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328 lines
12 KiB
328 lines
12 KiB
4 days ago
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from collections.abc import Callable
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from typing import Optional
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import torch
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import torch.nn as nn
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from ...cache_utils import Cache, DynamicCache
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from ...masking_utils import create_causal_mask
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutputWithPast,
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)
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, logging
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from ..clip.modeling_clip import CLIPMLP
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from ..llama.modeling_llama import (
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LlamaAttention,
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LlamaForCausalLM,
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LlamaForSequenceClassification,
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LlamaForTokenClassification,
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LlamaModel,
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LlamaRotaryEmbedding,
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apply_rotary_pos_emb,
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eager_attention_forward, # copied from Llama
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)
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from .configuration_phi import PhiConfig
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "microsoft/phi-1"
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_CONFIG_FOR_DOC = "PhiConfig"
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class PhiRotaryEmbedding(LlamaRotaryEmbedding):
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@staticmethod
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def compute_default_rope_parameters(
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config: PhiConfig | None = None,
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device: Optional["torch.device"] = None,
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seq_len: int | None = None,
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) -> tuple["torch.Tensor", float]:
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"""
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Computes the inverse frequencies according to the original RoPE implementation
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Args:
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config ([`~transformers.PreTrainedConfig`]):
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The model configuration.
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device (`torch.device`):
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The device to use for initialization of the inverse frequencies.
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seq_len (`int`, *optional*):
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The current sequence length. Unused for this type of RoPE.
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Returns:
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Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
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post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
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"""
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base = config.rope_parameters["rope_theta"]
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partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0)
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head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
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dim = int(head_dim * partial_rotary_factor)
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attention_factor = 1.0 # Unused in this type of RoPE
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# Compute the inverse frequencies
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inv_freq = 1.0 / (
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base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
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)
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return inv_freq, attention_factor
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class PhiAttention(LlamaAttention):
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def __init__(self, config: PhiConfig, layer_idx: int):
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super().__init__(config, layer_idx)
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self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
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self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
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self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
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self.dense = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=True)
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del self.o_proj
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self.rotary_ndims = int(self.head_dim * config.rope_parameters["partial_rotary_factor"])
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self.qk_layernorm = config.qk_layernorm
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if self.qk_layernorm:
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self.q_layernorm = nn.LayerNorm(
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config.hidden_size // config.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=True
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)
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self.k_layernorm = nn.LayerNorm(
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config.hidden_size // config.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=True
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: torch.Tensor | None,
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past_key_values: Cache | None = None,
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cache_position: torch.LongTensor | None = None,
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**kwargs,
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) -> tuple[torch.Tensor, torch.Tensor | None]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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if self.qk_layernorm:
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query_states = self.q_layernorm(query_states)
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key_states = self.k_layernorm(key_states)
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cos, sin = position_embeddings
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# Partial rotary embedding
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query_rot, query_pass = (
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query_states[..., : self.rotary_ndims],
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query_states[..., self.rotary_ndims :],
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)
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key_rot, key_pass = (
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key_states[..., : self.rotary_ndims],
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key_states[..., self.rotary_ndims :],
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)
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# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
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query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin)
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# [batch_size, seq_length, num_heads, head_dim]
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query_states = torch.cat((query_rot, query_pass), dim=-1)
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key_states = torch.cat((key_rot, key_pass), dim=-1)
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if past_key_values is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.dense(attn_output)
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return attn_output, attn_weights
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class PhiMLP(CLIPMLP):
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pass
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class PhiDecoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: PhiConfig, layer_idx: int):
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super().__init__()
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self.self_attn = PhiAttention(config, layer_idx=layer_idx)
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self.mlp = PhiMLP(config)
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self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.resid_dropout = nn.Dropout(config.resid_pdrop)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor | None = None,
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position_ids: torch.LongTensor | None = None,
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past_key_values: Cache | None = None,
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output_attentions: bool | None = False,
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use_cache: bool | None = False,
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cache_position: torch.LongTensor | None = None,
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position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
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**kwargs,
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) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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# Self Attention
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attn_outputs, self_attn_weights = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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attn_outputs = self.resid_dropout(attn_outputs)
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feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
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hidden_states = attn_outputs + feed_forward_hidden_states + residual
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (self_attn_weights,)
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return outputs
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class PhiModel(LlamaModel):
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def __init__(self, config: PhiConfig):
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super().__init__(config)
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self.layers = nn.ModuleList(
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[PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
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)
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self.embed_dropout = nn.Dropout(config.embd_pdrop)
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self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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del self.norm
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def forward(
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self,
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input_ids: torch.LongTensor | None = None,
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attention_mask: torch.Tensor | None = None,
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position_ids: torch.LongTensor | None = None,
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past_key_values: Cache | None = None,
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inputs_embeds: torch.FloatTensor | None = None,
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use_cache: bool | None = None,
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output_attentions: bool | None = None,
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output_hidden_states: bool | None = None,
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cache_position: torch.LongTensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> BaseModelOutputWithPast:
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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if self.gradient_checkpointing and self.training and use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
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)
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use_cache = False
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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if use_cache and past_key_values is None:
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past_key_values = DynamicCache(config=self.config)
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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0)
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causal_mask = create_causal_mask(
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config=self.config,
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input_embeds=inputs_embeds,
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attention_mask=attention_mask,
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cache_position=cache_position,
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past_key_values=past_key_values,
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position_ids=position_ids,
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)
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inputs_embeds = self.embed_dropout(inputs_embeds) # diff with Llama
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hidden_states = inputs_embeds
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position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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for decoder_layer in self.layers[: self.config.num_hidden_layers]:
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=causal_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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hidden_states = layer_outputs[0]
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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hidden_states = self.final_layernorm(hidden_states) # diff with Llama
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=past_key_values if use_cache else None,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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class PhiForCausalLM(LlamaForCausalLM):
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def __init__(self, config):
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super().__init__(config)
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True)
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class PhiForSequenceClassification(LlamaForSequenceClassification):
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pass
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class PhiForTokenClassification(LlamaForTokenClassification):
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pass
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__all__ = [
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"PhiPreTrainedModel", # noqa: F822
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"PhiModel",
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"PhiForCausalLM",
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"PhiForSequenceClassification",
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"PhiForTokenClassification",
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]
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