1216 lines
53 KiB
Python
1216 lines
53 KiB
Python
# coding=utf-8
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# Copyright 2024 JetMoe 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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"""PyTorch JetMoe 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.utils.checkpoint
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from torch import nn
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from torch.nn import functional as F
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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 ...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 (
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GenericForSequenceClassification,
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GradientCheckpointingLayer,
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)
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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, can_return_tuple, is_torch_flex_attn_available, logging
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from .configuration_jetmoe import JetMoeConfig
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if is_torch_flex_attn_available():
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from torch.nn.attention.flex_attention import BlockMask
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from ...integrations.flex_attention import make_flex_block_causal_mask
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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 JetMoeParallelExperts(nn.Module):
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def __init__(self, num_experts: int, input_size: int, output_size: int) -> None:
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"""
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Initialize the JetMoeParallelExperts module.
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The experts weights are stored in [num_experts, output_size, input_size] format. Such that it's compatible with
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many MoE libraries, such as [Megablock](https://github.com/databricks/megablocks) and
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[ScatterMoE](https://github.com/shawntan/scattermoe), as well as the
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[MoE kernel](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/fused_moe.py)
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used in vllm.
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Args:
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num_experts (int):
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Number of experts.
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input_size (int):
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Size of the input.
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output_size (int):
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Size of the output.
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.empty(num_experts, output_size, input_size))
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self.num_experts = num_experts
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self.input_size = input_size
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self.output_size = output_size
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def forward(self, inputs, expert_size):
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"""
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Forward pass of the JetMoeParallelExperts module.
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Args:
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inputs (Tensor):
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Input tensor.
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expert_size:
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Expert size information.
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Returns:
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Tensor: Output tensor.
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"""
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input_list = inputs.split(expert_size, dim=0)
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output_list = []
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for i in range(self.num_experts):
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output_list.append(F.linear(input_list[i], self.weight[i]))
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results = torch.cat(output_list, dim=0)
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return results
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class JetMoeTopKGating(nn.Module):
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def __init__(self, input_size: int, num_experts: int, top_k: int):
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"""
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Initialize the top-k gating mechanism.
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Args:
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input_size (`int`):
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Size of the input.
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num_experts (`int`):
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Number of experts.
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top_k (`int`):
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Number of top experts to select.
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"""
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super().__init__()
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self.num_experts = num_experts
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self.input_size = input_size
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self.top_k = top_k
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self.layer = nn.Linear(input_size, num_experts, bias=False)
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def forward(self, hidden_states):
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# compute the top_k routing decision
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logits = self.layer(hidden_states).float() # [batch_size x seq_len, num_experts]
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top_k_logits, top_k_indices = logits.topk(self.top_k, dim=1) # [num_tokens, top_k]
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top_k_gates = torch.softmax(top_k_logits, dim=1).type_as(hidden_states) # [num_tokens, top_k]
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# compute number of input given to each expert
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zeros = torch.zeros(
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[top_k_gates.size(0), self.num_experts], dtype=top_k_gates.dtype, device=top_k_gates.device
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) # [num_tokens, num_experts]
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gates = zeros.scatter(1, top_k_indices, 1) # [num_tokens, num_experts]
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expert_size = gates.long().sum(0) # [num_experts,]
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# (This cause torch.compile to fail with `torch._dynamo.exc.Unsupported: Backend compiler failed with a fake tensor exception at`)
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# (and `DataDependentOutputException`)
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expert_size = expert_size.tolist()
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# sort and group input tokens according to expert assignment
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top_k_experts = top_k_indices.flatten() # [num_tokens * top_k]
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_, index_sorted_experts = top_k_experts.sort(0) # [num_tokens * top_k]
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batch_index = index_sorted_experts.div(self.top_k, rounding_mode="trunc") # [num_tokens * top_k]
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# gather the gate values for grouped input tokens
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top_k_gates = top_k_gates.flatten() # [num_tokens * top_k]
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batch_gates = top_k_gates[index_sorted_experts] # [num_tokens * top_k]
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return index_sorted_experts, batch_index, batch_gates, expert_size, logits
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class JetMoeMoE(nn.Module):
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"""
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A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
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Args:
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config:
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Configuration object with model hyperparameters.
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"""
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def __init__(self, config: JetMoeConfig):
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super().__init__()
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self.input_size = config.hidden_size
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self.hidden_size = config.intermediate_size
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self.activation = ACT2FN[config.activation_function]
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self.bias = torch.nn.Parameter(torch.empty(self.input_size))
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self.input_linear = JetMoeParallelExperts(config.num_local_experts, self.input_size, self.hidden_size * 2)
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self.output_linear = JetMoeParallelExperts(config.num_local_experts, self.hidden_size, self.input_size)
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self.router = JetMoeTopKGating(
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input_size=self.input_size,
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num_experts=config.num_local_experts,
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top_k=config.num_experts_per_tok,
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)
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def forward(self, layer_input):
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"""
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Forward pass of the mixture of experts layer.
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Args:
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layer_input (Tensor):
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Input tensor.
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Returns:
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Tensor:
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Output tensor.
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Tensor:
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Router logits.
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"""
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bsz, length, emb_size = layer_input.size()
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layer_input = layer_input.reshape(-1, emb_size)
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_, batch_index, batch_gates, expert_size, router_logits = self.router(layer_input)
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expert_inputs = layer_input[batch_index]
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hidden_states = self.input_linear(expert_inputs, expert_size)
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chunked_hidden_states = hidden_states.chunk(2, dim=-1)
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hidden_states = self.activation(chunked_hidden_states[0]) * chunked_hidden_states[1]
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expert_outputs = self.output_linear(hidden_states, expert_size)
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expert_outputs = expert_outputs * batch_gates[:, None]
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zeros = torch.zeros((bsz * length, self.input_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
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layer_output = zeros.index_add(0, batch_index, expert_outputs)
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layer_output = layer_output.view(bsz, length, self.input_size)
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layer_output = layer_output + self.bias
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return layer_output, router_logits
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class JetMoeMoA(nn.Module):
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"""
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A Sparsely gated mixture of attention layer with pairs of query- and output-projections as experts.
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Args:
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config:
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Configuration object with model hyperparameters.
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"""
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def __init__(self, config: JetMoeConfig):
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super().__init__()
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self.num_experts = config.num_local_experts
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self.input_size = config.hidden_size
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self.hidden_size = config.kv_channels * config.num_key_value_heads
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self.top_k = config.num_experts_per_tok
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self.bias = torch.nn.Parameter(torch.empty(self.input_size))
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self.input_linear = JetMoeParallelExperts(self.num_experts, self.input_size, self.hidden_size)
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self.output_linear = JetMoeParallelExperts(self.num_experts, self.hidden_size, self.input_size)
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self.router = JetMoeTopKGating(
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input_size=self.input_size,
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num_experts=self.num_experts,
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top_k=self.top_k,
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)
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def map(self, layer_input):
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"""
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Map inputs to attention experts according to routing decision and compute query projection inside each experts.
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"""
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# Compute gating topology
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bsz, length, emb_size = layer_input.size()
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layer_input = layer_input.reshape(-1, emb_size) # [bsz * length, emb_size]
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index_sorted_experts, batch_index, batch_gates, expert_size, router_logits = self.router(layer_input)
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topo_info = (index_sorted_experts, batch_index, batch_gates, expert_size)
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# Group inputs according to topology and compute query projection
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expert_inputs = layer_input[batch_index] # [bsz * length * top_k, emb_size]
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expert_outputs = self.input_linear(expert_inputs, expert_size) # [bsz * length * top_k, hidden_size]
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# Ungroup queries back to original order
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zeros = torch.zeros(
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(bsz * length * self.top_k, self.hidden_size), dtype=expert_outputs.dtype, device=expert_outputs.device
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)
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layer_output = zeros.index_add(0, index_sorted_experts, expert_outputs)
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layer_output = layer_output.view(bsz, length, self.top_k, -1) # [bsz, length, top_k, hidden_size]
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return layer_output, router_logits, topo_info
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def reduce(self, layer_input, topo_info):
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"""
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Compute output projection inside each attention experts and merge the outputs of different experts.
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"""
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bsz, length, k, hidden_size = layer_input.size()
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layer_input = layer_input.reshape(-1, hidden_size) # [bsz * length * k, hidden_size]
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index_sorted_experts, batch_index, batch_gates, expert_size = topo_info
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# Group inputs according to topology and compute output projection
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expert_inputs = layer_input[index_sorted_experts] # [bsz * length * top_k, hidden_size]
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expert_outputs = self.output_linear(expert_inputs, expert_size) # [bsz * length * top_k, emb_size]
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# Apply gates to attention expert outputs
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expert_outputs = expert_outputs * batch_gates[:, None]
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# Ungroup and merge outputs to original order
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zeros = torch.zeros((bsz * length, self.input_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
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layer_output = zeros.index_add(0, batch_index, expert_outputs)
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layer_output = layer_output.view(bsz, length, self.input_size)
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layer_output = layer_output + self.bias
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return layer_output
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def forward(self, layer_input):
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raise NotImplementedError("This module doesn't support call and forward.")
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# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->JetMoe
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class JetMoeRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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JetMoeRMSNorm 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.gemma.modeling_gemma.GemmaRotaryEmbedding with Gemma->JetMoe
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class JetMoeRotaryEmbedding(nn.Module):
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def __init__(self, config: JetMoeConfig, 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.
|
|
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
|
position_ids (`torch.Tensor`, *optional*):
|
|
Deprecated and unused.
|
|
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
|
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
|
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
|
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
|
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
|
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
|
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
|
Returns:
|
|
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
|
"""
|
|
cos = cos.unsqueeze(unsqueeze_dim)
|
|
sin = sin.unsqueeze(unsqueeze_dim)
|
|
q_embed = (q * cos) + (rotate_half(q) * sin)
|
|
k_embed = (k * cos) + (rotate_half(k) * sin)
|
|
return q_embed, k_embed
|
|
|
|
|
|
class JetMoeAttention(nn.Module):
|
|
"""
|
|
Multi-headed attention from 'Attention Is All You Need' paper.
|
|
"""
|
|
|
|
def __init__(self, config: JetMoeConfig, layer_idx: Optional[int] = None):
|
|
"""
|
|
Initialize the JetMoeAttention module.
|
|
|
|
Args:
|
|
config:
|
|
Configuration object with model hyperparameters.
|
|
layer_idx:
|
|
Index of the layer in the model.
|
|
"""
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer_idx = layer_idx
|
|
self.is_causal = True
|
|
if layer_idx is None:
|
|
logger.warning_once(
|
|
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
|
|
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
|
|
"when creating this class."
|
|
)
|
|
|
|
self.top_k = config.num_experts_per_tok
|
|
self.attention_dropout = config.attention_dropout
|
|
self.kv_projection_size = config.kv_channels * config.num_key_value_heads
|
|
self.num_key_value_heads = config.num_key_value_heads
|
|
self.num_heads = config.num_attention_heads
|
|
self.head_dim = config.kv_channels
|
|
|
|
self.experts = JetMoeMoA(config)
|
|
|
|
self.kv_proj = torch.nn.Linear(config.hidden_size, self.kv_projection_size * 2, bias=False)
|
|
|
|
self.rotary_emb = JetMoeRotaryEmbedding(config)
|
|
|
|
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,
|
|
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
|
|
bsz, q_len, _ = hidden_states.size()
|
|
|
|
query_states, router_logits, topo_info = self.experts.map(hidden_states)
|
|
key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
|
|
|
|
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 = self.rotary_emb(value_states, position_ids)
|
|
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)
|
|
|
|
# repeat k/v heads for top-k attention experts
|
|
key_states = key_states.repeat(1, self.top_k, 1, 1)
|
|
value_states = value_states.repeat(1, self.top_k, 1, 1)
|
|
|
|
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
|
|
|
if attention_mask is not None: # no matter the length, we just slice it
|
|
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
|
attn_weights = attn_weights + causal_mask
|
|
|
|
# upcast attention to fp32
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
|
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
|
|
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
|
raise ValueError(
|
|
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
|
f" {attn_output.size()}"
|
|
)
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
|
|
|
|
attn_output = self.experts.reduce(attn_output, topo_info)
|
|
attn_output = attn_output.view(bsz, q_len, -1)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output, attn_weights, router_logits
|
|
|
|
|
|
class JetMoeSdpaAttention(JetMoeAttention):
|
|
"""
|
|
JetMoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
|
`JetMoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
|
SDPA API.
|
|
"""
|
|
|
|
# Adapted from JetMoeAttention.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,
|
|
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]], Optional[torch.Tensor]]:
|
|
if output_attentions:
|
|
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
|
logger.warning_once(
|
|
"JetMoeModel is using JetMoeSdpaAttention, 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,
|
|
)
|
|
|
|
bsz, q_len, _ = hidden_states.size()
|
|
|
|
query_states, router_logits, topo_info = self.experts.map(hidden_states)
|
|
key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
|
|
|
|
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 = self.rotary_emb(value_states, position_ids)
|
|
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)
|
|
|
|
# repeat k/v heads for top-k attention experts
|
|
key_states = key_states.repeat(1, self.top_k, 1, 1)
|
|
value_states = value_states.repeat(1, self.top_k, 1, 1)
|
|
|
|
causal_mask = attention_mask
|
|
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.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
|
|
|
|
attn_output = self.experts.reduce(attn_output, topo_info)
|
|
attn_output = attn_output.view(bsz, q_len, -1)
|
|
|
|
return attn_output, None, router_logits
|
|
|
|
|
|
class JetMoeFlashAttention2(JetMoeAttention):
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
|
# 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.
|
|
# 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).
|
|
self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Optional[torch.FloatTensor],
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
use_cache: Optional[bool] = False,
|
|
output_attentions: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[
|
|
tuple[torch.Tensor, tuple[torch.Tensor]],
|
|
Optional[tuple[torch.Tensor, tuple[torch.Tensor], tuple[torch.Tensor, ...]]],
|
|
]:
|
|
"""
|
|
Forward pass of the JetMoeAttention module.
|
|
|
|
Args:
|
|
hidden_states (Optional[torch.FloatTensor]): Input hidden states.
|
|
attention_mask (Optional[torch.FloatTensor]): Attention mask.
|
|
layer_past (Optional[tuple[torch.Tensor]]): Past layer state.
|
|
use_cache (Optional[bool]): Whether to use cached states.
|
|
output_attentions (Optional[bool]): Whether to output attention weights.
|
|
cache_position (Optional[torch.LongTensor]): Position of the cache.
|
|
|
|
Returns:
|
|
Union[tuple[torch.Tensor, tuple[torch.Tensor]], Optional[tuple[...]]]: Tuple containing outputs.
|
|
"""
|
|
output_attentions = False
|
|
bsz, q_len, hidden_size = hidden_states.size()
|
|
|
|
# calculate query, key, values
|
|
query_states, router_logits, topo_info = self.experts.map(hidden_states)
|
|
key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
|
|
|
|
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 = self.rotary_emb(value_states, position_ids)
|
|
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)
|
|
|
|
# repeat k/v heads for top-k attention experts
|
|
key_states = key_states.repeat(1, self.top_k, 1, 1)
|
|
value_states = value_states.repeat(1, self.top_k, 1, 1)
|
|
|
|
# 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. (LlamaRMSNorm 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.kv_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,
|
|
).to(input_dtype)
|
|
|
|
# output projection
|
|
attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
|
|
attn_output = self.experts.reduce(attn_output, topo_info)
|
|
attn_output = attn_output.view(bsz, q_len, hidden_size) # re-assemble all head outputs side by side
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output, attn_weights, router_logits
|
|
|
|
|
|
JETMOE_ATTENTION_CLASSES = {
|
|
"eager": JetMoeAttention,
|
|
"flash_attention_2": JetMoeFlashAttention2,
|
|
"sdpa": JetMoeSdpaAttention,
|
|
}
|
|
|
|
|
|
class JetMoeBlock(GradientCheckpointingLayer):
|
|
def __init__(self, config: JetMoeConfig, layer_idx: Optional[int] = None):
|
|
"""
|
|
Initialize the JetMoeBlock module.
|
|
|
|
Args:
|
|
config:
|
|
Configuration object with model hyperparameters.
|
|
"""
|
|
super().__init__()
|
|
self.input_layernorm = JetMoeRMSNorm(config.hidden_size)
|
|
self.self_attention = JETMOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
|
|
self.post_attention_layernorm = JetMoeRMSNorm(config.hidden_size)
|
|
|
|
self.mlp = JetMoeMoE(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Optional[torch.FloatTensor],
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[tuple[torch.Tensor]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_router_logits: Optional[bool] = False,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[tuple[torch.Tensor], Optional[tuple[torch.Tensor, tuple[torch.FloatTensor, ...]]]]:
|
|
# Self Attention
|
|
attn_output, self_attn_weights, attn_router_logits = self.self_attention(
|
|
hidden_states=self.input_layernorm(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,
|
|
)
|
|
|
|
hidden_states = hidden_states + attn_output
|
|
x_mlp, mlp_router_logits = self.mlp(self.post_attention_layernorm(hidden_states))
|
|
hidden_states = hidden_states + x_mlp
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights,)
|
|
|
|
if output_router_logits:
|
|
outputs += attn_router_logits, mlp_router_logits
|
|
|
|
return outputs
|
|
|
|
|
|
@auto_docstring
|
|
class JetMoePreTrainedModel(PreTrainedModel):
|
|
config: JetMoeConfig
|
|
base_model_prefix = "transformer"
|
|
supports_gradient_checkpointing = False
|
|
_no_split_modules = ["JetMoeBlock"]
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights."""
|
|
if isinstance(module, (nn.Linear,)):
|
|
# Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, JetMoeRMSNorm):
|
|
module.weight.data.fill_(1.0)
|
|
elif isinstance(module, JetMoeParallelExperts):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
elif isinstance(module, JetMoeMoA):
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, JetMoeMoE):
|
|
module.bias.data.zero_()
|
|
|
|
|
|
@auto_docstring
|
|
class JetMoeModel(JetMoePreTrainedModel):
|
|
"""
|
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`JetMoeBlock`]
|
|
|
|
Args:
|
|
config:
|
|
JetMoeConfig
|
|
"""
|
|
|
|
def __init__(self, config: JetMoeConfig):
|
|
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([JetMoeBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
|
|
self._attn_implementation = config._attn_implementation
|
|
self.norm = JetMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
self.gradient_checkpointing = False
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_router_logits: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> MoeModelOutputWithPast:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
output_router_logits = (
|
|
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if self.gradient_checkpointing and self.training 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)
|
|
|
|
if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
|
|
batch_size = inputs_embeds.shape[0]
|
|
is_padding_right = attention_mask[:, -1].sum().item() != batch_size
|
|
if is_padding_right:
|
|
raise ValueError(
|
|
"You are attempting to perform batched generation with padding_side='right'"
|
|
" this may lead to unexpected behaviour for Flash Attention version of JetMoe. Make sure to "
|
|
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
|
|
)
|
|
causal_mask = self._update_causal_mask(
|
|
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
# 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:
|
|
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,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
if output_router_logits:
|
|
all_router_logits += (layer_outputs[-2], layer_outputs[-1])
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
return MoeModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
router_logits=all_router_logits,
|
|
)
|
|
|
|
# Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask
|
|
def _update_causal_mask(
|
|
self,
|
|
attention_mask: Union[torch.Tensor, "BlockMask"],
|
|
input_tensor: torch.Tensor,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Cache,
|
|
output_attentions: bool = False,
|
|
):
|
|
if self.config._attn_implementation == "flash_attention_2":
|
|
if attention_mask is not None and (attention_mask == 0.0).any():
|
|
return attention_mask
|
|
return None
|
|
if self.config._attn_implementation == "flex_attention":
|
|
if isinstance(attention_mask, torch.Tensor):
|
|
attention_mask = make_flex_block_causal_mask(attention_mask)
|
|
return attention_mask
|
|
|
|
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
|
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
|
# to infer the attention mask.
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
|
|
|
|
# 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_compilable_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 = input_tensor.dtype
|
|
sequence_length = input_tensor.shape[1]
|
|
if using_compilable_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,
|
|
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
|
|
# Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
|
|
def _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask: torch.Tensor,
|
|
sequence_length: int,
|
|
target_length: int,
|
|
dtype: torch.dtype,
|
|
cache_position: torch.Tensor,
|
|
batch_size: int,
|
|
**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.
|
|
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=cache_position.device
|
|
)
|
|
if sequence_length != 1:
|
|
causal_mask = torch.triu(causal_mask, diagonal=1)
|
|
causal_mask *= torch.arange(target_length, device=cache_position.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, :].to(
|
|
causal_mask.device
|
|
)
|
|
padding_mask = padding_mask == 0
|
|
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
|
padding_mask, min_dtype
|
|
)
|
|
|
|
return causal_mask
|
|
|
|
|
|
class JetMoeForCausalLM(JetMoePreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = JetMoeModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.aux_loss_coef = config.aux_loss_coef
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
self.tie_word_embeddings = config.tie_word_embeddings
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**kwargs,
|
|
) -> 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]`.
|
|
"""
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs: MoeModelOutputWithPast = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
hidden_states = outputs.last_hidden_state
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(
|
|
logits,
|
|
labels,
|
|
vocab_size=self.config.vocab_size,
|
|
**kwargs,
|
|
)
|
|
|
|
aux_loss = None
|
|
if output_router_logits:
|
|
aux_loss = load_balancing_loss_func(
|
|
outputs.router_logits,
|
|
self.num_experts,
|
|
self.num_experts_per_tok,
|
|
attention_mask,
|
|
)
|
|
if labels is not None:
|
|
loss += self.aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
|
|
|
|
return MoeCausalLMOutputWithPast(
|
|
loss=loss,
|
|
aux_loss=aux_loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
router_logits=outputs.router_logits,
|
|
)
|
|
|
|
|
|
class JetMoeForSequenceClassification(GenericForSequenceClassification, JetMoePreTrainedModel): ...
|
|
|
|
|
|
__all__ = ["JetMoeForCausalLM", "JetMoeModel", "JetMoePreTrainedModel", "JetMoeForSequenceClassification"]
|