diff --git "a/modeling_phi3.py" "b/modeling_phi3.py"
--- "a/modeling_phi3.py"
+++ "b/modeling_phi3.py"
@@ -1,1626 +1,1563 @@
-# 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."""
-
-import inspect
-import math
-import warnings
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache
-from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
-from transformers.modeling_outputs import (
- BaseModelOutputWithPast,
- CausalLMOutputWithPast,
- SequenceClassifierOutputWithPast,
- TokenClassifierOutput,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
- add_code_sample_docstrings,
- add_start_docstrings,
- add_start_docstrings_to_model_forward,
- is_flash_attn_greater_or_equal_2_10,
- logging,
- replace_return_docstrings,
-)
-from .configuration_phi3 import Phi3Config
-
-
-logger = logging.get_logger(__name__)
-
-# Transformers scans dependencies in the modeling file, causing issues on conditional loading. The regex only ignores try/catch blocks, but not if statements
-# if is_flash_attn_2_available():
-_flash_supports_window_size = False
-try:
- from flash_attn import flash_attn_func, flash_attn_varlen_func
-
- _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
-
- if not _flash_supports_window_size:
- raise ValueError("Please update flash-attention to support window size.")
-
- from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
- from flash_attn.ops.activations import swiglu
- from flash_attn.ops.rms_norm import RMSNorm as Phi3FlashRMSNorm
-# else:
-except ImportError as error:
- logger.warning(
- f"Flash Attention or Flash Attention Submodules not found, consider installing for better performance: {error}."
- )
- if not _flash_supports_window_size:
- logger.warning(
- "This version of flash does not support window size. Please use `attn_implementation='eager'` or upgrade flash-attn library."
- )
- swiglu = None
- Phi3FlashRMSNorm = None
-
-_CHECKPOINT_FOR_DOC = "microsoft/Phi-3-mini-4k-instruct"
-_CONFIG_FOR_DOC = "Phi3Config"
-
-PHI3_PRETRAINED_MODEL_ARCHIVE_LIST = [
- "microsoft/Phi-3-mini-4k-instruct",
- "microsoft/Phi-3-mini-128k-instruct",
- # See all Phi-3 models at https://huggingface.co/models?filter=Phi-3
-]
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Phi3
-class Phi3RMSNorm(nn.Module):
- def __init__(self, hidden_size, eps=1e-6):
- """
- Phi3RMSNorm is equivalent to T5LayerNorm
- """
- super().__init__()
- self.weight = nn.Parameter(torch.ones(hidden_size))
- self.variance_epsilon = eps
-
- def forward(self, hidden_states):
- input_dtype = hidden_states.dtype
- hidden_states = hidden_states.to(torch.float32)
- variance = hidden_states.pow(2).mean(-1, keepdim=True)
- hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
- return self.weight * hidden_states.to(input_dtype)
-
-
-PHI3_NORM_CLASS = Phi3RMSNorm if Phi3FlashRMSNorm is None else Phi3FlashRMSNorm
-
-
-# Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def _get_unpad_data(attention_mask):
- seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
- indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
- max_seqlen_in_batch = seqlens_in_batch.max().item()
- cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
- return (
- indices,
- cu_seqlens,
- max_seqlen_in_batch,
- )
-
-
-# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Phi3
-class Phi3RotaryEmbedding(nn.Module):
- def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
- super().__init__()
-
- self.dim = dim
- self.max_position_embeddings = max_position_embeddings
- self.base = base
- inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
- self.register_buffer("inv_freq", inv_freq, persistent=False)
-
- # Build here to make `torch.jit.trace` work.
- self._set_cos_sin_cache(
- seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
- )
-
- def _set_cos_sin_cache(self, seq_len, device, dtype):
- self.max_seq_len_cached = seq_len
- t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
-
- freqs = torch.outer(t, self.inv_freq)
- # Different from paper, but it uses a different permutation in order to obtain the same calculation
- emb = torch.cat((freqs, freqs), dim=-1)
- self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
- self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
-
- def forward(self, x, seq_len=None):
- # x: [bs, num_attention_heads, seq_len, head_size]
- if seq_len > self.max_seq_len_cached:
- self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
-
- return (
- self.cos_cached[:seq_len].to(dtype=x.dtype),
- self.sin_cached[:seq_len].to(dtype=x.dtype),
- )
-
-
-class Phi3LongScaledRotaryEmbedding(nn.Module):
- def __init__(
- self,
- dim,
- short_factor,
- long_factor,
- max_position_embeddings=4096,
- original_max_position_embeddings=4096,
- base=10000,
- magnitude_scaling_policy="su",
- ):
- super().__init__()
-
- self.dim = dim
- self.max_position_embeddings = max_position_embeddings
- self.original_max_position_embeddings = original_max_position_embeddings
- self.base = base
-
- if magnitude_scaling_policy == "su":
- self._calc_mscale = self._calc_mscale_su
- elif magnitude_scaling_policy == "yarn":
- self._calc_mscale = self._calc_mscale_yarn
- else:
- self._calc_mscale = lambda scale: float(scale)
-
- self.short_factor = short_factor
- self.long_factor = long_factor
-
- def _calc_mscale_su(self, scale):
- if scale <= 1.0:
- return 1.0
- return math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings))
-
- def _calc_mscale_yarn(self, scale):
- if scale <= 1.0:
- return 1.0
- return 0.1 * math.log(scale) + 1.0
-
- @torch.no_grad()
- def forward(self, x, seq_len=None):
- if seq_len is None:
- seq_len = x.shape[-2]
- t = torch.arange(seq_len, device=x.device, dtype=torch.float32)
-
- if seq_len > self.original_max_position_embeddings:
- t = torch.arange(seq_len, device=x.device, dtype=torch.float32)
- rescale_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device)
- else:
- t = torch.arange(self.original_max_position_embeddings, device=x.device, dtype=torch.float32)
- rescale_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device)
- assert rescale_factors.shape == (
- self.dim // 2,
- ), f"misaligned shape for LongRoPE rescale factors: {rescale_factors.shape}"
-
- inv_freq = 1.0 / (
- rescale_factors * (self.base ** (torch.arange(0, self.dim, 2).float().to(x.device) / self.dim))
- )
-
- freqs = torch.outer(t, inv_freq)
- mscale = self._calc_mscale(self.max_position_embeddings / self.original_max_position_embeddings)
- emb = torch.cat((freqs, freqs), dim=-1)
-
- return (emb.cos() * mscale).to(x.dtype), (emb.sin() * mscale).to(x.dtype)
-
-
-# Copied from transformers.models.llama.modeling_llama.rotate_half
-def rotate_half(x):
- """Rotates half the hidden dims of the input."""
- x1 = x[..., : x.shape[-1] // 2]
- x2 = x[..., x.shape[-1] // 2 :]
- return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids, 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`):
- The position indices of the tokens corresponding to the query and key tensors. For example, this can be
- used to pass offsetted position ids when working with a KV-cache.
- 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[position_ids].unsqueeze(unsqueeze_dim)
- sin = sin[position_ids].unsqueeze(unsqueeze_dim)
- # Need fp32 here to match logits
- q_embed = (q.to(dtype=torch.float32) * cos.to(dtype=torch.float32)) + (
- rotate_half(q).to(dtype=torch.float32) * sin.to(dtype=torch.float32)
- )
- k_embed = (k.to(dtype=torch.float32) * cos.to(dtype=torch.float32)) + (
- rotate_half(k).to(dtype=torch.float32) * sin.to(dtype=torch.float32)
- )
- return q_embed.to(q.dtype), k_embed.to(k.dtype)
-
-
-class Phi3MLP(nn.Module):
- """Gated Linear Unit.
-
- Reference:
- Language Modeling with Gated Convolutional Networks.
- https://arxiv.org/pdf/1612.08083v3.pdf.
-
- """
-
- 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:
- y = self.gate_up_proj(hidden_states)
-
- # Special case for SwiGLU
- if self.config.hidden_act == "silu" and swiglu is not None:
- gate, y = y.chunk(2, dim=-1)
- y = swiglu(gate, y)
- else:
- gate, y = y.chunk(2, dim=-1)
- y = y * self.activation_fn(gate)
-
- return self.down_proj(y)
-
-
-# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
- """
- This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
- num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
- """
- batch, num_key_value_heads, slen, head_dim = hidden_states.shape
- if n_rep == 1:
- return hidden_states
- hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
- return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-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
- 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.attention_dropout = config.attention_dropout
- self.hidden_size = config.hidden_size
- self.num_heads = config.num_attention_heads
- self.head_dim = self.hidden_size // self.num_heads
- self.num_key_value_heads = config.num_key_value_heads
- self.num_key_value_groups = self.num_heads // self.num_key_value_heads
- self.max_position_embeddings = config.max_position_embeddings
- self.original_max_position_embeddings = config.original_max_position_embeddings
- self.rope_theta = config.rope_theta
- self.rope_scaling = config.rope_scaling
- self.is_causal = True
-
- if (self.head_dim * self.num_heads) != self.hidden_size:
- raise ValueError(
- f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
- f" and `num_heads`: {self.num_heads})."
- )
-
- op_size = self.num_heads * self.head_dim + 2 * (self.num_key_value_heads * self.head_dim)
- self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
-
- self.qkv_proj = nn.Linear(self.hidden_size, op_size, bias=False)
-
- if self.rope_scaling is None:
- self.rotary_emb = Phi3RotaryEmbedding(
- self.head_dim,
- max_position_embeddings=self.max_position_embeddings,
- base=self.rope_theta,
- )
- else:
- self.rotary_emb = Phi3LongScaledRotaryEmbedding(
- self.head_dim,
- self.config.rope_scaling["short_factor"],
- self.config.rope_scaling["long_factor"],
- max_position_embeddings=self.config.max_position_embeddings,
- original_max_position_embeddings=self.config.original_max_position_embeddings,
- base=self.config.rope_theta,
- )
-
- def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
- return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
- 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,
- ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
- logger.warning_once("You are not running the flash-attention implementation, expect numerical differences.")
-
- bsz, q_len, _ = hidden_states.size()
-
- qkv = self.qkv_proj(hidden_states)
- query_pos = self.num_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(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)
-
- kv_seq_len = key_states.shape[-2]
- if past_key_value is not None:
- if self.layer_idx is None:
- raise ValueError(
- f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
- "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
- "with a layer index."
- )
- kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
- cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
- 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} # Specific to RoPE models
- key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
- # repeat k/v heads if n_kv_heads < n_heads
- key_states = repeat_kv(key_states, self.num_key_value_groups)
- value_states = repeat_kv(value_states, self.num_key_value_groups)
-
- attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
- if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
- raise ValueError(
- f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
- f" {attn_weights.size()}"
- )
-
- if attention_mask is not None:
- if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
- raise ValueError(
- f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
- )
- attn_weights = attn_weights + attention_mask
-
- # upcast attention to fp32
- attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_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.hidden_size)
-
- attn_output = self.o_proj(attn_output)
-
- if not output_attentions:
- attn_weights = None
-
- return attn_output, attn_weights, past_key_value
-
-
-class Phi3FlashAttention2(Phi3Attention):
- """
- Phi-3 flash attention module. This module inherits from `Phi3Attention` as the weights of the module stays
- untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
- flash attention and deal with padding tokens in case the input contains any of them.
- """
-
- # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
- 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 alignement, 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 = not is_flash_attn_greater_or_equal_2_10()
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- attention_mask: Optional[torch.LongTensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- past_key_value: Optional[Cache] = None,
- output_attentions: bool = False,
- use_cache: bool = False,
- **kwargs,
- ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
- # Phi3FlashAttention2 attention does not support output_attentions
-
- if not _flash_supports_window_size:
- logger.warning_once(
- "The current flash attention version does not support sliding window attention. Please use `attn_implementation='eager'` or upgrade flash-attn library."
- )
- raise ValueError("The current flash attention version does not support sliding window attention.")
-
- output_attentions = False
-
- if "padding_mask" in kwargs:
- warnings.warn(
- "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
- )
-
- # overwrite attention_mask with padding_mask
- attention_mask = kwargs.pop("padding_mask")
-
- bsz, q_len, _ = hidden_states.size()
-
- qkv = self.qkv_proj(hidden_states)
- query_pos = self.num_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 :]
-
- # Flash attention requires the input to have the shape
- # batch_size x seq_length x head_dim x hidden_dim
- # therefore we just need to keep the original shape
- 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)
-
- kv_seq_len = key_states.shape[-2]
- if past_key_value is not None:
- if self.layer_idx is None:
- raise ValueError(
- f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
- "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
- "with a layer index."
- )
- kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-
- # Because the input can be padded, the absolute sequence length depends on the max position id.
- rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
- cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
-
- query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
- use_sliding_windows = (
- _flash_supports_window_size
- and getattr(self.config, "sliding_window", None) is not None
- and kv_seq_len > self.config.sliding_window
- )
-
- if past_key_value is not None:
- # Activate slicing cache only if the config has a value `sliding_windows` attribute
- cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
- if (
- getattr(self.config, "sliding_window", None) is not None
- and kv_seq_len > self.config.sliding_window
- and cache_has_contents
- ):
- slicing_tokens = 1 - self.config.sliding_window
-
- past_key = past_key_value[self.layer_idx][0]
- past_value = past_key_value[self.layer_idx][1]
-
- past_key = past_key[:, :, slicing_tokens:, :].contiguous()
- past_value = past_value[:, :, slicing_tokens:, :].contiguous()
-
- if past_key.shape[-2] != self.config.sliding_window - 1:
- raise ValueError(
- f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
- f" {past_key.shape}"
- )
-
- if attention_mask is not None:
- attention_mask = attention_mask[:, slicing_tokens:]
- attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
-
- cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
- key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
- # repeat k/v heads if n_kv_heads < n_heads
- key_states = repeat_kv(key_states, self.num_key_value_groups)
- value_states = repeat_kv(value_states, self.num_key_value_groups)
-
- attn_dropout = 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.
-
- if query_states.dtype == torch.float32:
- if torch.is_autocast_enabled():
- target_dtype = 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.qkv_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)
-
- # Reashape to the expected shape for Flash Attention
- query_states = query_states.transpose(1, 2)
- key_states = key_states.transpose(1, 2)
- value_states = value_states.transpose(1, 2)
-
- attn_output = self._flash_attention_forward(
- query_states,
- key_states,
- value_states,
- attention_mask,
- q_len,
- dropout=attn_dropout,
- use_sliding_windows=use_sliding_windows,
- )
-
- 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, past_key_value
-
- # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._flash_attention_forward
- def _flash_attention_forward(
- self,
- query_states,
- key_states,
- value_states,
- attention_mask,
- query_length,
- dropout=0.0,
- softmax_scale=None,
- use_sliding_windows=False,
- ):
- """
- Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
- first unpad the input, then computes the attention scores and pad the final attention scores.
-
- Args:
- query_states (`torch.Tensor`):
- Input query states to be passed to Flash Attention API
- key_states (`torch.Tensor`):
- Input key states to be passed to Flash Attention API
- value_states (`torch.Tensor`):
- Input value states to be passed to Flash Attention API
- attention_mask (`torch.Tensor`):
- The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
- position of padding tokens and 1 for the position of non-padding tokens.
- dropout (`float`):
- Attention dropout
- softmax_scale (`float`, *optional*):
- The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
- use_sliding_windows (`bool`, *optional*):
- Whether to activate sliding window attention.
- """
- if not self._flash_attn_uses_top_left_mask:
- causal = self.is_causal
- else:
- # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
- causal = self.is_causal and query_length != 1
-
- # Contains at least one padding token in the sequence
- if attention_mask is not None:
- batch_size = query_states.shape[0]
- query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
- query_states, key_states, value_states, attention_mask, query_length
- )
-
- cu_seqlens_q, cu_seqlens_k = cu_seq_lens
- max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-
- if not use_sliding_windows:
- attn_output_unpad = flash_attn_varlen_func(
- query_states,
- key_states,
- value_states,
- cu_seqlens_q=cu_seqlens_q,
- cu_seqlens_k=cu_seqlens_k,
- max_seqlen_q=max_seqlen_in_batch_q,
- max_seqlen_k=max_seqlen_in_batch_k,
- dropout_p=dropout,
- softmax_scale=softmax_scale,
- causal=causal,
- )
- else:
- attn_output_unpad = flash_attn_varlen_func(
- query_states,
- key_states,
- value_states,
- cu_seqlens_q=cu_seqlens_q,
- cu_seqlens_k=cu_seqlens_k,
- max_seqlen_q=max_seqlen_in_batch_q,
- max_seqlen_k=max_seqlen_in_batch_k,
- dropout_p=dropout,
- softmax_scale=softmax_scale,
- causal=causal,
- window_size=(self.config.sliding_window, self.config.sliding_window),
- )
-
- attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
- else:
- if not use_sliding_windows:
- attn_output = flash_attn_func(
- query_states,
- key_states,
- value_states,
- dropout,
- softmax_scale=softmax_scale,
- causal=causal,
- )
- else:
- attn_output = flash_attn_func(
- query_states,
- key_states,
- value_states,
- dropout,
- softmax_scale=softmax_scale,
- causal=causal,
- window_size=(self.config.sliding_window, self.config.sliding_window),
- )
-
- return attn_output
-
- # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
- def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
- batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
-
- # On the first iteration we need to properly re-create the padding mask
- # by slicing it on the proper place
- if kv_seq_len != attention_mask.shape[-1]:
- attention_mask_num_tokens = attention_mask.shape[-1]
- attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
-
- indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
-
- key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
- value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
-
- if query_length == kv_seq_len:
- query_layer = index_first_axis(
- query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
- )
- cu_seqlens_q = cu_seqlens_k
- max_seqlen_in_batch_q = max_seqlen_in_batch_k
- indices_q = indices_k
- elif query_length == 1:
- max_seqlen_in_batch_q = 1
- cu_seqlens_q = torch.arange(
- batch_size + 1, dtype=torch.int32, device=query_layer.device
- ) # There is a memcpy here, that is very bad.
- indices_q = cu_seqlens_q[:-1]
- query_layer = query_layer.squeeze(1)
- else:
- # The -q_len: slice assumes left padding.
- attention_mask = attention_mask[:, -query_length:]
- query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
-
- return (
- query_layer,
- key_layer,
- value_layer,
- indices_q,
- (cu_seqlens_q, cu_seqlens_k),
- (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
- )
-
-
-# copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Phi3
-# TODO @Arthur no longer copied from LLama after static cache
-class Phi3SdpaAttention(Phi3Attention):
- """
- Phi3 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
- `Phi3Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
- SDPA API.
- """
-
- # Adapted from Phi3Attention.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,
- ) -> 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(
- "Phi3Model is using Phi3SdpaAttention, 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,
- )
-
- bsz, q_len, _ = hidden_states.size()
-
- qkv = self.qkv_proj(hidden_states)
- query_pos = self.num_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(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)
-
- kv_seq_len = key_states.shape[-2]
- if past_key_value is not None:
- kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
- cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-
- 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} # 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)
-
- if attention_mask is not None:
- if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
- raise ValueError(
- f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
- )
-
- # 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()
-
- attn_output = torch.nn.functional.scaled_dot_product_attention(
- query_states,
- key_states,
- value_states,
- attn_mask=attention_mask,
- dropout_p=self.attention_dropout if self.training else 0.0,
- # 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=self.is_causal and attention_mask is None and q_len > 1,
- )
-
- 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, past_key_value
-
-
-PHI3_ATTENTION_CLASSES = {
- "eager": Phi3Attention,
- "flash_attention_2": Phi3FlashAttention2,
- "sdpa": Phi3SdpaAttention,
-}
-
-
-class Phi3DecoderLayer(nn.Module):
- def __init__(self, config: Phi3Config, layer_idx: int):
- super().__init__()
-
- self.config = config
- self.self_attn = PHI3_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
-
- self.mlp = Phi3MLP(config)
- self.input_layernorm = PHI3_NORM_CLASS(config.hidden_size, eps=config.rms_norm_eps)
-
- self.resid_attn_dropout = nn.Dropout(config.resid_pdrop)
- self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop)
- self.post_attention_layernorm = PHI3_NORM_CLASS(config.hidden_size, eps=config.rms_norm_eps)
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- attention_mask: Optional[torch.Tensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- past_key_value: Optional[Tuple[torch.Tensor]] = None,
- output_attentions: Optional[bool] = False,
- use_cache: Optional[bool] = False,
- **kwargs,
- ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
- if "padding_mask" in kwargs:
- warnings.warn(
- "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
- )
- """
- 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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
- position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
- `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
- output_attentions (`bool`, *optional*):
- Whether or not to return the attentions tensors of all attention layers. See `attentions` under
- returned tensors for more detail.
- use_cache (`bool`, *optional*):
- If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
- (see `past_key_values`).
- past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
- """
-
- residual = hidden_states
-
- hidden_states = self.input_layernorm(hidden_states)
-
- # Self Attention
- attn_outputs, self_attn_weights, present_key_value = 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,
- )
-
- hidden_states = residual + self.resid_attn_dropout(attn_outputs)
-
- 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)
-
- outputs = (hidden_states,)
-
- if output_attentions:
- outputs += (self_attn_weights,)
-
- if use_cache:
- outputs += (present_key_value,)
-
- return outputs
-
-
-PHI3_START_DOCSTRING = r"""
- This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
- library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
- etc.)
-
- This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
- Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
- and behavior.
-
- Parameters:
- config ([`Phi3Config`]):
- Model configuration class with all the parameters of the model. Initializing with a config file does not
- load the weights associated with the model, only the configuration. Check out the
- [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-@add_start_docstrings(
- "The bare Phi-3 model outputting raw hidden-states without any specific head on top.",
- PHI3_START_DOCSTRING,
-)
-class Phi3PreTrainedModel(PreTrainedModel):
- config_class = Phi3Config
- base_model_prefix = "model"
- supports_gradient_checkpointing = True
- _no_split_modules = ["Phi3DecoderLayer"]
- _skip_keys_device_placement = "past_key_values"
- _supports_flash_attn_2 = True
- _supports_sdpa = False
- _supports_cache_class = True
-
- _version = "0.0.5"
-
- 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_()
-
-
-PHI3_INPUTS_DOCSTRING = r"""
- Args:
- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
- it.
-
- Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
- [`PreTrainedTokenizer.__call__`] for details.
-
- [What are input IDs?](../glossary#input-ids)
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
- - 1 for tokens that are **not masked**,
- - 0 for tokens that are **masked**.
-
- [What are attention masks?](../glossary#attention-mask)
-
- Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
- [`PreTrainedTokenizer.__call__`] for details.
-
- If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
- `past_key_values`).
-
- If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
- and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
- information on the default strategy.
-
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
- config.n_positions - 1]`.
-
- [What are position IDs?](../glossary#position-ids)
- past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
- blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
- returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
-
- Two formats are allowed:
- - a [`~cache_utils.Cache`] instance;
- - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
- shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
- cache format.
-
- The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
- legacy cache format will be returned.
-
- If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
- have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
- of shape `(batch_size, sequence_length)`.
- inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
- Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
- is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
- model's internal embedding lookup matrix.
- 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`).
- 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_hidden_states (`bool`, *optional*):
- Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
- more detail.
- return_dict (`bool`, *optional*):
- Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
- "The bare Phi-3 model outputting raw hidden-states without any specific head on top.",
- PHI3_START_DOCSTRING,
-)
-class Phi3Model(Phi3PreTrainedModel):
- """
- Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
-
- Args:
- config: Phi3Config
- """
-
- def __init__(self, config: Phi3Config):
- 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.embed_dropout = nn.Dropout(config.embd_pdrop)
- self.layers = nn.ModuleList(
- [Phi3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
- )
- self.norm = PHI3_NORM_CLASS(config.hidden_size, eps=config.rms_norm_eps)
-
- self._attn_implementation = config._attn_implementation
-
- self.gradient_checkpointing = False
- # Initialize weights and apply final processing
- self.post_init()
-
- def get_input_embeddings(self):
- return self.embed_tokens
-
- def set_input_embeddings(self, value):
- self.embed_tokens = value
-
- @add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING)
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.Tensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- past_key_values: Optional[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,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple, BaseModelOutputWithPast]:
- 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
- )
- use_cache = use_cache if use_cache is not None else self.config.use_cache
-
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- # retrieve input_ids and inputs_embeds
- if input_ids is not None and inputs_embeds is not None:
- raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
- elif input_ids is not None:
- batch_size, seq_length = input_ids.shape[:2]
- elif inputs_embeds is not None:
- batch_size, seq_length = inputs_embeds.shape[:2]
- else:
- raise ValueError("You have to specify either input_ids or inputs_embeds")
-
- past_key_values_length = 0
-
- 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
-
- if use_cache:
- use_legacy_cache = not isinstance(past_key_values, Cache)
- if use_legacy_cache:
- past_key_values = DynamicCache.from_legacy_cache(past_key_values)
- past_key_values_length = past_key_values.get_usable_length(seq_length)
-
- if position_ids is None:
- device = input_ids.device if input_ids is not None else inputs_embeds.device
- position_ids = torch.arange(
- past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
- )
- position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
- else:
- position_ids = position_ids.view(-1, seq_length).long()
-
- if inputs_embeds is None:
- inputs_embeds = self.embed_tokens(input_ids)
-
- if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
- 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 Phi3. Make sure to "
- " call `tokenizer.padding_side = 'left'` before tokenizing the input. "
- )
-
- if self._attn_implementation == "flash_attention_2":
- # 2d mask is passed through the layers
- attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
- else:
- # 4d mask is passed through the layers
- attention_mask = _prepare_4d_causal_attention_mask(
- attention_mask,
- (batch_size, seq_length),
- inputs_embeds,
- past_key_values_length,
- sliding_window=self.config.sliding_window,
- )
-
- hidden_states = inputs_embeds
-
- # decoder layers
- all_hidden_states = () if output_hidden_states else None
- all_self_attns = () if output_attentions else None
- next_decoder_cache = None
-
- for decoder_layer in self.layers:
- if output_hidden_states:
- all_hidden_states += (hidden_states,)
-
- if self.gradient_checkpointing and self.training:
- layer_outputs = self._gradient_checkpointing_func(
- decoder_layer.__call__,
- hidden_states,
- attention_mask,
- position_ids,
- past_key_values,
- output_attentions,
- use_cache,
- )
- else:
- layer_outputs = decoder_layer(
- hidden_states,
- attention_mask=attention_mask,
- position_ids=position_ids,
- past_key_value=past_key_values,
- output_attentions=output_attentions,
- use_cache=use_cache,
- )
-
- hidden_states = layer_outputs[0]
-
- if use_cache:
- next_decoder_cache = layer_outputs[2 if output_attentions else 1]
-
- if output_attentions:
- all_self_attns += (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,)
-
- next_cache = None
- if use_cache:
- next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
- if not return_dict:
- return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
- return BaseModelOutputWithPast(
- last_hidden_state=hidden_states,
- past_key_values=next_cache,
- hidden_states=all_hidden_states,
- attentions=all_self_attns,
- )
-
-
-class Phi3ForCausalLM(Phi3PreTrainedModel):
- _tied_weights_keys = ["lm_head.weight"]
-
- # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi3
- def __init__(self, config):
- super().__init__(config)
- self.model = Phi3Model(config)
- self.vocab_size = config.vocab_size
- self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
- def get_input_embeddings(self):
- return self.model.embed_tokens
-
- # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
- def set_input_embeddings(self, value):
- self.model.embed_tokens = value
-
- # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
- def get_output_embeddings(self):
- return self.lm_head
-
- # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
- def set_output_embeddings(self, new_embeddings):
- self.lm_head = new_embeddings
-
- # 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
-
- @add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING)
- @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.Tensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- past_key_values: Optional[List[torch.FloatTensor]] = 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,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple, CausalLMOutputWithPast]:
- r"""
- Args:
- 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]`.
-
- Returns:
-
- Example:
-
- ```python
- >>> from transformers import AutoTokenizer, Phi3ForCausalLM
-
- >>> model = Phi3ForCausalLM.from_pretrained("microsoft/phi-3")
- >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-3")
-
- >>> prompt = "This is an example script ."
- >>> 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]
- 'This is an example script .\n\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str'
- ```"""
-
- 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
- )
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
- outputs = self.model(
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- past_key_values=past_key_values,
- inputs_embeds=inputs_embeds,
- use_cache=use_cache,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- hidden_states = outputs[0]
- logits = self.lm_head(hidden_states)
- logits = logits.float()
-
- loss = None
- if labels is not None:
- # Shift so that tokens < n predict n
- shift_logits = logits[..., :-1, :].contiguous()
- shift_labels = labels[..., 1:].contiguous()
- # Flatten the tokens
- loss_fct = CrossEntropyLoss()
- shift_logits = shift_logits.view(-1, self.config.vocab_size)
- shift_labels = shift_labels.view(-1)
- # Enable model parallelism
- shift_labels = shift_labels.to(shift_logits.device)
- loss = loss_fct(shift_logits, shift_labels)
-
- if not return_dict:
- output = (logits,) + outputs[1:]
- return (loss,) + output if loss is not None else output
-
- return CausalLMOutputWithPast(
- loss=loss,
- logits=logits,
- past_key_values=outputs.past_key_values,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
- # Copied from transformers.models.persimmon.modeling_persimmon.PersimmonForCausalLM.prepare_inputs_for_generation
- def prepare_inputs_for_generation(
- self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
- ):
- if past_key_values is not None:
- if isinstance(past_key_values, Cache):
- cache_length = past_key_values.get_seq_length()
- past_length = past_key_values.seen_tokens
- max_cache_length = past_key_values.get_max_length()
- else:
- cache_length = past_length = past_key_values[0][0].shape[2]
- max_cache_length = None
-
- # Keep only the unprocessed tokens:
- # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
- # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
- # input)
- if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
- input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
- # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
- # input_ids based on the past_length.
- elif past_length < input_ids.shape[1]:
- input_ids = input_ids[:, past_length:]
- # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
-
- # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
- if (
- max_cache_length is not None
- and attention_mask is not None
- and cache_length + input_ids.shape[1] > max_cache_length
- ):
- attention_mask = attention_mask[:, -max_cache_length:]
-
- position_ids = kwargs.get("position_ids", None)
- if attention_mask is not None and position_ids is None:
- # create position_ids on the fly for batch generation
- position_ids = attention_mask.long().cumsum(-1) - 1
- position_ids.masked_fill_(attention_mask == 0, 1)
- if past_key_values:
- position_ids = position_ids[:, -input_ids.shape[1] :]
-
- # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
- if inputs_embeds is not None and past_key_values is None:
- model_inputs = {"inputs_embeds": inputs_embeds}
- else:
- model_inputs = {"input_ids": input_ids}
-
- model_inputs.update(
- {
- "position_ids": position_ids,
- "past_key_values": past_key_values,
- "use_cache": kwargs.get("use_cache"),
- "attention_mask": attention_mask,
- }
- )
- return model_inputs
-
- @staticmethod
- # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
- def _reorder_cache(past_key_values, beam_idx):
- reordered_past = ()
- for layer_past in past_key_values:
- reordered_past += (
- tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
- )
- return reordered_past
-
-
-@add_start_docstrings(
- """
- The [`Phi3Model`] with a sequence classification head on top (linear layer).
-
- [`Phi3ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
- (e.g. GPT-2) do.
-
- Since it does classification on the last token, it requires to know the position of the last token. If a
- `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
- no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
- padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
- each row of the batch).
- """,
- PHI3_START_DOCSTRING,
-)
-# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Phi3, LLAMA->PHI3, self.transformer->self.model, transformer_outputs->model_outputs
-class Phi3ForSequenceClassification(Phi3PreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
- self.num_labels = config.num_labels
- self.model = Phi3Model(config)
- self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- def get_input_embeddings(self):
- return self.model.embed_tokens
-
- def set_input_embeddings(self, value):
- self.model.embed_tokens = value
-
- @add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING)
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.Tensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- past_key_values: Optional[List[torch.FloatTensor]] = 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,
- return_dict: Optional[bool] = None,
- ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
- r"""
- labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
- Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
- config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
- `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- model_outputs = self.model(
- 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,
- return_dict=return_dict,
- )
- hidden_states = model_outputs[0]
- logits = self.score(hidden_states)
-
- if input_ids is not None:
- batch_size = input_ids.shape[0]
- else:
- batch_size = inputs_embeds.shape[0]
-
- if self.config.pad_token_id is None and batch_size != 1:
- raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
- if self.config.pad_token_id is None:
- sequence_lengths = -1
- else:
- if input_ids is not None:
- # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
- sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
- sequence_lengths = sequence_lengths % input_ids.shape[-1]
- sequence_lengths = sequence_lengths.to(logits.device)
- else:
- sequence_lengths = -1
-
- pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
-
- loss = None
- if labels is not None:
- labels = labels.to(logits.device)
- if self.config.problem_type is None:
- if self.num_labels == 1:
- self.config.problem_type = "regression"
- elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
- self.config.problem_type = "single_label_classification"
- else:
- self.config.problem_type = "multi_label_classification"
-
- if self.config.problem_type == "regression":
- loss_fct = MSELoss()
- if self.num_labels == 1:
- loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
- else:
- loss = loss_fct(pooled_logits, labels)
- elif self.config.problem_type == "single_label_classification":
- loss_fct = CrossEntropyLoss()
- loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
- elif self.config.problem_type == "multi_label_classification":
- loss_fct = BCEWithLogitsLoss()
- loss = loss_fct(pooled_logits, labels)
- if not return_dict:
- output = (pooled_logits,) + model_outputs[1:]
- return ((loss,) + output) if loss is not None else output
-
- return SequenceClassifierOutputWithPast(
- loss=loss,
- logits=pooled_logits,
- past_key_values=model_outputs.past_key_values,
- hidden_states=model_outputs.hidden_states,
- attentions=model_outputs.attentions,
- )
-
-
-@add_start_docstrings(
- """
- [`Phi3Model`] with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
- Named-Entity-Recognition (NER) tasks.
- """,
- PHI3_START_DOCSTRING,
-)
-# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with Mpt->Phi3,MPT->PHI3,self.transformer->self.model,transformer_outputs->model_outputs
-class Phi3ForTokenClassification(Phi3PreTrainedModel):
- def __init__(self, config: Phi3Config):
- super().__init__(config)
- self.num_labels = config.num_labels
-
- self.model = Phi3Model(config)
- if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
- classifier_dropout = config.classifier_dropout
- elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
- classifier_dropout = config.hidden_dropout
- else:
- classifier_dropout = 0.1
- self.dropout = nn.Dropout(classifier_dropout)
- self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- @add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING)
- @add_code_sample_docstrings(
- checkpoint=_CHECKPOINT_FOR_DOC,
- output_type=TokenClassifierOutput,
- config_class=_CONFIG_FOR_DOC,
- )
- def forward(
- self,
- input_ids: Optional[torch.LongTensor] = None,
- past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
- attention_mask: Optional[torch.Tensor] = None,
- inputs_embeds: Optional[torch.Tensor] = None,
- labels: Optional[torch.Tensor] = None,
- use_cache: Optional[bool] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- **deprecated_arguments,
- ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
- r"""
- labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
- Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
- config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
- `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- model_outputs = self.model(
- input_ids,
- past_key_values=past_key_values,
- attention_mask=attention_mask,
- inputs_embeds=inputs_embeds,
- use_cache=use_cache,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- hidden_states = model_outputs[0]
- hidden_states = self.dropout(hidden_states)
- logits = self.classifier(hidden_states)
-
- loss = None
- if labels is not None:
- # move labels to correct device to enable model parallelism
- labels = labels.to(logits.device)
- batch_size, seq_length = labels.shape
- loss_fct = CrossEntropyLoss()
- loss = loss_fct(
- logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
- )
-
- if not return_dict:
- output = (logits,) + model_outputs[2:]
- return ((loss,) + output) if loss is not None else output
-
- return TokenClassifierOutput(
- loss=loss,
- logits=logits,
- hidden_states=model_outputs.hidden_states,
- attentions=model_outputs.attentions,
- )
+# 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."""
+
+import inspect
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from transformers.modeling_outputs import (
+ BaseModelOutputWithPast,
+ CausalLMOutputWithPast,
+ SequenceClassifierOutputWithPast,
+ TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+ add_code_sample_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ is_flash_attn_2_available,
+ is_flash_attn_greater_or_equal_2_10,
+ logging,
+ replace_return_docstrings,
+)
+from .configuration_phi3 import Phi3Config
+
+
+logger = logging.get_logger(__name__)
+
+# Transformers scans dependencies in the modeling file, causing issues on conditional loading. The regex only ignores try/catch blocks, but not if statements
+# if is_flash_attn_2_available():
+_flash_supports_window_size = False
+try:
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
+
+ _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+except ImportError as error:
+ logger.warning(
+ f"`flash-attention` package not found, consider installing for better performance: {error}."
+ )
+ if not _flash_supports_window_size:
+ logger.warning(
+ "Current `flash-attention` does not support `window_size`. Either upgrade or use `attn_implementation='eager'`."
+ )
+
+_CHECKPOINT_FOR_DOC = "microsoft/Phi-3-mini-4k-instruct"
+_CONFIG_FOR_DOC = "Phi3Config"
+
+PHI3_PRETRAINED_MODEL_ARCHIVE_LIST = [
+ "microsoft/Phi-3-mini-4k-instruct",
+ "microsoft/Phi-3-mini-128k-instruct",
+ # See all Phi-3 models at https://huggingface.co/models?filter=Phi-3
+]
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Phi3
+class Phi3RMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ Phi3RMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+ return (
+ indices,
+ cu_seqlens,
+ max_seqlen_in_batch,
+ )
+
+
+# Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with gemma->phi3, Gemma->Phi3
+class Phi3RotaryEmbedding(nn.Module):
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+ super().__init__()
+
+ self.dim = dim
+ self.max_position_embeddings = max_position_embeddings
+ self.base = base
+ self.register_buffer("inv_freq", None, persistent=False)
+
+ @torch.no_grad()
+ def forward(self, x, position_ids, seq_len=None):
+ # x: [bs, num_attention_heads, seq_len, head_size]
+ if self.inv_freq is None:
+ self.inv_freq = 1.0 / (
+ self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim)
+ )
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+ position_ids_expanded = position_ids[:, None, :].float()
+ # Force float32 since bfloat16 loses precision on long contexts
+ # See https://github.com/huggingface/transformers/pull/29285
+ device_type = x.device.type
+ device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False):
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos()
+ sin = emb.sin()
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class Phi3LongRoPEScaledRotaryEmbedding(Phi3RotaryEmbedding):
+ def __init__(self, dim, config, device=None):
+ super().__init__(dim, config.max_position_embeddings, config.rope_theta, device)
+
+ self.short_factor = config.rope_scaling["short_factor"]
+ self.long_factor = config.rope_scaling["long_factor"]
+ self.original_max_position_embeddings = config.original_max_position_embeddings
+
+ @torch.no_grad()
+ def forward(self, x, position_ids, seq_len=None):
+ seq_len = torch.max(position_ids) + 1
+ if seq_len > self.original_max_position_embeddings:
+ ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device)
+ else:
+ ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device)
+
+ inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim
+ self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape)
+
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ # Force float32 since bfloat16 loses precision on long contexts
+ # See https://github.com/huggingface/transformers/pull/29285
+ device_type = x.device.type
+ device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False):
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+
+ scale = self.max_position_embeddings / self.original_max_position_embeddings
+ if scale <= 1.0:
+ scaling_factor = 1.0
+ else:
+ scaling_factor = math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings))
+
+ cos = emb.cos() * scaling_factor
+ sin = emb.sin() * scaling_factor
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+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)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+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)
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+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
+ 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.attention_dropout = config.attention_dropout
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.original_max_position_embeddings = config.original_max_position_embeddings
+ self.rope_theta = config.rope_theta
+ self.rope_scaling = config.rope_scaling
+ self.is_causal = True
+
+ if (self.head_dim * self.num_heads) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ op_size = self.num_heads * self.head_dim + 2 * (self.num_key_value_heads * self.head_dim)
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+ self.qkv_proj = nn.Linear(self.hidden_size, op_size, bias=False)
+ self._init_rope()
+
+ def _init_rope(self):
+ if self.rope_scaling is None:
+ self.rotary_emb = Phi3RotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ base=self.rope_theta,
+ )
+ else:
+ scaling_type = self.config.rope_scaling["type"]
+ if scaling_type == "longrope":
+ self.rotary_emb = Phi3LongRoPEScaledRotaryEmbedding(self.head_dim, self.config)
+ else:
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+ 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,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ logger.warning_once("You are not running the flash-attention implementation, expect numerical differences.")
+
+ bsz, q_len, _ = hidden_states.size()
+
+ qkv = self.qkv_proj(hidden_states)
+ query_pos = self.num_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(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)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ if self.layer_idx is None:
+ raise ValueError(
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+ "with a layer index."
+ )
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
+
+ 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} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ # repeat k/v heads if n_kv_heads < n_heads
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_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.hidden_size)
+
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value
+
+
+class Phi3FlashAttention2(Phi3Attention):
+ """
+ Phi-3 flash attention module. This module inherits from `Phi3Attention` as the weights of the module stays
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+ flash attention and deal with padding tokens in case the input contains any of them.
+ """
+
+ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+ 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 alignement, 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 = not is_flash_attn_greater_or_equal_2_10()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ # Phi3FlashAttention2 attention does not support output_attentions
+
+ if not _flash_supports_window_size:
+ logger.warning_once(
+ "The current flash attention version does not support sliding window attention. Please use `attn_implementation='eager'` or upgrade flash-attn library."
+ )
+ raise ValueError("The current flash attention version does not support sliding window attention.")
+
+ output_attentions = False
+
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+
+ # overwrite attention_mask with padding_mask
+ attention_mask = kwargs.pop("padding_mask")
+
+ bsz, q_len, _ = hidden_states.size()
+
+ qkv = self.qkv_proj(hidden_states)
+ query_pos = self.num_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 :]
+
+ # Flash attention requires the input to have the shape
+ # batch_size x seq_length x head_dim x hidden_dim
+ # therefore we just need to keep the original shape
+ 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)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ if self.layer_idx is None:
+ raise ValueError(
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+ "with a layer index."
+ )
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+ # Because the input can be padded, the absolute sequence length depends on the max position id.
+ rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
+ cos, sin = self.rotary_emb(value_states, position_ids, seq_len=rotary_seq_len)
+
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ use_sliding_windows = (
+ _flash_supports_window_size
+ and getattr(self.config, "sliding_window", None) is not None
+ and kv_seq_len > self.config.sliding_window
+ )
+
+ if past_key_value is not None:
+ # Activate slicing cache only if the config has a value `sliding_windows` attribute
+ cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
+ if (
+ getattr(self.config, "sliding_window", None) is not None
+ and kv_seq_len > self.config.sliding_window
+ and cache_has_contents
+ ):
+ slicing_tokens = 1 - self.config.sliding_window
+
+ past_key = past_key_value[self.layer_idx][0]
+ past_value = past_key_value[self.layer_idx][1]
+
+ past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+ past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+ if past_key.shape[-2] != self.config.sliding_window - 1:
+ raise ValueError(
+ f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+ f" {past_key.shape}"
+ )
+
+ if attention_mask is not None:
+ attention_mask = attention_mask[:, slicing_tokens:]
+ attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
+
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ # repeat k/v heads if n_kv_heads < n_heads
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_dropout = 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.
+
+ if query_states.dtype == torch.float32:
+ if torch.is_autocast_enabled():
+ target_dtype = 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.qkv_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)
+
+ # Reashape to the expected shape for Flash Attention
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ attn_output = self._flash_attention_forward(
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ q_len,
+ dropout=attn_dropout,
+ use_sliding_windows=use_sliding_windows,
+ )
+
+ 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, past_key_value
+
+ # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._flash_attention_forward
+ def _flash_attention_forward(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ query_length,
+ dropout=0.0,
+ softmax_scale=None,
+ use_sliding_windows=False,
+ ):
+ """
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+ first unpad the input, then computes the attention scores and pad the final attention scores.
+
+ Args:
+ query_states (`torch.Tensor`):
+ Input query states to be passed to Flash Attention API
+ key_states (`torch.Tensor`):
+ Input key states to be passed to Flash Attention API
+ value_states (`torch.Tensor`):
+ Input value states to be passed to Flash Attention API
+ attention_mask (`torch.Tensor`):
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+ position of padding tokens and 1 for the position of non-padding tokens.
+ dropout (`float`):
+ Attention dropout
+ softmax_scale (`float`, *optional*):
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+ use_sliding_windows (`bool`, *optional*):
+ Whether to activate sliding window attention.
+ """
+ if not self._flash_attn_uses_top_left_mask:
+ causal = self.is_causal
+ else:
+ # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+ causal = self.is_causal and query_length != 1
+
+ # Contains at least one padding token in the sequence
+ if attention_mask is not None:
+ batch_size = query_states.shape[0]
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+ query_states, key_states, value_states, attention_mask, query_length
+ )
+
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+ if not use_sliding_windows:
+ attn_output_unpad = flash_attn_varlen_func(
+ query_states,
+ key_states,
+ value_states,
+ cu_seqlens_q=cu_seqlens_q,
+ cu_seqlens_k=cu_seqlens_k,
+ max_seqlen_q=max_seqlen_in_batch_q,
+ max_seqlen_k=max_seqlen_in_batch_k,
+ dropout_p=dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ )
+ else:
+ attn_output_unpad = flash_attn_varlen_func(
+ query_states,
+ key_states,
+ value_states,
+ cu_seqlens_q=cu_seqlens_q,
+ cu_seqlens_k=cu_seqlens_k,
+ max_seqlen_q=max_seqlen_in_batch_q,
+ max_seqlen_k=max_seqlen_in_batch_k,
+ dropout_p=dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ window_size=(self.config.sliding_window, self.config.sliding_window),
+ )
+
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+ else:
+ if not use_sliding_windows:
+ attn_output = flash_attn_func(
+ query_states,
+ key_states,
+ value_states,
+ dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ )
+ else:
+ attn_output = flash_attn_func(
+ query_states,
+ key_states,
+ value_states,
+ dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ window_size=(self.config.sliding_window, self.config.sliding_window),
+ )
+
+ return attn_output
+
+ # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+ batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+
+ # On the first iteration we need to properly re-create the padding mask
+ # by slicing it on the proper place
+ if kv_seq_len != attention_mask.shape[-1]:
+ attention_mask_num_tokens = attention_mask.shape[-1]
+ attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+
+ key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+ value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+
+ if query_length == kv_seq_len:
+ query_layer = index_first_axis(
+ query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+ )
+ cu_seqlens_q = cu_seqlens_k
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
+ indices_q = indices_k
+ elif query_length == 1:
+ max_seqlen_in_batch_q = 1
+ cu_seqlens_q = torch.arange(
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
+ ) # There is a memcpy here, that is very bad.
+ indices_q = cu_seqlens_q[:-1]
+ query_layer = query_layer.squeeze(1)
+ else:
+ # The -q_len: slice assumes left padding.
+ attention_mask = attention_mask[:, -query_length:]
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+ return (
+ query_layer,
+ key_layer,
+ value_layer,
+ indices_q,
+ (cu_seqlens_q, cu_seqlens_k),
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+ )
+
+
+# copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Phi3
+# TODO @Arthur no longer copied from LLama after static cache
+class Phi3SdpaAttention(Phi3Attention):
+ """
+ Phi3 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+ `Phi3Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+ SDPA API.
+ """
+
+ # Adapted from Phi3Attention.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,
+ ) -> 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(
+ "Phi3Model is using Phi3SdpaAttention, 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,
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ qkv = self.qkv_proj(hidden_states)
+ query_pos = self.num_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(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)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
+
+ 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} # 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)
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+
+ # 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()
+
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attn_mask=attention_mask,
+ dropout_p=self.attention_dropout if self.training else 0.0,
+ # 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=self.is_causal and attention_mask is None and q_len > 1,
+ )
+
+ 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, past_key_value
+
+
+PHI3_ATTENTION_CLASSES = {
+ "eager": Phi3Attention,
+ "flash_attention_2": Phi3FlashAttention2,
+ "sdpa": Phi3SdpaAttention,
+}
+
+
+class Phi3DecoderLayer(nn.Module):
+ def __init__(self, config: Phi3Config, layer_idx: int):
+ super().__init__()
+
+ self.config = config
+ self.self_attn = PHI3_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
+
+ self.mlp = Phi3MLP(config)
+ self.input_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.resid_attn_dropout = nn.Dropout(config.resid_pdrop)
+ self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop)
+ self.post_attention_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ **kwargs,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+ """
+ 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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
+ `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ """
+
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ attn_outputs, self_attn_weights, present_key_value = 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,
+ )
+
+ hidden_states = residual + self.resid_attn_dropout(attn_outputs)
+
+ 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)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ return outputs
+
+
+PHI3_START_DOCSTRING = r"""
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`Phi3Config`]):
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
+ load the weights associated with the model, only the configuration. Check out the
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+ "The bare Phi-3 model outputting raw hidden-states without any specific head on top.",
+ PHI3_START_DOCSTRING,
+)
+class Phi3PreTrainedModel(PreTrainedModel):
+ config_class = Phi3Config
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["Phi3DecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_flash_attn_2 = True
+ _supports_sdpa = False
+ _supports_cache_class = True
+
+ _version = "0.0.5"
+
+ 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_()
+
+
+PHI3_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ Two formats are allowed:
+ - a [`~cache_utils.Cache`] instance;
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+ cache format.
+
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+ legacy cache format will be returned.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+ of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ 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`).
+ 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_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare Phi-3 model outputting raw hidden-states without any specific head on top.",
+ PHI3_START_DOCSTRING,
+)
+class Phi3Model(Phi3PreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
+
+ Args:
+ config: Phi3Config
+ """
+
+ def __init__(self, config: Phi3Config):
+ 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.embed_dropout = nn.Dropout(config.embd_pdrop)
+ self.layers = nn.ModuleList(
+ [Phi3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self._attn_implementation = config._attn_implementation
+ self.norm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[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,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
+ 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
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ batch_size, seq_length = input_ids.shape[:2]
+ elif inputs_embeds is not None:
+ batch_size, seq_length = inputs_embeds.shape[:2]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ past_key_values_length = 0
+
+ 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
+
+ if use_cache:
+ use_legacy_cache = not isinstance(past_key_values, Cache)
+ if use_legacy_cache:
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+ if position_ids is None:
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+ position_ids = torch.arange(
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+ )
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+ else:
+ position_ids = position_ids.view(-1, seq_length).long()
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+ 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 Phi3. Make sure to "
+ " call `tokenizer.padding_side = 'left'` before tokenizing the input. "
+ )
+
+ if self._attn_implementation == "flash_attention_2":
+ # 2d mask is passed through the layers
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+ else:
+ # 4d mask is passed through the layers
+ attention_mask = _prepare_4d_causal_attention_mask(
+ attention_mask,
+ (batch_size, seq_length),
+ inputs_embeds,
+ past_key_values_length,
+ sliding_window=self.config.sliding_window,
+ )
+
+ hidden_states = inputs_embeds
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ next_decoder_cache = None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if self.gradient_checkpointing and self.training:
+ layer_outputs = self._gradient_checkpointing_func(
+ decoder_layer.__call__,
+ hidden_states,
+ attention_mask,
+ position_ids,
+ past_key_values,
+ output_attentions,
+ use_cache,
+ )
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+ if output_attentions:
+ all_self_attns += (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,)
+
+ next_cache = None
+ if use_cache:
+ next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+ if not return_dict:
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=next_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class Phi3ForCausalLM(Phi3PreTrainedModel):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi3
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = Phi3Model(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ # 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
+
+ # Ignore copy
+ @add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = 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,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+ r"""
+ Args:
+ 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]`.
+
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, Phi3ForCausalLM
+
+ >>> model = Phi3ForCausalLM.from_pretrained("microsoft/phi-3-mini-4k-instruct")
+ >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-3-mini-4k-instruct")
+
+ >>> prompt = "This is an example script ."
+ >>> 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]
+ 'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum'
+ ```"""
+
+ 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
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ logits = self.lm_head(hidden_states)
+ logits = logits.float()
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ # Copied from transformers.models.persimmon.modeling_persimmon.PersimmonForCausalLM.prepare_inputs_for_generation
+ def prepare_inputs_for_generation(
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+ ):
+ if past_key_values is not None:
+ if isinstance(past_key_values, Cache):
+ cache_length = past_key_values.get_seq_length()
+ past_length = past_key_values.seen_tokens
+ max_cache_length = past_key_values.get_max_length()
+ else:
+ cache_length = past_length = past_key_values[0][0].shape[2]
+ max_cache_length = None
+
+ # Keep only the unprocessed tokens:
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+ # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+ # input)
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+ # input_ids based on the past_length.
+ elif past_length < input_ids.shape[1]:
+ input_ids = input_ids[:, past_length:]
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+ if (
+ max_cache_length is not None
+ and attention_mask is not None
+ and cache_length + input_ids.shape[1] > max_cache_length
+ ):
+ attention_mask = attention_mask[:, -max_cache_length:]
+
+ position_ids = kwargs.get("position_ids", None)
+ if attention_mask is not None and position_ids is None:
+ # create position_ids on the fly for batch generation
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ if past_key_values:
+ position_ids = position_ids[:, -input_ids.shape[1] :]
+
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids}
+
+ model_inputs.update(
+ {
+ "position_ids": position_ids,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "attention_mask": attention_mask,
+ }
+ )
+ return model_inputs
+
+ @staticmethod
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
+ def _reorder_cache(past_key_values, beam_idx):
+ reordered_past = ()
+ for layer_past in past_key_values:
+ reordered_past += (
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+ )
+ return reordered_past
+
+
+@add_start_docstrings(
+ """
+ The [`Phi3Model`] with a sequence classification head on top (linear layer).
+
+ [`Phi3ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """,
+ PHI3_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Phi3, LLAMA->PHI3, self.transformer->self.model, transformer_outputs->model_outputs
+class Phi3ForSequenceClassification(Phi3PreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.model = Phi3Model(config)
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = 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,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ model_outputs = self.model(
+ 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,
+ return_dict=return_dict,
+ )
+ hidden_states = model_outputs[0]
+ logits = self.score(hidden_states)
+
+ if input_ids is not None:
+ batch_size = input_ids.shape[0]
+ else:
+ batch_size = inputs_embeds.shape[0]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ sequence_lengths = -1
+ else:
+ if input_ids is not None:
+ # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+ sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+ sequence_lengths = sequence_lengths % input_ids.shape[-1]
+ sequence_lengths = sequence_lengths.to(logits.device)
+ else:
+ sequence_lengths = -1
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+ if not return_dict:
+ output = (pooled_logits,) + model_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=model_outputs.past_key_values,
+ hidden_states=model_outputs.hidden_states,
+ attentions=model_outputs.attentions,
+ )
+
+
+@add_start_docstrings(
+ """
+ [`Phi3Model`] with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+ Named-Entity-Recognition (NER) tasks.
+ """,
+ PHI3_START_DOCSTRING,
+)
+# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with Mpt->Phi3,MPT->PHI3,self.transformer->self.model,transformer_outputs->model_outputs
+class Phi3ForTokenClassification(Phi3PreTrainedModel):
+ def __init__(self, config: Phi3Config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.model = Phi3Model(config)
+ if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
+ classifier_dropout = config.classifier_dropout
+ elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
+ classifier_dropout = config.hidden_dropout
+ else:
+ classifier_dropout = 0.1
+ self.dropout = nn.Dropout(classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING)
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TokenClassifierOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **deprecated_arguments,
+ ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ model_outputs = self.model(
+ input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = model_outputs[0]
+ hidden_states = self.dropout(hidden_states)
+ logits = self.classifier(hidden_states)
+
+ loss = None
+ if labels is not None:
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(logits.device)
+ batch_size, seq_length = labels.shape
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(
+ logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
+ )
+
+ if not return_dict:
+ output = (logits,) + model_outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=model_outputs.hidden_states,
+ attentions=model_outputs.attentions,
+ )