):
|
|
# base_model_prefix = "model"
|
|
|
|
# def __init__(self, config):
|
|
# super().__init__(config)
|
|
# self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
|
# self.model = SmallthinkerModel(config) # diff with Llama: transformer->model
|
|
|
|
# # Initialize weights and apply final processing
|
|
# self.post_init()
|
|
|
|
# def get_input_embeddings(self):
|
|
# return self.model.embed_tokens
|
|
|
|
# def set_input_embeddings(self, value):
|
|
# 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[Union[Cache, List[torch.FloatTensor]]] = 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:
|
|
# r"""
|
|
# start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
# Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
# Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
# are not taken into account for computing the loss.
|
|
# end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
# Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
# Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
# are not taken into account for computing the loss.
|
|
# """
|
|
|
|
# outputs: BaseModelOutputWithPast = self.model(
|
|
# 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__ = [
|
|
"SmallthinkerForCausalLM",
|
|
"SmallthinkerForQuestionAnswering",
|
|
"SmallthinkerModel",
|
|
"SmallthinkerPreTrainedModel",
|
|
"SmallthinkerForSequenceClassification",
|
|
"SmallthinkerForTokenClassification",
|
|
]
|
|
|
|
if __name__ == "__main__":
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
|
|
test_config = SmallthinkerConfig()
|
|
tokenizer = AutoTokenizer.from_pretrained("./qwen-tokenizer")
|
|
text = "Once upon a day"
|
|
tokens = tokenizer.encode_plus( text,add_special_tokens=True,return_tensors='pt')
|
|
# print(tokens)
|
|
test_model = AutoModelForCausalLM.from_pretrained(".").cuda()
|
|
|
|
output = test_model.generate(tokens)
|
|
otokens = tokenizer.decode(output[0])
|
|
# print(otokens)
|