268 lines
11 KiB
Python
268 lines
11 KiB
Python
# 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",
|
|
]
|