1097 lines
50 KiB
Python
1097 lines
50 KiB
Python
# 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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"""PyTorch OLMoE model."""
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import math
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from typing import Optional, Union
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache, StaticCache
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from ...generation import GenerationMixin
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from ...modeling_attn_mask_utils import AttentionMaskConverter
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from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from ...modeling_utils import PreTrainedModel
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from ...utils import auto_docstring, logging
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from .configuration_olmoe import OlmoeConfig
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if is_flash_attn_available():
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from ...modeling_flash_attention_utils import _flash_attention_forward
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logger = logging.get_logger(__name__)
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# Copied from transformers.models.mixtral.modeling_mixtral.load_balancing_loss_func
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def load_balancing_loss_func(
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gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
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num_experts: Optional[int] = None,
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top_k=2,
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attention_mask: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, int]:
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r"""
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Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
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See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
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function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
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experts is too unbalanced.
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Args:
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gate_logits:
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Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
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shape [batch_size X sequence_length, num_experts].
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num_experts:
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Number of experts
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top_k:
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The number of experts to route per-token, can be also interpreted as the `top-k` routing
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parameter.
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attention_mask (`torch.Tensor`, *optional*):
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The attention_mask used in forward function
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shape [batch_size X sequence_length] if not None.
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Returns:
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The auxiliary loss.
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"""
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if gate_logits is None or not isinstance(gate_logits, tuple):
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return 0
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if isinstance(gate_logits, tuple):
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compute_device = gate_logits[0].device
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concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
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routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
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_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
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expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
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if attention_mask is None:
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# Compute the percentage of tokens routed to each experts
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tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
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# Compute the average probability of routing to these experts
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router_prob_per_expert = torch.mean(routing_weights, dim=0)
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else:
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batch_size, sequence_length = attention_mask.shape
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num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
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# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
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expert_attention_mask = (
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attention_mask[None, :, :, None, None]
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.expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
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.reshape(-1, top_k, num_experts)
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.to(compute_device)
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)
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# Compute the percentage of tokens routed to each experts
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tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
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expert_attention_mask, dim=0
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)
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# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
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router_per_expert_attention_mask = (
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attention_mask[None, :, :, None]
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.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
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.reshape(-1, num_experts)
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.to(compute_device)
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)
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# Compute the average probability of routing to these experts
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router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
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router_per_expert_attention_mask, dim=0
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)
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overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
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return overall_loss * num_experts
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class OlmoeRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-5):
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"""
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OlmoeRMSNorm 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)
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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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# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Olmoe
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class OlmoeRotaryEmbedding(nn.Module):
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def __init__(self, config: OlmoeConfig, device=None):
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super().__init__()
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# BC: "rope_type" was originally "type"
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if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
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self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
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else:
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self.rope_type = "default"
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self.max_seq_len_cached = config.max_position_embeddings
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self.original_max_seq_len = config.max_position_embeddings
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self.config = config
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self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.original_inv_freq = self.inv_freq
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@torch.no_grad()
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@dynamic_rope_update # 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 torch.autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos() * self.attention_scaling
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sin = emb.sin() * self.attention_scaling
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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# Copied from transformers.models.llama.modeling_llama.rotate_half
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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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# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
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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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# Copied from transformers.models.olmo.modeling_olmo.OlmoMLP with Olmo->Olmoe
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class OlmoeMLP(nn.Module):
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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.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=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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# Copied from transformers.models.llama.modeling_llama.repeat_kv
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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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class OlmoeAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: OlmoeConfig, layer_idx: Optional[int] = None):
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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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if layer_idx is None:
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logger.warning_once(
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f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
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"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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self.attention_dropout = config.attention_dropout
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.hidden_size // self.num_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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self.is_causal = True
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if (self.head_dim * self.num_heads) != self.hidden_size:
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raise ValueError(
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads})."
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)
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
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self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
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self.q_norm = OlmoeRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
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self.k_norm = OlmoeRMSNorm(
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(self.hidden_size // self.num_heads) * self.num_key_value_heads, eps=config.rms_norm_eps
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
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**kwargs,
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_norm(self.q_proj(hidden_states))
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key_states = self.k_norm(self.k_proj(hidden_states))
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value_states = self.v_proj(hidden_states)
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if self.config.clip_qkv is not None:
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query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attention_mask is not None: # no matter the length, we just slice it
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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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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights
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class OlmoeFlashAttention2(OlmoeAttention):
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"""
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OLMoE flash attention module. This module inherits from `OlmoeAttention` as the weights of the module stays
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untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
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flash attention and deal with padding tokens in case the input contains any of them.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
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# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
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# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
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self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
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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.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
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**kwargs,
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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output_attentions = False
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_norm(self.q_proj(hidden_states))
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key_states = self.k_norm(self.k_proj(hidden_states))
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value_states = self.v_proj(hidden_states)
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if self.config.clip_qkv is not None:
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query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
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value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
|
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# Flash attention requires the input to have the shape
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# batch_size x seq_length x head_dim x hidden_dim
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# therefore we just need to keep the original shape
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
|
||
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:
|
||
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||
|
||
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
||
# to be able to avoid many of these transpose/reshape/view.
|
||
query_states = query_states.transpose(1, 2)
|
||
key_states = key_states.transpose(1, 2)
|
||
value_states = value_states.transpose(1, 2)
|
||
|
||
dropout_rate = self.attention_dropout if self.training else 0.0
|
||
|
||
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
||
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
||
# cast them back in the correct dtype just to be sure everything works as expected.
|
||
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
||
# in fp32. (OlmoeRMSNorm handles it correctly)
|
||
|
||
input_dtype = query_states.dtype
|
||
device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
|
||
if input_dtype == torch.float32:
|
||
if torch.is_autocast_enabled():
|
||
target_dtype = (
|
||
torch.get_autocast_dtype(device_type)
|
||
if hasattr(torch, "get_autocast_dtype")
|
||
else torch.get_autocast_gpu_dtype()
|
||
)
|
||
# Handle the case where the model is quantized
|
||
elif hasattr(self.config, "_pre_quantization_dtype"):
|
||
target_dtype = self.config._pre_quantization_dtype
|
||
else:
|
||
target_dtype = self.q_proj.weight.dtype
|
||
|
||
logger.warning_once(
|
||
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
||
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
||
f" {target_dtype}."
|
||
)
|
||
|
||
query_states = query_states.to(target_dtype)
|
||
key_states = key_states.to(target_dtype)
|
||
value_states = value_states.to(target_dtype)
|
||
|
||
attn_output = _flash_attention_forward(
|
||
query_states,
|
||
key_states,
|
||
value_states,
|
||
attention_mask,
|
||
q_len,
|
||
dropout=dropout_rate,
|
||
use_top_left_mask=self._flash_attn_uses_top_left_mask,
|
||
is_causal=self.is_causal,
|
||
)
|
||
|
||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
||
attn_output = self.o_proj(attn_output)
|
||
|
||
if not output_attentions:
|
||
attn_weights = None
|
||
|
||
return attn_output, attn_weights
|
||
|
||
|
||
class OlmoeSdpaAttention(OlmoeAttention):
|
||
"""
|
||
OLMoE attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
||
`OlmoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
||
SDPA API.
|
||
"""
|
||
|
||
# Adapted from OlmoeAttention.forward
|
||
def forward(
|
||
self,
|
||
hidden_states: torch.Tensor,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_value: Optional[Cache] = None,
|
||
output_attentions: bool = False,
|
||
use_cache: bool = False,
|
||
cache_position: Optional[torch.LongTensor] = None,
|
||
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
|
||
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
|
||
if output_attentions:
|
||
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
||
logger.warning_once(
|
||
"OlmoeModel is using OlmoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
||
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
||
)
|
||
return super().forward(
|
||
hidden_states=hidden_states,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_value=past_key_value,
|
||
output_attentions=output_attentions,
|
||
use_cache=use_cache,
|
||
cache_position=cache_position,
|
||
position_embeddings=position_embeddings,
|
||
)
|
||
|
||
bsz, q_len, _ = hidden_states.size()
|
||
|
||
query_states = self.q_norm(self.q_proj(hidden_states))
|
||
key_states = self.k_norm(self.k_proj(hidden_states))
|
||
value_states = self.v_proj(hidden_states)
|
||
|
||
if self.config.clip_qkv is not None:
|
||
query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
|
||
key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
|
||
value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
|
||
|
||
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||
|
||
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:
|
||
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||
|
||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||
|
||
causal_mask = attention_mask
|
||
# if attention_mask is not None and cache_position is not None:
|
||
if attention_mask is not None:
|
||
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
|
||
|
||
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
||
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
||
if query_states.device.type == "cuda" and causal_mask is not None:
|
||
query_states = query_states.contiguous()
|
||
key_states = key_states.contiguous()
|
||
value_states = value_states.contiguous()
|
||
|
||
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
|
||
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
|
||
is_causal = True if causal_mask is None and q_len > 1 else False
|
||
|
||
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
||
query_states,
|
||
key_states,
|
||
value_states,
|
||
attn_mask=causal_mask,
|
||
dropout_p=self.attention_dropout if self.training else 0.0,
|
||
is_causal=is_causal,
|
||
)
|
||
|
||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||
attn_output = attn_output.view(bsz, q_len, self.hidden_size)
|
||
|
||
attn_output = self.o_proj(attn_output)
|
||
|
||
return attn_output, None
|
||
|
||
|
||
OLMOE_ATTENTION_CLASSES = {
|
||
"eager": OlmoeAttention,
|
||
"flash_attention_2": OlmoeFlashAttention2,
|
||
"sdpa": OlmoeSdpaAttention,
|
||
}
|
||
|
||
|
||
class OlmoeSparseMoeBlock(nn.Module):
|
||
def __init__(self, config):
|
||
super().__init__()
|
||
self.num_experts = config.num_experts
|
||
self.top_k = config.num_experts_per_tok
|
||
self.norm_topk_prob = config.norm_topk_prob
|
||
self.gate = nn.Linear(config.hidden_size, self.num_experts, bias=False)
|
||
self.experts = nn.ModuleList([OlmoeMLP(config) for _ in range(self.num_experts)])
|
||
|
||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
||
hidden_states = hidden_states.view(-1, hidden_dim)
|
||
# router_logits: (batch * sequence_length, n_experts)
|
||
router_logits = self.gate(hidden_states)
|
||
|
||
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
|
||
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
|
||
if self.norm_topk_prob:
|
||
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
||
# we cast back to the input dtype
|
||
routing_weights = routing_weights.to(hidden_states.dtype)
|
||
|
||
final_hidden_states = torch.zeros(
|
||
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
|
||
)
|
||
|
||
# One hot encode the selected experts to create an expert mask
|
||
# this will be used to easily index which expert is going to be selected
|
||
expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
|
||
|
||
# Loop over all available experts in the model and perform the computation on each expert
|
||
for expert_idx in range(self.num_experts):
|
||
expert_layer = self.experts[expert_idx]
|
||
idx, top_x = torch.where(expert_mask[expert_idx])
|
||
|
||
# Index the correct hidden states and compute the expert hidden state for
|
||
# the current expert. We need to make sure to multiply the output hidden
|
||
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
|
||
current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
|
||
current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
|
||
|
||
# However `index_add_` only support torch tensors for indexing so we'll use
|
||
# the `top_x` tensor here.
|
||
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
||
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
|
||
return final_hidden_states, router_logits
|
||
|
||
|
||
class OlmoeDecoderLayer(GradientCheckpointingLayer):
|
||
def __init__(self, config: OlmoeConfig, layer_idx: int):
|
||
super().__init__()
|
||
self.hidden_size = config.hidden_size
|
||
|
||
self.self_attn = OLMOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
|
||
|
||
self.mlp = OlmoeSparseMoeBlock(config)
|
||
self.input_layernorm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
self.post_attention_layernorm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
|
||
def forward(
|
||
self,
|
||
hidden_states: torch.Tensor,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_value: Optional[Cache] = None,
|
||
output_attentions: Optional[bool] = False,
|
||
output_router_logits: Optional[bool] = False,
|
||
use_cache: Optional[bool] = False,
|
||
cache_position: Optional[torch.LongTensor] = None,
|
||
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
|
||
**kwargs,
|
||
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
||
"""
|
||
Args:
|
||
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
||
attention_mask (`torch.FloatTensor`, *optional*):
|
||
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
|
||
query_sequence_length, key_sequence_length)` if default attention is used.
|
||
output_attentions (`bool`, *optional*):
|
||
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
||
returned tensors for more detail.
|
||
output_router_logits (`bool`, *optional*):
|
||
Whether or not to return the logits of all the routers. They are useful for computing the router loss,
|
||
and should not be returned during inference.
|
||
use_cache (`bool`, *optional*):
|
||
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
||
(see `past_key_values`).
|
||
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
||
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
||
Indices depicting the position of the input sequence tokens in the sequence
|
||
position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
|
||
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
|
||
with `head_dim` being the embedding dimension of each attention head.
|
||
kwargs (`dict`, *optional*):
|
||
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
|
||
into the model
|
||
"""
|
||
residual = hidden_states
|
||
|
||
hidden_states = self.input_layernorm(hidden_states)
|
||
|
||
# Self Attention
|
||
hidden_states, self_attn_weights = self.self_attn(
|
||
hidden_states=hidden_states,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_value=past_key_value,
|
||
output_attentions=output_attentions,
|
||
use_cache=use_cache,
|
||
cache_position=cache_position,
|
||
position_embeddings=position_embeddings,
|
||
**kwargs,
|
||
)
|
||
hidden_states = residual + hidden_states
|
||
|
||
# Fully Connected
|
||
residual = hidden_states
|
||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||
hidden_states, router_logits = self.mlp(hidden_states)
|
||
hidden_states = residual + hidden_states
|
||
|
||
outputs = (hidden_states,)
|
||
|
||
if output_attentions:
|
||
outputs += (self_attn_weights,)
|
||
|
||
if output_router_logits:
|
||
outputs += (router_logits,)
|
||
|
||
return outputs
|
||
|
||
|
||
@auto_docstring
|
||
class OlmoePreTrainedModel(PreTrainedModel):
|
||
config: OlmoeConfig
|
||
base_model_prefix = "model"
|
||
supports_gradient_checkpointing = True
|
||
_no_split_modules = ["OlmoeDecoderLayer"]
|
||
_skip_keys_device_placement = ["past_key_values"]
|
||
_supports_flash_attn = True
|
||
_supports_sdpa = True
|
||
|
||
_can_compile_fullgraph = False # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
|
||
|
||
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, OlmoeRMSNorm):
|
||
module.weight.data.fill_(1.0)
|
||
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_()
|
||
|
||
|
||
@auto_docstring
|
||
class OlmoeModel(OlmoePreTrainedModel):
|
||
def __init__(self, config: OlmoeConfig):
|
||
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(
|
||
[OlmoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
||
)
|
||
self.norm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
self.rotary_emb = OlmoeRotaryEmbedding(config=config)
|
||
self.gradient_checkpointing = False
|
||
|
||
# Initialize weights and apply final processing
|
||
self.post_init()
|
||
|
||
@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,
|
||
use_cache: Optional[bool] = None,
|
||
output_attentions: Optional[bool] = None,
|
||
output_hidden_states: Optional[bool] = None,
|
||
output_router_logits: Optional[bool] = None,
|
||
return_dict: Optional[bool] = None,
|
||
cache_position: Optional[torch.LongTensor] = None,
|
||
) -> Union[tuple, 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
|
||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
||
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 and use_cache:
|
||
logger.warning_once(
|
||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
||
)
|
||
use_cache = False
|
||
|
||
if inputs_embeds is None:
|
||
inputs_embeds = self.embed_tokens(input_ids)
|
||
|
||
# TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
|
||
if not isinstance(past_key_values, (type(None), Cache)):
|
||
raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
|
||
|
||
if use_cache and past_key_values is None:
|
||
past_key_values = DynamicCache()
|
||
|
||
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)
|
||
|
||
causal_mask = self._update_causal_mask(
|
||
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
||
)
|
||
|
||
# embed positions
|
||
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 decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
||
if output_hidden_states:
|
||
all_hidden_states += (hidden_states,)
|
||
|
||
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,
|
||
)
|
||
|
||
hidden_states = layer_outputs[0]
|
||
|
||
if output_attentions:
|
||
all_self_attns += (layer_outputs[1],)
|
||
|
||
if output_router_logits and layer_outputs[-1] is not None:
|
||
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,)
|
||
|
||
if not return_dict:
|
||
return tuple(
|
||
v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] if v is not None
|
||
)
|
||
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: torch.Tensor,
|
||
input_tensor: torch.Tensor,
|
||
cache_position: torch.Tensor,
|
||
past_key_values: Cache,
|
||
output_attentions: bool,
|
||
):
|
||
if self.config._attn_implementation == "flash_attention_2":
|
||
if attention_mask is not None and 0.0 in attention_mask:
|
||
return attention_mask
|
||
return None
|
||
|
||
# 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)
|
||
|
||
# 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 and not output_attentions:
|
||
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
||
attention_mask,
|
||
inputs_embeds=input_tensor,
|
||
past_key_values_length=past_seen_tokens,
|
||
is_training=self.training,
|
||
):
|
||
return None
|
||
|
||
dtype, device = input_tensor.dtype, input_tensor.device
|
||
sequence_length = input_tensor.shape[1]
|
||
if using_static_cache:
|
||
target_length = past_key_values.get_max_cache_shape()
|
||
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,
|
||
device=device,
|
||
cache_position=cache_position,
|
||
batch_size=input_tensor.shape[0],
|
||
)
|
||
|
||
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
|
||
min_dtype = torch.finfo(dtype).min
|
||
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,
|
||
device: torch.device,
|
||
cache_position: torch.Tensor,
|
||
batch_size: int,
|
||
**kwargs,
|
||
):
|
||
"""
|
||
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.
|
||
device (`torch.device`):
|
||
The device to place the 4D attention mask on.
|
||
cache_position (`torch.Tensor`):
|
||
Indices depicting the position of the input sequence tokens in the sequence.
|
||
batch_size (`torch.Tensor`):
|
||
Batch size.
|
||
"""
|
||
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=device
|
||
)
|
||
if sequence_length != 1:
|
||
causal_mask = torch.triu(causal_mask, diagonal=1)
|
||
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
|
||
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
|
||
mask_length = attention_mask.shape[-1]
|
||
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
|
||
padding_mask = padding_mask == 0
|
||
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
||
padding_mask, min_dtype
|
||
)
|
||
|
||
return causal_mask
|
||
|
||
|
||
class OlmoeForCausalLM(OlmoePreTrainedModel, GenerationMixin):
|
||
_tied_weights_keys = ["lm_head.weight"]
|
||
|
||
def __init__(self, config):
|
||
super().__init__(config)
|
||
self.model = OlmoeModel(config)
|
||
self.vocab_size = config.vocab_size
|
||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||
|
||
self.router_aux_loss_coef = config.router_aux_loss_coef
|
||
self.num_experts = config.num_experts
|
||
self.num_experts_per_tok = config.num_experts_per_tok
|
||
# Initialize weights and apply final processing
|
||
self.post_init()
|
||
|
||
def set_decoder(self, decoder):
|
||
self.model = decoder
|
||
|
||
def get_decoder(self):
|
||
return self.model
|
||
|
||
@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,
|
||
output_router_logits: Optional[bool] = None,
|
||
return_dict: Optional[bool] = None,
|
||
cache_position: Optional[torch.LongTensor] = None,
|
||
logits_to_keep: Union[int, torch.Tensor] = 0,
|
||
**kwargs,
|
||
) -> Union[tuple, 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, OlmoeForCausalLM
|
||
|
||
>>> model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924")
|
||
>>> tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924")
|
||
|
||
>>> 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 sure if you’re conscious of this, but I’m'
|
||
```
|
||
"""
|
||
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
|
||
)
|
||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
||
outputs = 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,
|
||
return_dict=return_dict,
|
||
cache_position=cache_position,
|
||
)
|
||
|
||
hidden_states = outputs[0]
|
||
# 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 if return_dict else outputs[-1],
|
||
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
|
||
|
||
if not return_dict:
|
||
output = (logits,) + outputs[1:]
|
||
if output_router_logits:
|
||
output = (aux_loss,) + output
|
||
return (loss,) + output if loss is not None else output
|
||
|
||
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,
|
||
)
|
||
|
||
|
||
__all__ = ["OlmoeForCausalLM", "OlmoeModel", "OlmoePreTrainedModel"]
|