1362 lines
59 KiB
Python
1362 lines
59 KiB
Python
# coding=utf-8
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# Copyright 2024 Microsoft 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 Phimoe 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 ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
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from ...generation import GenerationMixin
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from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_causal_attention_mask
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from ...modeling_flash_attention_utils import 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
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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_phimoe import PhimoeConfig
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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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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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# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
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# It means that the function will not be traced through and simply appear as a node in the graph.
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_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
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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 PhimoeRotaryEmbedding(nn.Module):
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def __init__(
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self,
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config: Optional[PhimoeConfig] = None,
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):
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super().__init__()
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self.config = config
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if config.rope_scaling is not None:
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self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
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self.short_mscale = config.rope_scaling.get("short_mscale")
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self.long_mscale = config.rope_scaling.get("long_mscale")
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else:
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self.rope_type = "default"
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self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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def forward(self, x, seq_len=None):
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mscale = None
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if self.config.rope_scaling and seq_len:
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mscale = (
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self.long_mscale
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if seq_len > self.config.rope_scaling["original_max_position_embeddings"]
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else self.short_mscale
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)
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inv_freq, attention_scaling = self.rope_init_fn(self.config, x.device, seq_len)
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mscale = attention_scaling if mscale is None else mscale
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t = torch.arange(seq_len, device=x.device, dtype=torch.float32)
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freqs = torch.outer(t, inv_freq)
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emb = torch.cat((freqs, freqs), dim=-1)
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return (emb.cos() * mscale).to(x.dtype), (emb.sin() * mscale).to(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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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, 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`):
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The position indices of the tokens corresponding to the query and key tensors. For example, this can be
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used to pass offsetted position ids when working with a KV-cache.
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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[position_ids].unsqueeze(unsqueeze_dim)
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sin = sin[position_ids].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.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 PhimoeAttention(nn.Module):
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"""
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Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
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and "Generating Long Sequences with Sparse Transformers".
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"""
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def __init__(self, config: PhimoeConfig, 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.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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self.attention_dropout = config.attention_dropout
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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=self.config.attention_bias)
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self.k_proj = nn.Linear(
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self.hidden_size, self.num_key_value_heads * self.head_dim, bias=self.config.attention_bias
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)
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self.v_proj = nn.Linear(
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self.hidden_size, self.num_key_value_heads * self.head_dim, bias=self.config.attention_bias
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)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=self.config.attention_bias)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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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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) -> 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_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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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, position_ids)
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if past_key_value is not None:
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
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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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# repeat k/v heads if n_kv_heads < n_heads
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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 PhimoeFlashAttention2(PhimoeAttention):
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"""
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Phimoe flash attention module. This module inherits from `PhimoeAttention` 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 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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):
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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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, position_ids)
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if past_key_value is not None:
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
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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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# repeat k/v heads if n_kv_heads < n_heads
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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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dropout_rate = 0.0 if not self.training else self.attention_dropout
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# In PEFT, usually we cast the layer norms in float32 for training stability reasons
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# therefore the input hidden states gets silently casted in float32. Hence, we need
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# cast them back in float16 just to be sure everything works as expected.
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input_dtype = query_states.dtype
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device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
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if input_dtype == torch.float32:
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if torch.is_autocast_enabled():
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target_dtype = (
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torch.get_autocast_dtype(device_type)
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if hasattr(torch, "get_autocast_dtype")
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else torch.get_autocast_gpu_dtype()
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)
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# Handle the case where the model is quantized
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elif hasattr(self.config, "_pre_quantization_dtype"):
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target_dtype = self.config._pre_quantization_dtype
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else:
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target_dtype = self.q_proj.weight.dtype
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logger.warning_once(
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f"The input hidden states seems to be silently casted in float32, this might be related to"
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f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
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f" {target_dtype}."
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)
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query_states = query_states.to(target_dtype)
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key_states = key_states.to(target_dtype)
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value_states = value_states.to(target_dtype)
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# Reashape to the expected shape for Flash Attention
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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attn_output = _flash_attention_forward(
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query_states,
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key_states,
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value_states,
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attention_mask,
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q_len,
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position_ids=position_ids,
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dropout=dropout_rate,
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sliding_window=getattr(self.config, "sliding_window", None),
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is_causal=self.is_causal,
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)
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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 PhimoeSdpaAttention(PhimoeAttention):
|
|
"""
|
|
Phimoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
|
`PhimoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
|
SDPA API.
|
|
"""
|
|
|
|
# Adapted from PhimoeAttention.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(
|
|
"PhimoeModel is using PhimoeSdpaAttention, 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,
|
|
position_embeddings=position_embeddings,
|
|
)
|
|
|
|
bsz, q_len, _ = hidden_states.size()
|
|
|
|
query_states = self.q_proj(hidden_states)
|
|
key_states = self.k_proj(hidden_states)
|
|
value_states = self.v_proj(hidden_states)
|
|
|
|
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, position_ids)
|
|
|
|
if past_key_value is not None:
|
|
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
|
|
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: # no matter the length, we just slice it
|
|
causal_mask = attention_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 attention_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.
|
|
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
|
|
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
|
|
|
|
|
|
PHIMOE_ATTENTION_CLASSES = {
|
|
"eager": PhimoeAttention,
|
|
"flash_attention_2": PhimoeFlashAttention2,
|
|
"sdpa": PhimoeSdpaAttention,
|
|
}
|
|
|
|
|
|
# Copied from transformers.models.mixtral.modeling_mixtral.MixtralBlockSparseTop2MLP with Mixtral->Phimoe
|
|
class PhimoeBlockSparseTop2MLP(nn.Module):
|
|
def __init__(self, config: PhimoeConfig):
|
|
super().__init__()
|
|
self.ffn_dim = config.intermediate_size
|
|
self.hidden_dim = config.hidden_size
|
|
|
|
self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
|
|
self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
|
|
self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
|
|
|
|
self.act_fn = ACT2FN[config.hidden_act]
|
|
|
|
def forward(self, hidden_states):
|
|
current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
|
|
current_hidden_states = self.w2(current_hidden_states)
|
|
return current_hidden_states
|
|
|
|
|
|
class MultiplierProcessor(torch.autograd.Function):
|
|
@staticmethod
|
|
def forward(
|
|
ctx,
|
|
scores: torch.Tensor,
|
|
multiplier: torch.Tensor,
|
|
selected_experts: torch.Tensor,
|
|
masked_gates: torch.Tensor,
|
|
mask_for_one: torch.Tensor,
|
|
):
|
|
"""
|
|
Forward pass for the custom autograd function.
|
|
|
|
Args:
|
|
ctx: Context object to save information for backward computation.
|
|
scores (torch.Tensor): Input scores tensor.
|
|
multiplier (torch.Tensor): Multiplier tensor.
|
|
selected_experts (torch.Tensor): Tensor of selected experts.
|
|
masked_gates (torch.Tensor): Masked gates tensor.
|
|
mask_for_one (torch.Tensor): Mask for one tensor.
|
|
|
|
Returns:
|
|
torch.Tensor: Result of the forward pass.
|
|
"""
|
|
ctx.save_for_backward(multiplier, selected_experts, masked_gates)
|
|
return multiplier * mask_for_one
|
|
|
|
@staticmethod
|
|
def backward(
|
|
ctx,
|
|
grad_at_output: torch.Tensor,
|
|
):
|
|
"""
|
|
Backward pass for the custom autograd function.
|
|
|
|
Args:
|
|
ctx: Context object with saved tensors from the forward pass.
|
|
grad_at_output (torch.Tensor): Gradient at the output.
|
|
|
|
Returns:
|
|
tuple[torch.Tensor, None, None, None, None]: Gradients for the inputs.
|
|
"""
|
|
multiplier, selected_experts, masked_gates = ctx.saved_tensors
|
|
|
|
grad_at_output = grad_at_output * multiplier
|
|
|
|
grad_at_scores_expanded = masked_gates * grad_at_output.mul(-1)
|
|
grad_at_scores_expanded.scatter_add_(
|
|
dim=-1,
|
|
index=selected_experts,
|
|
src=grad_at_output,
|
|
)
|
|
|
|
return (
|
|
grad_at_scores_expanded,
|
|
None,
|
|
None,
|
|
None,
|
|
None,
|
|
)
|
|
|
|
|
|
def sparsemixer(scores, jitter_eps, training, top_k=2):
|
|
"""
|
|
Sparse mixer function to select top-k experts and compute multipliers.
|
|
Based on the paper: https://huggingface.co/papers/2409.12136
|
|
We first replace the TopK(·) function as random sampling of discrete variables
|
|
in model training. Then, following Liu et al. (2023a) and Liu et al. (2023b), we apply Heun's
|
|
third order method to approximate the expert routing gradient and construct a modified
|
|
back-propagation to give a mathematically sound gradient estimation for expert routing.
|
|
|
|
Args:
|
|
scores (torch.Tensor): Input scores tensor.
|
|
jitter_eps (float): Jitter epsilon for numerical stability.
|
|
training (bool): Flag indicating if the model is in training mode.
|
|
top_k (int): Number of top experts to select.
|
|
|
|
Returns:
|
|
tuple[torch.Tensor, torch.Tensor]: Multiplier and selected experts tensors.
|
|
"""
|
|
if top_k != 2:
|
|
raise ValueError("top_k must be equal to 2")
|
|
|
|
# first expert
|
|
|
|
with torch.no_grad():
|
|
# Compute mask for sparsity
|
|
mask_logits_threshold, max_ind = scores.max(dim=-1, keepdim=True)
|
|
factor = scores.abs().clamp(min=mask_logits_threshold)
|
|
mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (2 * jitter_eps)
|
|
|
|
# Apply mask
|
|
masked_gates = scores.masked_fill(mask_logits_threshold, float("-inf"))
|
|
if training:
|
|
selected_experts = (
|
|
(
|
|
masked_gates
|
|
- torch.empty_like(masked_gates, memory_format=torch.legacy_contiguous_format).exponential_().log()
|
|
)
|
|
.max(dim=-1)[1]
|
|
.unsqueeze(-1)
|
|
) # Gumbel sampling, more robust than the multinomial method
|
|
else:
|
|
selected_experts = max_ind
|
|
|
|
# Compute scores for gradients
|
|
masked_gates = torch.softmax(masked_gates, dim=-1)
|
|
multiplier_o = masked_gates.gather(dim=-1, index=selected_experts)
|
|
|
|
if training:
|
|
# Compute midpoint mask
|
|
max_scores, max_ind = masked_gates.max(dim=-1, keepdim=True)
|
|
mask_for_one = torch.logical_or(
|
|
selected_experts == max_ind,
|
|
torch.rand_like(max_scores) > 0.75, # Heun's third-order method
|
|
)
|
|
# 1 -> 1.0 & 0 -> 1./3: lambda x: (x + 0.5) / 1.5
|
|
mask_for_one = torch.add(0.3333, mask_for_one, alpha=0.6667).type_as(masked_gates)
|
|
|
|
multiplier = MultiplierProcessor.apply(
|
|
scores,
|
|
multiplier_o,
|
|
selected_experts,
|
|
masked_gates,
|
|
mask_for_one,
|
|
)
|
|
else:
|
|
multiplier = multiplier_o
|
|
|
|
# Masked out first expert
|
|
masked_scores = torch.scatter(
|
|
scores,
|
|
-1,
|
|
selected_experts,
|
|
float("-inf"),
|
|
)
|
|
with torch.no_grad():
|
|
# Compute mask for sparsity
|
|
mask_logits_threshold, max_ind = masked_scores.max(dim=-1, keepdim=True)
|
|
factor = scores.abs().clamp(min=mask_logits_threshold)
|
|
mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (2 * jitter_eps)
|
|
|
|
# Apply mask
|
|
masked_gates_top2 = masked_scores.masked_fill(mask_logits_threshold, float("-inf"))
|
|
if training:
|
|
selected_experts_top2 = (
|
|
(
|
|
masked_gates_top2
|
|
- torch.empty_like(masked_gates_top2, memory_format=torch.legacy_contiguous_format)
|
|
.exponential_()
|
|
.log()
|
|
)
|
|
.max(dim=-1)[1]
|
|
.unsqueeze(-1)
|
|
) # Gumbel sampling, more robust than the multinomial method
|
|
else:
|
|
selected_experts_top2 = max_ind
|
|
# Compute scores for gradients
|
|
masked_gates_top2 = torch.softmax(masked_gates_top2, dim=-1)
|
|
multiplier_top2_o = masked_gates_top2.gather(dim=-1, index=selected_experts_top2)
|
|
|
|
if training:
|
|
# Compute midpoint mask
|
|
max_scores, max_ind = masked_gates_top2.max(dim=-1, keepdim=True)
|
|
mask_for_one_top2 = torch.logical_or(
|
|
selected_experts_top2 == max_ind,
|
|
torch.rand_like(max_scores).uniform_() > 0.75, # Heun's third-order method
|
|
)
|
|
# 1 -> 1.0 & 0 -> 1./3: lambda x: (x + 0.5) / 1.5
|
|
mask_for_one_top2 = torch.add(0.3333, mask_for_one_top2, alpha=0.6667).type_as(masked_gates_top2)
|
|
|
|
multiplier_top2 = MultiplierProcessor.apply(
|
|
scores,
|
|
multiplier_top2_o,
|
|
selected_experts_top2,
|
|
masked_gates_top2,
|
|
mask_for_one_top2,
|
|
)
|
|
else:
|
|
multiplier_top2 = multiplier_top2_o
|
|
|
|
multiplier = torch.concat((multiplier, multiplier_top2), dim=-1)
|
|
selected_experts = torch.concat((selected_experts, selected_experts_top2), dim=-1)
|
|
|
|
return (
|
|
multiplier,
|
|
selected_experts,
|
|
)
|
|
|
|
|
|
class PhimoeSparseMoeBlock(nn.Module):
|
|
"""
|
|
This implementation is
|
|
strictly equivalent to standard MoE with full capacity (no
|
|
dropped tokens). It's faster since it formulates MoE operations
|
|
in terms of block-sparse operations to accommodate imbalanced
|
|
assignments of tokens to experts, whereas standard MoE either
|
|
(1) drop tokens at the cost of reduced performance or (2) set
|
|
capacity factor to number of experts and thus waste computation
|
|
and memory on padding.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.hidden_dim = config.hidden_size
|
|
self.ffn_dim = config.intermediate_size
|
|
self.num_experts = config.num_local_experts
|
|
self.top_k = config.num_experts_per_tok
|
|
# gating
|
|
self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
|
|
|
|
self.experts = nn.ModuleList([PhimoeBlockSparseTop2MLP(config) for _ in range(self.num_experts)])
|
|
|
|
# Jitter parameters
|
|
self.router_jitter_noise = config.router_jitter_noise
|
|
self.input_jitter_noise = config.input_jitter_noise
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
""" """
|
|
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
|
if self.training and self.input_jitter_noise > 0:
|
|
hidden_states *= torch.empty_like(hidden_states).uniform_(
|
|
1.0 - self.input_jitter_noise, 1.0 + self.input_jitter_noise
|
|
)
|
|
hidden_states = hidden_states.view(-1, hidden_dim)
|
|
router_logits = self.gate(hidden_states)
|
|
|
|
routing_weights, selected_experts = sparsemixer(
|
|
router_logits,
|
|
jitter_eps=self.router_jitter_noise,
|
|
training=self.training,
|
|
)
|
|
|
|
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 sollicitated
|
|
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])
|
|
|
|
if top_x.shape[0] == 0:
|
|
continue
|
|
|
|
# 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 PhimoeDecoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: PhimoeConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
|
|
self.self_attn = PHIMOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
|
|
|
|
self.block_sparse_moe = PhimoeSparseMoeBlock(config)
|
|
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True)
|
|
self.post_attention_layernorm = nn.LayerNorm(
|
|
config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[tuple[torch.Tensor]] = 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, sequence_length)` where padding elements are indicated by 0.
|
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
|
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`).
|
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
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,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states, router_logits = self.block_sparse_moe(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 PhimoePreTrainedModel(PreTrainedModel):
|
|
config: PhimoeConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["PhimoeDecoderLayer"]
|
|
_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, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
@auto_docstring
|
|
class PhimoeModel(PhimoePreTrainedModel):
|
|
"""
|
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhimoeDecoderLayer`]
|
|
Args:
|
|
config: PhimoeConfig
|
|
"""
|
|
|
|
def __init__(self, config: PhimoeConfig):
|
|
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(
|
|
[PhimoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self._attn_implementation = config._attn_implementation
|
|
self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True)
|
|
self.rotary_emb = PhimoeRotaryEmbedding(config=config)
|
|
|
|
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[Cache] = 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_router_logits = (
|
|
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
|
)
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError(
|
|
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
|
|
)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
# 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 inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if cache_position is None:
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
cache_position = torch.arange(
|
|
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
|
)
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0)
|
|
|
|
causal_mask = self._update_causal_mask(
|
|
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
position_embeddings = self.rotary_emb(hidden_states, seq_len=cache_position[-1] + 1)
|
|
|
|
# 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,
|
|
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:
|
|
all_router_logits += (layer_outputs[-1],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
return MoeModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
router_logits=all_router_logits,
|
|
)
|
|
|
|
def _update_causal_mask(
|
|
self,
|
|
attention_mask: Union[torch.Tensor, "BlockMask"],
|
|
input_tensor: torch.Tensor,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Cache,
|
|
output_attentions: bool = False,
|
|
):
|
|
if self.config._attn_implementation == "flash_attention_2":
|
|
if attention_mask is not None and past_key_values is not None:
|
|
is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
|
|
if is_padding_right:
|
|
raise ValueError(
|
|
"You are attempting to perform batched generation with padding_side='right'"
|
|
" this may lead to unexpected behaviour for Flash Attention version of Phimoe. Make sure to "
|
|
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
|
|
)
|
|
if attention_mask is not None and 0.0 in attention_mask:
|
|
return attention_mask
|
|
return None
|
|
if self.config._attn_implementation == "flex_attention":
|
|
if isinstance(attention_mask, torch.Tensor):
|
|
attention_mask = make_flex_block_causal_mask(attention_mask)
|
|
return attention_mask
|
|
|
|
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
|
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
|
# to infer the attention mask.
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
using_static_cache = isinstance(past_key_values, StaticCache)
|
|
using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
|
|
|
|
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
|
if (
|
|
self.config._attn_implementation == "sdpa"
|
|
and not (using_static_cache or using_sliding_window_cache)
|
|
and not output_attentions
|
|
):
|
|
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
|
attention_mask,
|
|
inputs_embeds=input_tensor,
|
|
past_key_values_length=past_seen_tokens,
|
|
sliding_window=self.config.sliding_window,
|
|
is_training=self.training,
|
|
):
|
|
return None
|
|
|
|
dtype = input_tensor.dtype
|
|
min_dtype = torch.finfo(dtype).min
|
|
sequence_length = input_tensor.shape[1]
|
|
# SlidingWindowCache or StaticCache
|
|
if using_sliding_window_cache or using_static_cache:
|
|
target_length = past_key_values.get_max_cache_shape()
|
|
# DynamicCache or no cache
|
|
else:
|
|
target_length = (
|
|
attention_mask.shape[-1]
|
|
if isinstance(attention_mask, torch.Tensor)
|
|
else past_seen_tokens + sequence_length + 1
|
|
)
|
|
|
|
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
|
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask,
|
|
sequence_length=sequence_length,
|
|
target_length=target_length,
|
|
dtype=dtype,
|
|
cache_position=cache_position,
|
|
batch_size=input_tensor.shape[0],
|
|
config=self.config,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
if (
|
|
self.config._attn_implementation == "sdpa"
|
|
and attention_mask is not None
|
|
and attention_mask.device.type in ["cuda", "xpu", "npu"]
|
|
and not output_attentions
|
|
):
|
|
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
|
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
|
# Details: https://github.com/pytorch/pytorch/issues/110213
|
|
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
|
|
|
return causal_mask
|
|
|
|
@staticmethod
|
|
def _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask: torch.Tensor,
|
|
sequence_length: int,
|
|
target_length: int,
|
|
dtype: torch.dtype,
|
|
cache_position: torch.Tensor,
|
|
batch_size: int,
|
|
config: PhimoeConfig,
|
|
past_key_values: Cache,
|
|
):
|
|
"""
|
|
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
|
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
|
|
|
|
Args:
|
|
attention_mask (`torch.Tensor`):
|
|
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
|
|
sequence_length (`int`):
|
|
The sequence length being processed.
|
|
target_length (`int`):
|
|
The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
|
|
dtype (`torch.dtype`):
|
|
The dtype to use for the 4D attention mask.
|
|
cache_position (`torch.Tensor`):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
batch_size (`torch.Tensor`):
|
|
Batch size.
|
|
config (`PhimoeConfig`):
|
|
The model's configuration class
|
|
past_key_values (`Cache`):
|
|
The cache class that is being used currently to generate
|
|
"""
|
|
if attention_mask is not None and attention_mask.dim() == 4:
|
|
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
|
|
causal_mask = attention_mask
|
|
else:
|
|
min_dtype = torch.finfo(dtype).min
|
|
causal_mask = torch.full(
|
|
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
|
|
)
|
|
diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
|
|
-1, 1
|
|
)
|
|
text_config = config.get_text_config()
|
|
if getattr(text_config, "use_sliding_window", True) and text_config.sliding_window is not None:
|
|
# if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
|
|
# the check is needed to verify is current checkpoint was trained with sliding window or not
|
|
if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
|
|
sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= (
|
|
cache_position.reshape(-1, 1) - text_config.sliding_window
|
|
)
|
|
diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
|
|
causal_mask *= diagonal_attend_mask
|
|
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
|
if attention_mask is not None:
|
|
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
|
if attention_mask.shape[-1] > target_length:
|
|
attention_mask = attention_mask[:, :target_length]
|
|
mask_length = attention_mask.shape[-1]
|
|
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
|
|
causal_mask.device
|
|
)
|
|
padding_mask = padding_mask == 0
|
|
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
|
padding_mask, min_dtype
|
|
)
|
|
return causal_mask
|
|
|
|
|
|
class PhimoeForCausalLM(PhimoePreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = PhimoeModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=self.config.lm_head_bias)
|
|
self.router_aux_loss_coef = config.router_aux_loss_coef
|
|
self.num_experts = config.num_local_experts
|
|
self.num_experts_per_tok = config.num_experts_per_tok
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
# 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]`.
|
|
|
|
Example:
|
|
```python
|
|
>>> from transformers import AutoTokenizer, PhimoeForCausalLM
|
|
>>> model = PhimoeForCausalLM.from_pretrained("microsoft/Phi-3.5-MoE-instruct")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-MoE-instruct")
|
|
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
|
```"""
|
|
if (
|
|
use_cache
|
|
and self.config.rope_scaling
|
|
and cache_position is not None
|
|
and cache_position[0] == self.config.original_max_position_embeddings
|
|
):
|
|
logger.warning(
|
|
f"If you are not using the generate method, you may encounter nonsensical outputs after the {self.config.original_max_position_embeddings}th token, as the KV cache needs to be recomputed."
|
|
)
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_router_logits = (
|
|
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
|
)
|
|
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs: MoeModelOutputWithPast = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
output_router_logits=output_router_logits,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
hidden_states = outputs.last_hidden_state
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
|
|
|
|
aux_loss = None
|
|
if output_router_logits:
|
|
aux_loss = load_balancing_loss_func(
|
|
outputs.router_logits,
|
|
self.num_experts,
|
|
self.num_experts_per_tok,
|
|
attention_mask,
|
|
)
|
|
if labels is not None:
|
|
loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
|
|
|
|
return MoeCausalLMOutputWithPast(
|
|
loss=loss,
|
|
aux_loss=aux_loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
router_logits=outputs.router_logits,
|
|
)
|
|
|
|
# Copied from transformers.models.phi3.modeling_phi3.Phi3ForCausalLM.prepare_inputs_for_generation
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
attention_mask=None,
|
|
inputs_embeds=None,
|
|
cache_position=None,
|
|
position_ids=None,
|
|
use_cache=True,
|
|
logits_to_keep=None,
|
|
**kwargs,
|
|
):
|
|
# Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the
|
|
# process
|
|
|
|
# When the first time input length reached long and short factor switching point, enforce re-compute cache
|
|
# It will cause downside of slower at this single token position, however, better than current failure.
|
|
if (
|
|
past_key_values
|
|
and self.config.rope_scaling
|
|
and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1
|
|
):
|
|
past_length = cache_position[0]
|
|
if past_length <= self.config.original_max_position_embeddings:
|
|
past_key_values = None
|
|
|
|
model_inputs = super().prepare_inputs_for_generation(
|
|
input_ids=input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
cache_position=cache_position,
|
|
position_ids=position_ids,
|
|
use_cache=use_cache,
|
|
logits_to_keep=logits_to_keep,
|
|
**kwargs,
|
|
)
|
|
return model_inputs
|
|
|
|
|
|
class PhimoeForSequenceClassification(GenericForSequenceClassification, PhimoePreTrainedModel): ...
|
|
|
|
|
|
__all__ = [
|
|
"PhimoePreTrainedModel",
|
|
"PhimoeModel",
|
|
"PhimoeForCausalLM",
|
|
"PhimoeForSequenceClassification",
|
|
]
|