# coding=utf-8 # Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch Phi-3 model.""" from typing import Callable, Optional import torch import torch.utils.checkpoint from torch import nn from ...activations import ACT2FN from ...cache_utils import Cache from ...modeling_flash_attention_utils import FlashAttentionKwargs from ...modeling_utils import ALL_ATTENTION_FUNCTIONS from ...processing_utils import Unpack from ...utils import logging from ..mistral.modeling_mistral import ( MistralDecoderLayer, MistralForCausalLM, MistralForSequenceClassification, MistralForTokenClassification, MistralPreTrainedModel, eager_attention_forward, rotate_half, ) from .configuration_phi3 import Phi3Config logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "microsoft/Phi-3-mini-4k-instruct" _CONFIG_FOR_DOC = "Phi3Config" class Phi3MLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False) self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) self.activation_fn = ACT2FN[config.hidden_act] def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: up_states = self.gate_up_proj(hidden_states) gate, up_states = up_states.chunk(2, dim=-1) up_states = up_states * self.activation_fn(gate) return self.down_proj(up_states) def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. 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) rotary_dim = cos.shape[-1] q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:] k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:] q_embed = torch.cat([(q_rot * cos) + (rotate_half(q_rot) * sin), q_pass], dim=-1) k_embed = torch.cat([(k_rot * cos) + (rotate_half(k_rot) * sin), k_pass], dim=-1) return q_embed, k_embed class Phi3Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: Phi3Config, layer_idx: Optional[int] = None): super().__init__() self.config = config self.layer_idx = layer_idx self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.num_key_value_heads = config.num_key_value_heads self.scaling = self.head_dim**-0.5 self.attention_dropout = config.attention_dropout self.is_causal = True op_size = config.num_attention_heads * self.head_dim + 2 * (config.num_key_value_heads * self.head_dim) self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) self.qkv_proj = nn.Linear(config.hidden_size, op_size, bias=False) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) qkv = self.qkv_proj(hidden_states) query_pos = self.config.num_attention_heads * self.head_dim query_states = qkv[..., :query_pos] key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] query_states = query_states.view(hidden_shape).transpose(1, 2) key_states = key_states.view(hidden_shape).transpose(1, 2) value_states = value_states.view(hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, sliding_window=getattr(self.config, "sliding_window", None), **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class Phi3DecoderLayer(MistralDecoderLayer): def __init__(self, config: Phi3Config, layer_idx: int): super().__init__(config, layer_idx) self.config = config self.self_attn = Phi3Attention(config=config, layer_idx=layer_idx) self.mlp = Phi3MLP(config) self.resid_attn_dropout = nn.Dropout(config.resid_pdrop) self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop) 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, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) 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, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = residual + self.resid_attn_dropout(hidden_states) # main diff with Llama residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + self.resid_mlp_dropout(hidden_states) # main diff with Llama return hidden_states class Phi3PreTrainedModel(MistralPreTrainedModel): _version = "0.0.5" class Phi3ForCausalLM(MistralForCausalLM, Phi3PreTrainedModel): 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 = Phi3PreTrainedModel().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 Phi3ForSequenceClassification(MistralForSequenceClassification): pass class Phi3ForTokenClassification(MistralForTokenClassification): pass __all__ = [ "Phi3PreTrainedModel", "Phi3Model", # noqa: F822 "Phi3ForCausalLM", "Phi3ForSequenceClassification", "Phi3ForTokenClassification", ]