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710 lines
29 KiB
710 lines
29 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/afmoe/modular_afmoe.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_afmoe.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2025 Arcee AI and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections.abc import Callable
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from typing import Optional
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import torch
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from torch import nn
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from ... import initialization as init
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache
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from ...generation import GenerationMixin
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from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
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from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, MoeModelOutputWithPast
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
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from ...utils.generic import check_model_inputs, maybe_autocast
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from .configuration_afmoe import AfmoeConfig
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class AfmoeRotaryEmbedding(nn.Module):
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inv_freq: torch.Tensor # fix linting for `register_buffer`
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def __init__(self, config: AfmoeConfig, device=None):
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super().__init__()
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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_type = self.config.rope_parameters["rope_type"]
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rope_init_fn: Callable = self.compute_default_rope_parameters
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if self.rope_type != "default":
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rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
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@staticmethod
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def compute_default_rope_parameters(
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config: AfmoeConfig | None = None,
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device: Optional["torch.device"] = None,
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seq_len: int | None = None,
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) -> tuple["torch.Tensor", float]:
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"""
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Computes the inverse frequencies according to the original RoPE implementation
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Args:
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config ([`~transformers.PreTrainedConfig`]):
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The model configuration.
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device (`torch.device`):
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The device to use for initialization of the inverse frequencies.
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seq_len (`int`, *optional*):
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The current sequence length. Unused for this type of RoPE.
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Returns:
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Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
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post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
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"""
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base = config.rope_parameters["rope_theta"]
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dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
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attention_factor = 1.0 # Unused in this type of RoPE
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# Compute the inverse frequencies
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inv_freq = 1.0 / (
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base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
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)
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return inv_freq, attention_factor
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@torch.no_grad()
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@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
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def forward(self, x, position_ids):
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
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with maybe_autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos() * self.attention_scaling
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sin = emb.sin() * self.attention_scaling
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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@use_kernel_forward_from_hub("RMSNorm")
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class AfmoeRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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AfmoeRMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return (self.weight * hidden_states).to(input_dtype) # main diff with Llama
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
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class AfmoeMLP(nn.Module):
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def __init__(self, config, intermediate_size=None):
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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 if intermediate_size is None else intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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return down_proj
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class AfmoeTokenChoiceRouter(nn.Module):
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"""
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Token-choice top-K router for MoE routing.
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This router assigns each token to the top-K experts based on sigmoid scores, matching the released checkpoints.
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"""
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.top_k = config.num_experts_per_tok
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self.num_experts = config.num_experts
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self.route_scale = config.route_scale
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self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
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def forward(self, hidden_states: torch.Tensor, expert_bias: torch.Tensor):
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_, _, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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scores = torch.sigmoid(self.gate(hidden_states).to(torch.float32))
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_, selected_experts = torch.topk(scores + expert_bias, k=self.top_k, dim=1)
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top_scores = scores.gather(dim=1, index=selected_experts)
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denominator = top_scores.sum(dim=-1, keepdim=True) + 1e-20
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top_scores = top_scores / denominator
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top_scores = top_scores * self.route_scale
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return top_scores, selected_experts
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class AfmoeExperts(nn.ModuleList):
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"""
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Container holding the routed experts.
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This mirrors the Experts pattern used across other MoE models to ease checkpoint conversion.
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"""
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def __init__(self, config: AfmoeConfig):
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super().__init__()
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self.top_k = config.num_experts_per_tok
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self.num_experts = config.num_experts
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for _ in range(self.num_experts):
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self.append(AfmoeMLP(config, intermediate_size=config.moe_intermediate_size))
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def forward(
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self, hidden_states: torch.Tensor, selected_experts: torch.Tensor, routing_weights: torch.Tensor
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) -> torch.Tensor:
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"""
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Args:
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hidden_states: (batch, seq, hidden)
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selected_experts: (batch, seq, top_k)
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routing_weights: (batch, seq, top_k)
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"""
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batch_size, seq_len, hidden_dim = hidden_states.shape
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if seq_len == 0:
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return hidden_states.new_zeros(batch_size, 0, hidden_dim)
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hidden_states_flat = hidden_states.view(-1, hidden_dim)
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top_k = selected_experts.shape[-1]
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# Map every token routing decision to a unique position so we can process expert by expert.
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token_indices = torch.arange(
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hidden_states_flat.shape[0], device=hidden_states.device, dtype=torch.long
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).repeat_interleave(top_k)
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expert_indices = selected_experts.reshape(-1)
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routing_weights = routing_weights.reshape(-1)
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sorting = torch.argsort(expert_indices, stable=True)
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token_indices = token_indices[sorting]
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expert_indices = expert_indices[sorting]
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routing_weights = routing_weights[sorting]
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dispatched_tokens = hidden_states_flat.index_select(0, token_indices)
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expert_outputs = torch.zeros_like(dispatched_tokens)
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unique_experts, counts = torch.unique_consecutive(expert_indices, return_counts=True)
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start = 0
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for expert_id, count in zip(unique_experts.tolist(), counts.tolist()):
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if count == 0:
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continue
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end = start + count
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expert_input = dispatched_tokens[start:end]
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expert_output = self[expert_id](expert_input)
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expert_outputs[start:end] = expert_output
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start = end
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weighted_outputs = (expert_outputs.to(torch.float32) * routing_weights.unsqueeze(-1)).to(hidden_states.dtype)
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aggregated = torch.zeros_like(hidden_states_flat)
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scatter_indices = token_indices.unsqueeze(-1).expand_as(weighted_outputs)
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aggregated.scatter_add_(0, scatter_indices, weighted_outputs)
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return aggregated.view(batch_size, seq_len, hidden_dim)
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class AfmoeMoE(nn.Module):
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"""
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Mixture of Experts (MoE) module for AFMoE.
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This module implements a sparse MoE layer with both shared experts (always active) and
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routed experts (activated based on token-choice routing).
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"""
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.router = AfmoeTokenChoiceRouter(config)
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self.shared_experts = AfmoeMLP(config, config.moe_intermediate_size * config.num_shared_experts)
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self.experts = AfmoeExperts(config)
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self.expert_bias = nn.Parameter(torch.zeros(config.num_experts), requires_grad=False)
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def forward(self, hidden_states):
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batch_size, seq_len, hidden_dim = hidden_states.shape
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hidden_states_flat = hidden_states.view(-1, hidden_dim)
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# Get routing decisions
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top_scores, selected_experts = self.router(hidden_states, self.expert_bias)
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top_scores = top_scores.view(batch_size, seq_len, self.config.num_experts_per_tok)
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selected_experts = selected_experts.view(batch_size, seq_len, self.config.num_experts_per_tok)
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# Process through shared experts
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shared_output = self.shared_experts(hidden_states_flat).view(batch_size, seq_len, hidden_dim)
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routed_output = self.experts(hidden_states, selected_experts, top_scores)
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return shared_output + routed_output
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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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@use_kernel_func_from_hub("rotary_pos_emb")
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def apply_rotary_pos_emb(q, k, cos, sin, 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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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: torch.Tensor | None,
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scaling: float,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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@use_kernelized_func(apply_rotary_pos_emb)
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class AfmoeAttention(nn.Module):
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"""
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Multi-headed attention module with optional sliding window and gating.
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This attention mechanism supports both full attention and sliding window attention,
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and includes Q/K normalization and gating of the output. It inherits from [`LlamaAttention`] to minimize the amount
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of custom logic we need to maintain.
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"""
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def __init__(self, config: AfmoeConfig, 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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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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# Parent LlamaAttention already sets: layer_idx, num_heads, num_key_value_heads, num_key_value_groups, head_dim
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# We only add AFMoE-specific attributes
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self.is_local_attention = config.layer_types[layer_idx] == "sliding_attention"
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self.sliding_window = config.sliding_window if self.is_local_attention else None
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self.q_norm = AfmoeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = AfmoeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.gate_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
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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: torch.Tensor | None,
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past_key_value: Cache | None = None,
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cache_position: torch.LongTensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor, torch.Tensor]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape)
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key_states = self.k_proj(hidden_states).view(hidden_shape)
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value_states = self.v_proj(hidden_states).view(hidden_shape)
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gate_states = self.gate_proj(hidden_states)
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query_states = self.q_norm(query_states).transpose(1, 2)
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key_states = self.k_norm(key_states).transpose(1, 2)
|
|
value_states = value_states.transpose(1, 2)
|
|
|
|
if self.is_local_attention:
|
|
cos, sin = position_embeddings
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
|
|
|
if past_key_value is not None:
|
|
cache_kwargs = {"cache_position": cache_position}
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
|
|
self.config._attn_implementation, eager_attention_forward
|
|
)
|
|
|
|
output, attn_weights = attention_interface(
|
|
self,
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attention_mask=attention_mask,
|
|
dropout=0.0 if not self.training else self.attention_dropout,
|
|
scaling=self.scaling,
|
|
sliding_window=self.sliding_window,
|
|
**kwargs,
|
|
)
|
|
|
|
output = output.view(*input_shape, -1).contiguous()
|
|
output = output * torch.sigmoid(gate_states)
|
|
attn_output = self.o_proj(output)
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class AfmoeDecoderLayer(GradientCheckpointingLayer):
|
|
"""
|
|
AFMoE decoder layer with dual normalization.
|
|
|
|
This layer applies self-attention followed by either a dense MLP or MoE block,
|
|
with dual normalization (pre and post) around each component.
|
|
"""
|
|
|
|
def __init__(self, config: AfmoeConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
self.layer_idx = layer_idx
|
|
|
|
self.self_attn = AfmoeAttention(config=config, layer_idx=layer_idx)
|
|
self.attention_type = config.layer_types[layer_idx]
|
|
|
|
# Dual normalization for attention
|
|
self.input_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
# Dual normalization for FFN
|
|
self.pre_mlp_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_mlp_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
# MoE or dense FFN
|
|
self.moe_enabled = layer_idx >= config.num_dense_layers
|
|
if self.moe_enabled:
|
|
self.mlp = AfmoeMoE(config)
|
|
else:
|
|
self.mlp = AfmoeMLP(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_value: Cache | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> torch.FloatTensor:
|
|
residual = hidden_states
|
|
|
|
# Self Attention with dual normalization
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
hidden_states, _ = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# FFN with dual normalization
|
|
residual = hidden_states
|
|
hidden_states = self.pre_mlp_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = self.post_mlp_layernorm(hidden_states)
|
|
|
|
hidden_states = residual + hidden_states
|
|
return hidden_states
|
|
|
|
|
|
class AfmoePreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config: AfmoeConfig
|
|
base_model_prefix = "model"
|
|
_no_split_modules = ["AfmoeDecoderLayer"]
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_can_record_outputs = {
|
|
"hidden_states": AfmoeDecoderLayer,
|
|
"attentions": AfmoeAttention,
|
|
}
|
|
_keep_in_fp32_modules = [
|
|
"input_layernorm",
|
|
"post_attention_layernorm",
|
|
"pre_mlp_layernorm",
|
|
"post_mlp_layernorm",
|
|
"q_norm",
|
|
"k_norm",
|
|
"norm",
|
|
"expert_bias",
|
|
]
|
|
_supports_sdpa = True
|
|
_supports_flash_attn = True
|
|
_supports_flex_attn = True
|
|
_supports_attention_backend = True
|
|
supports_gradient_checkpointing = True
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
super()._init_weights(module)
|
|
if isinstance(module, AfmoeTokenChoiceRouter):
|
|
init.zeros_(module.gate.weight)
|
|
elif isinstance(module, AfmoeMoE):
|
|
init.zeros_(module.expert_bias)
|
|
|
|
|
|
@auto_docstring
|
|
class AfmoeModel(AfmoePreTrainedModel):
|
|
"""
|
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`AfmoeDecoderLayer`]
|
|
|
|
Args:
|
|
config: AfmoeConfig
|
|
"""
|
|
|
|
def __init__(self, config: AfmoeConfig):
|
|
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(
|
|
[AfmoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = AfmoeRotaryEmbedding(config=config)
|
|
self.gradient_checkpointing = False
|
|
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
@check_model_inputs
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple | MoeModelOutputWithPast:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache(config=self.config)
|
|
|
|
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)
|
|
|
|
# It may already have been prepared by e.g. `generate`
|
|
if not isinstance(causal_mask_mapping := attention_mask, dict):
|
|
mask_kwargs = {
|
|
"config": self.config,
|
|
"input_embeds": inputs_embeds,
|
|
"attention_mask": attention_mask,
|
|
"cache_position": cache_position,
|
|
"past_key_values": past_key_values,
|
|
}
|
|
causal_mask_mapping = {
|
|
"full_attention": create_causal_mask(**mask_kwargs),
|
|
"sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
|
|
}
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
# Apply muP input scaling if enabled
|
|
if self.config.mup_enabled:
|
|
hidden_states = hidden_states * (self.config.hidden_size**0.5)
|
|
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
for decoder_layer in self.layers:
|
|
hidden_states = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask_mapping[decoder_layer.attention_type],
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
return MoeModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values if use_cache else None,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class AfmoeForCausalLM(AfmoePreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
|
|
_tp_plan = {"lm_head": "colwise_gather_output"}
|
|
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = AfmoeModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> CausalLMOutputWithPast:
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, AfmoeForCausalLM
|
|
|
|
>>> model = AfmoeForCausalLM.from_pretrained("meta-afmoe/Afmoe-2-7b-hf")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("meta-afmoe/Afmoe-2-7b-hf")
|
|
|
|
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
|
```"""
|
|
outputs: BaseModelOutputWithPast = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs.last_hidden_state
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = ["AfmoeForCausalLM", "AfmoeModel", "AfmoePreTrainedModel"]
|