Upload Moondream
Browse files- config.json +1 -1
- fourier_features.py +18 -0
- generation_config.json +1 -1
- model.safetensors +2 -2
- modeling_phi.py +548 -252
- moondream.py +57 -5
- region_model.py +43 -0
config.json
CHANGED
@@ -11,5 +11,5 @@
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"model_type": "phi"
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},
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"torch_dtype": "float16",
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-
"transformers_version": "4.
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}
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"model_type": "phi"
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},
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"torch_dtype": "float16",
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"transformers_version": "4.44.0"
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}
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fourier_features.py
ADDED
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# Adopted from https://github.com/crowsonkb/k-diffusion/blob/transformer-model-v2/k_diffusion/layers.py
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import torch
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import torch.nn as nn
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import math
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class FourierFeatures(nn.Module):
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def __init__(self, in_features, out_features, std=1.0):
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super().__init__()
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assert out_features % 2 == 0
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self.register_buffer(
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"weight", torch.randn([out_features // 2, in_features]) * std
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)
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def forward(self, input):
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f = 2 * math.pi * input @ self.weight.T
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return torch.cat([f.cos(), f.sin()], dim=-1)
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generation_config.json
CHANGED
@@ -2,5 +2,5 @@
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.
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}
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.44.0"
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}
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model.safetensors
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:4bf7aed8ba4325d23fa7cd348d795a27f3b272682536f08aca4cdd62cde79293
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size 3736040266
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modeling_phi.py
CHANGED
@@ -13,62 +13,113 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from transformers.activations import ACT2FN
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from transformers.cache_utils import Cache, DynamicCache
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from transformers.modeling_attn_mask_utils import
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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)
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import (
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is_flash_attn_2_available,
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is_flash_attn_greater_or_equal_2_10,
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logging,
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)
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from .configuration_moondream import PhiConfig
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from flash_attn import flash_attn_func, flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
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except ImportError:
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# Workaround for https://github.com/huggingface/transformers/issues/28459,
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# don't move to contextlib.suppress(ImportError)
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pass
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logger = logging.get_logger(__name__)
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# Copied from transformers.models.
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class PhiRotaryEmbedding(nn.Module):
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
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super().__init__()
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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inv_freq = 1.0 / (
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self.base
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)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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t = torch.arange(
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self.max_seq_len_cached, device=device, dtype=
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)
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freqs = torch.outer(t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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)
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# Copied from transformers.models.
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class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
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"""PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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t = torch.arange(
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self.max_seq_len_cached, device=device, dtype=
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)
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t = t / self.scaling_factor
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freqs = torch.outer(t, self.inv_freq)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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# Copied from transformers.models.
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class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
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"""PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
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- (self.scaling_factor - 1)
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) ** (self.dim / (self.dim - 2))
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inv_freq = 1.0 / (
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base
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)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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t = torch.arange(
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self.max_seq_len_cached, device=device, dtype=
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)
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freqs = torch.outer(t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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return torch.cat((-x2, x1), dim=-1)
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# Copied from transformers.models.
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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self.layer_idx = layer_idx
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if layer_idx is None:
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logger.warning_once(
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f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
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"to errors during the forward call
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"when creating this class."
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)
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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"sin": sin,
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"cos": cos,
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"partial_rotation_size": self.rotary_emb.dim,
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}
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key_states, value_states = past_key_value.update(
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key_states, value_states, self.layer_idx, cache_kwargs
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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# PhiFlashAttention2 attention does not support output_attentions
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"sin": sin,
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"cos": cos,
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"partial_rotation_size": self.rotary_emb.dim,
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}
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key_states, value_states = past_key_value.update(
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key_states, value_states, self.layer_idx, cache_kwargs
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key_states = key_states.to(target_dtype)
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value_states = value_states.to(target_dtype)
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attn_output =
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query_states,
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key_states,
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value_states,
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attention_mask,
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q_len,
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dropout=attn_dropout,
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softmax_scale=None,
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)
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
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return attn_output, attn_weights, past_key_value
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# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
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def _flash_attention_forward(
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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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query_length,
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dropout=0.0,
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softmax_scale=None,
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):
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"""
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Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
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first unpad the input, then computes the attention scores and pad the final attention scores.
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Args:
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query_states (`torch.Tensor`):
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Input query states to be passed to Flash Attention API
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key_states (`torch.Tensor`):
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Input key states to be passed to Flash Attention API
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value_states (`torch.Tensor`):
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Input value states to be passed to Flash Attention API
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attention_mask (`torch.Tensor`):
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The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
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position of padding tokens and 1 for the position of non-padding tokens.
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dropout (`int`, *optional*):
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Attention dropout
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softmax_scale (`float`, *optional*):
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The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
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"""
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if not self._flash_attn_uses_top_left_mask:
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causal = self.is_causal
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else:
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# 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__.
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causal = self.is_causal and query_length != 1
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value_states,
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indices_q,
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cu_seq_lens,
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max_seq_lens,
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) = self._upad_input(
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query_states, key_states, value_states, attention_mask, query_length
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cu_seqlens_q=cu_seqlens_q,
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cu_seqlens_k=cu_seqlens_k,
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max_seqlen_q=max_seqlen_in_batch_q,
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max_seqlen_k=max_seqlen_in_batch_k,
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dropout_p=dropout,
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softmax_scale=softmax_scale,
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causal=causal,
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causal=causal,
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):
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indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
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batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
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query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
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PHI_ATTENTION_CLASSES = {
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"eager": PhiAttention,
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"flash_attention_2": PhiFlashAttention2,
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}
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@@ -681,6 +785,8 @@ class PhiDecoderLayer(nn.Module):
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output_attentions: Optional[bool] = False,
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use_cache: Optional[bool] = False,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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) -> Tuple[
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torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
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]:
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If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
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(see `past_key_values`).
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past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
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"""
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residual = hidden_states
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@@ -714,6 +825,7 @@ class PhiDecoderLayer(nn.Module):
|
|
714 |
past_key_value=past_key_value,
|
715 |
output_attentions=output_attentions,
|
716 |
use_cache=use_cache,
|
|
|
717 |
)
|
718 |
attn_outputs = self.resid_dropout(attn_outputs)
|
719 |
|
@@ -730,6 +842,27 @@ class PhiDecoderLayer(nn.Module):
|
|
730 |
return outputs
|
731 |
|
732 |
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|
733 |
class PhiPreTrainedModel(PreTrainedModel):
|
734 |
config_class = PhiConfig
|
735 |
base_model_prefix = "model"
|
@@ -737,6 +870,7 @@ class PhiPreTrainedModel(PreTrainedModel):
|
|
737 |
_no_split_modules = ["PhiDecoderLayer"]
|
738 |
_skip_keys_device_placement = "past_key_values"
|
739 |
_supports_flash_attn_2 = True
|
|
|
740 |
_supports_cache_class = True
|
741 |
|
742 |
def _init_weights(self, module):
|
@@ -761,7 +895,84 @@ class Embedding(nn.Module):
|
|
761 |
def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
|
762 |
return self.wte(input_ids)
|
763 |
|
764 |
-
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|
765 |
class PhiModel(PhiPreTrainedModel):
|
766 |
"""
|
767 |
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
|
@@ -783,7 +994,9 @@ class PhiModel(PhiPreTrainedModel):
|
|
783 |
for layer_idx in range(config.num_hidden_layers)
|
784 |
]
|
785 |
)
|
|
|
786 |
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
|
|
787 |
|
788 |
self.gradient_checkpointing = False
|
789 |
# Initialize weights and apply final processing
|
@@ -795,6 +1008,7 @@ class PhiModel(PhiPreTrainedModel):
|
|
795 |
def set_input_embeddings(self, value):
|
796 |
self.embd.wte = value
|
797 |
|
|
|
798 |
def forward(
|
799 |
self,
|
800 |
input_ids: torch.LongTensor = None,
|
@@ -806,6 +1020,7 @@ class PhiModel(PhiPreTrainedModel):
|
|
806 |
output_attentions: Optional[bool] = None,
|
807 |
output_hidden_states: Optional[bool] = None,
|
808 |
return_dict: Optional[bool] = None,
|
|
|
809 |
) -> Union[Tuple, BaseModelOutputWithPast]:
|
810 |
output_attentions = (
|
811 |
output_attentions
|
@@ -823,19 +1038,10 @@ class PhiModel(PhiPreTrainedModel):
|
|
823 |
return_dict if return_dict is not None else self.config.use_return_dict
|
824 |
)
|
825 |
|
826 |
-
|
827 |
-
if input_ids is not None and inputs_embeds is not None:
|
828 |
raise ValueError(
|
829 |
-
"You cannot specify both input_ids and inputs_embeds at the same time"
|
830 |
)
|
831 |
-
elif input_ids is not None:
|
832 |
-
batch_size, seq_length = input_ids.shape[:2]
|
833 |
-
elif inputs_embeds is not None:
|
834 |
-
batch_size, seq_length = inputs_embeds.shape[:2]
|
835 |
-
else:
|
836 |
-
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
837 |
-
|
838 |
-
past_key_values_length = 0
|
839 |
|
840 |
if self.gradient_checkpointing and self.training:
|
841 |
if use_cache:
|
@@ -844,43 +1050,37 @@ class PhiModel(PhiPreTrainedModel):
|
|
844 |
)
|
845 |
use_cache = False
|
846 |
|
847 |
-
|
848 |
-
|
849 |
-
|
850 |
-
|
851 |
-
|
852 |
-
|
853 |
-
|
854 |
-
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
855 |
-
position_ids = torch.arange(
|
856 |
-
past_key_values_length,
|
857 |
-
seq_length + past_key_values_length,
|
858 |
-
dtype=torch.long,
|
859 |
-
device=device,
|
860 |
)
|
861 |
-
position_ids = position_ids.unsqueeze(0)
|
862 |
|
863 |
if inputs_embeds is None:
|
864 |
inputs_embeds = self.embd(input_ids)
|
865 |
|
866 |
-
|
867 |
-
|
868 |
-
|
869 |
-
if self._use_flash_attention_2:
|
870 |
-
# 2d mask is passed through the layers
|
871 |
-
attention_mask = (
|
872 |
-
attention_mask
|
873 |
-
if (attention_mask is not None and 0 in attention_mask)
|
874 |
-
else None
|
875 |
)
|
876 |
-
|
877 |
-
|
878 |
-
|
879 |
-
|
880 |
-
(batch_size, seq_length),
|
881 |
-
inputs_embeds,
|
882 |
-
past_key_values_length,
|
883 |
)
|
|
|
|
|
|
|
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|
|
|
|
|
|
884 |
|
885 |
hidden_states = inputs_embeds
|
886 |
|
@@ -897,19 +1097,22 @@ class PhiModel(PhiPreTrainedModel):
|
|
897 |
layer_outputs = self._gradient_checkpointing_func(
|
898 |
decoder_layer.__call__,
|
899 |
hidden_states,
|
900 |
-
|
901 |
position_ids,
|
902 |
-
past_key_values,
|
903 |
output_attentions,
|
|
|
|
|
|
|
904 |
)
|
905 |
else:
|
906 |
layer_outputs = decoder_layer(
|
907 |
hidden_states,
|
908 |
-
attention_mask=
|
909 |
position_ids=position_ids,
|
910 |
past_key_value=past_key_values,
|
911 |
output_attentions=output_attentions,
|
912 |
use_cache=use_cache,
|
|
|
913 |
)
|
914 |
|
915 |
hidden_states = layer_outputs[0]
|
@@ -944,6 +1147,86 @@ class PhiModel(PhiPreTrainedModel):
|
|
944 |
attentions=all_self_attns,
|
945 |
)
|
946 |
|
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|
947 |
|
948 |
class CausalLMHead(nn.Module):
|
949 |
"""Causal Language Modeling head. Simplified version."""
|
@@ -958,7 +1241,6 @@ class CausalLMHead(nn.Module):
|
|
958 |
|
959 |
|
960 |
class PhiForCausalLM(PhiPreTrainedModel):
|
961 |
-
_tied_weights_keys = ["lm_head.linear.weight"]
|
962 |
|
963 |
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
|
964 |
def __init__(self, config):
|
@@ -976,7 +1258,7 @@ class PhiForCausalLM(PhiPreTrainedModel):
|
|
976 |
|
977 |
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
|
978 |
def set_input_embeddings(self, value):
|
979 |
-
self.
|
980 |
|
981 |
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
|
982 |
def get_output_embeddings(self):
|
@@ -994,6 +1276,10 @@ class PhiForCausalLM(PhiPreTrainedModel):
|
|
994 |
def get_decoder(self):
|
995 |
return self.model
|
996 |
|
|
|
|
|
|
|
|
|
997 |
def forward(
|
998 |
self,
|
999 |
input_ids: torch.LongTensor = None,
|
@@ -1006,6 +1292,8 @@ class PhiForCausalLM(PhiPreTrainedModel):
|
|
1006 |
output_attentions: Optional[bool] = None,
|
1007 |
output_hidden_states: Optional[bool] = None,
|
1008 |
return_dict: Optional[bool] = None,
|
|
|
|
|
1009 |
) -> Union[Tuple, CausalLMOutputWithPast]:
|
1010 |
r"""
|
1011 |
Args:
|
@@ -1014,6 +1302,11 @@ class PhiForCausalLM(PhiPreTrainedModel):
|
|
1014 |
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
1015 |
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
1016 |
|
|
|
|
|
|
|
|
|
|
|
1017 |
Returns:
|
1018 |
|
1019 |
Example:
|
@@ -1058,13 +1351,16 @@ class PhiForCausalLM(PhiPreTrainedModel):
|
|
1058 |
output_attentions=output_attentions,
|
1059 |
output_hidden_states=output_hidden_states,
|
1060 |
return_dict=return_dict,
|
|
|
1061 |
)
|
1062 |
|
1063 |
hidden_states = outputs[0]
|
1064 |
-
logits = self.lm_head(hidden_states)
|
1065 |
|
1066 |
loss = None
|
1067 |
if labels is not None:
|
|
|
|
|
1068 |
# Shift so that tokens < n predict n
|
1069 |
shift_logits = logits[..., :-1, :].contiguous()
|
1070 |
shift_labels = labels[..., 1:].contiguous()
|
@@ -1095,41 +1391,23 @@ class PhiForCausalLM(PhiPreTrainedModel):
|
|
1095 |
past_key_values=None,
|
1096 |
attention_mask=None,
|
1097 |
inputs_embeds=None,
|
|
|
|
|
|
|
|
|
1098 |
**kwargs,
|
1099 |
):
|
|
|
|
|
|
|
1100 |
if past_key_values is not None:
|
1101 |
-
if
|
1102 |
-
|
1103 |
-
|
1104 |
-
|
1105 |
-
else
|
1106 |
-
|
1107 |
-
max_cache_length = None
|
1108 |
-
|
1109 |
-
# Keep only the unprocessed tokens:
|
1110 |
-
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
1111 |
-
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
|
1112 |
-
# input)
|
1113 |
-
if (
|
1114 |
-
attention_mask is not None
|
1115 |
-
and attention_mask.shape[1] > input_ids.shape[1]
|
1116 |
-
):
|
1117 |
-
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
1118 |
-
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
1119 |
-
# input_ids based on the past_length.
|
1120 |
-
elif past_length < input_ids.shape[1]:
|
1121 |
-
input_ids = input_ids[:, past_length:]
|
1122 |
-
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
1123 |
-
|
1124 |
-
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
1125 |
-
if (
|
1126 |
-
max_cache_length is not None
|
1127 |
-
and attention_mask is not None
|
1128 |
-
and cache_length + input_ids.shape[1] > max_cache_length
|
1129 |
-
):
|
1130 |
-
attention_mask = attention_mask[:, -max_cache_length:]
|
1131 |
|
1132 |
-
position_ids = kwargs.get("position_ids", None)
|
1133 |
if attention_mask is not None and position_ids is None:
|
1134 |
# create position_ids on the fly for batch generation
|
1135 |
position_ids = attention_mask.long().cumsum(-1) - 1
|
@@ -1137,31 +1415,49 @@ class PhiForCausalLM(PhiPreTrainedModel):
|
|
1137 |
if past_key_values:
|
1138 |
position_ids = position_ids[:, -input_ids.shape[1] :]
|
1139 |
|
|
|
|
|
|
|
1140 |
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
1141 |
-
if inputs_embeds is not None and
|
1142 |
-
model_inputs = {"inputs_embeds": inputs_embeds}
|
1143 |
else:
|
1144 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1145 |
|
1146 |
model_inputs.update(
|
1147 |
{
|
1148 |
"position_ids": position_ids,
|
|
|
1149 |
"past_key_values": past_key_values,
|
1150 |
-
"use_cache":
|
1151 |
"attention_mask": attention_mask,
|
|
|
1152 |
}
|
1153 |
)
|
1154 |
return model_inputs
|
1155 |
-
|
1156 |
-
@staticmethod
|
1157 |
-
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
|
1158 |
-
def _reorder_cache(past_key_values, beam_idx):
|
1159 |
-
reordered_past = ()
|
1160 |
-
for layer_past in past_key_values:
|
1161 |
-
reordered_past += (
|
1162 |
-
tuple(
|
1163 |
-
past_state.index_select(0, beam_idx.to(past_state.device))
|
1164 |
-
for past_state in layer_past
|
1165 |
-
),
|
1166 |
-
)
|
1167 |
-
return reordered_past
|
|
|
13 |
# See the License for the specific language governing permissions and
|
14 |
# limitations under the License.
|
15 |
|
16 |
+
"""PyTorch Phi model."""
|
|
|
17 |
|
18 |
+
import math
|
19 |
from typing import List, Optional, Tuple, Union
|
20 |
|
21 |
import torch
|
|
|
22 |
import torch.utils.checkpoint
|
23 |
+
from packaging import version
|
24 |
from torch import nn
|
25 |
from torch.nn import CrossEntropyLoss
|
26 |
|
27 |
from transformers.activations import ACT2FN
|
28 |
+
from transformers.cache_utils import Cache, DynamicCache, StaticCache
|
29 |
+
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
|
30 |
from transformers.modeling_outputs import (
|
31 |
BaseModelOutputWithPast,
|
32 |
CausalLMOutputWithPast,
|
33 |
)
|
34 |
from transformers.modeling_utils import PreTrainedModel
|
35 |
from transformers.utils import (
|
36 |
+
add_start_docstrings,
|
37 |
+
add_start_docstrings_to_model_forward,
|
38 |
+
get_torch_version,
|
39 |
is_flash_attn_2_available,
|
40 |
is_flash_attn_greater_or_equal_2_10,
|
41 |
+
is_torchdynamo_compiling,
|
42 |
logging,
|
43 |
+
replace_return_docstrings,
|
44 |
)
|
45 |
from .configuration_moondream import PhiConfig
|
46 |
|
47 |
|
48 |
+
if is_flash_attn_2_available():
|
49 |
+
from transformers.modeling_flash_attention_utils import _flash_attention_forward
|
|
|
|
|
|
|
|
|
|
|
|
|
50 |
|
51 |
|
52 |
logger = logging.get_logger(__name__)
|
53 |
|
54 |
+
_CONFIG_FOR_DOC = "PhiConfig"
|
55 |
|
56 |
+
|
57 |
+
# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
|
58 |
+
def _prepare_4d_causal_attention_mask_with_cache_position(
|
59 |
+
attention_mask: torch.Tensor,
|
60 |
+
sequence_length: int,
|
61 |
+
target_length: int,
|
62 |
+
dtype: torch.dtype,
|
63 |
+
device: torch.device,
|
64 |
+
min_dtype: float,
|
65 |
+
cache_position: torch.Tensor,
|
66 |
+
batch_size: int,
|
67 |
+
):
|
68 |
+
"""
|
69 |
+
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
70 |
+
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
|
71 |
+
|
72 |
+
Args:
|
73 |
+
attention_mask (`torch.Tensor`):
|
74 |
+
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
|
75 |
+
sequence_length (`int`):
|
76 |
+
The sequence length being processed.
|
77 |
+
target_length (`int`):
|
78 |
+
The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
|
79 |
+
dtype (`torch.dtype`):
|
80 |
+
The dtype to use for the 4D attention mask.
|
81 |
+
device (`torch.device`):
|
82 |
+
The device to plcae the 4D attention mask on.
|
83 |
+
min_dtype (`float`):
|
84 |
+
The minimum value representable with the dtype `dtype`.
|
85 |
+
cache_position (`torch.Tensor`):
|
86 |
+
Indices depicting the position of the input sequence tokens in the sequence.
|
87 |
+
batch_size (`torch.Tensor`):
|
88 |
+
Batch size.
|
89 |
+
"""
|
90 |
+
if attention_mask is not None and attention_mask.dim() == 4:
|
91 |
+
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
|
92 |
+
causal_mask = attention_mask
|
93 |
+
else:
|
94 |
+
causal_mask = torch.full(
|
95 |
+
(sequence_length, target_length),
|
96 |
+
fill_value=min_dtype,
|
97 |
+
dtype=dtype,
|
98 |
+
device=device,
|
99 |
+
)
|
100 |
+
if sequence_length != 1:
|
101 |
+
causal_mask = torch.triu(causal_mask, diagonal=1)
|
102 |
+
causal_mask *= torch.arange(
|
103 |
+
target_length, device=device
|
104 |
+
) > cache_position.reshape(-1, 1)
|
105 |
+
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
106 |
+
if attention_mask is not None:
|
107 |
+
causal_mask = (
|
108 |
+
causal_mask.clone()
|
109 |
+
) # copy to contiguous memory for in-place edit
|
110 |
+
mask_length = attention_mask.shape[-1]
|
111 |
+
padding_mask = (
|
112 |
+
causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
|
113 |
+
)
|
114 |
+
padding_mask = padding_mask == 0
|
115 |
+
causal_mask[:, :, :, :mask_length] = causal_mask[
|
116 |
+
:, :, :, :mask_length
|
117 |
+
].masked_fill(padding_mask, min_dtype)
|
118 |
+
|
119 |
+
return causal_mask
|
120 |
|
121 |
|
122 |
+
# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Phi
|
123 |
class PhiRotaryEmbedding(nn.Module):
|
124 |
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
125 |
super().__init__()
|
|
|
128 |
self.max_position_embeddings = max_position_embeddings
|
129 |
self.base = base
|
130 |
inv_freq = 1.0 / (
|
131 |
+
self.base
|
132 |
+
** (
|
133 |
+
torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device)
|
134 |
+
/ self.dim
|
135 |
+
)
|
136 |
)
|
137 |
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
138 |
|
|
|
146 |
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
147 |
self.max_seq_len_cached = seq_len
|
148 |
t = torch.arange(
|
149 |
+
self.max_seq_len_cached, device=device, dtype=torch.int64
|
150 |
+
).type_as(self.inv_freq)
|
151 |
|
152 |
freqs = torch.outer(t, self.inv_freq)
|
153 |
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
|
|
166 |
)
|
167 |
|
168 |
|
169 |
+
# Copied from transformers.models.falcon.modeling_falcon.FalconLinearScalingRotaryEmbedding with Falcon->Phi
|
170 |
class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
|
171 |
"""PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
172 |
|
|
|
184 |
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
185 |
self.max_seq_len_cached = seq_len
|
186 |
t = torch.arange(
|
187 |
+
self.max_seq_len_cached, device=device, dtype=torch.int64
|
188 |
+
).type_as(self.inv_freq)
|
189 |
t = t / self.scaling_factor
|
190 |
|
191 |
freqs = torch.outer(t, self.inv_freq)
|
|
|
195 |
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
196 |
|
197 |
|
198 |
+
# Copied from transformers.models.falcon.modeling_falcon.FalconDynamicNTKScalingRotaryEmbedding with Falcon->Phi
|
199 |
class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
|
200 |
"""PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
201 |
|
|
|
219 |
- (self.scaling_factor - 1)
|
220 |
) ** (self.dim / (self.dim - 2))
|
221 |
inv_freq = 1.0 / (
|
222 |
+
base
|
223 |
+
** (
|
224 |
+
torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device)
|
225 |
+
/ self.dim
|
226 |
+
)
|
227 |
)
|
228 |
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
229 |
|
230 |
t = torch.arange(
|
231 |
+
self.max_seq_len_cached, device=device, dtype=torch.int64
|
232 |
+
).type_as(self.inv_freq)
|
233 |
|
234 |
freqs = torch.outer(t, self.inv_freq)
|
235 |
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
|
|
246 |
return torch.cat((-x2, x1), dim=-1)
|
247 |
|
248 |
|
249 |
+
# Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb
|
250 |
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
|
251 |
"""Applies Rotary Position Embedding to the query and key tensors.
|
252 |
|
|
|
315 |
self.layer_idx = layer_idx
|
316 |
if layer_idx is None:
|
317 |
logger.warning_once(
|
318 |
+
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
|
319 |
+
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
|
320 |
"when creating this class."
|
321 |
)
|
322 |
|
|
|
381 |
past_key_value: Optional[Cache] = None,
|
382 |
output_attentions: bool = False,
|
383 |
use_cache: bool = False,
|
384 |
+
cache_position: Optional[torch.LongTensor] = None,
|
385 |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
386 |
bsz, q_len, _ = hidden_states.size()
|
387 |
|
|
|
433 |
"sin": sin,
|
434 |
"cos": cos,
|
435 |
"partial_rotation_size": self.rotary_emb.dim,
|
436 |
+
"cache_position": cache_position,
|
437 |
}
|
438 |
key_states, value_states = past_key_value.update(
|
439 |
key_states, value_states, self.layer_idx, cache_kwargs
|
|
|
442 |
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
443 |
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
444 |
|
445 |
+
# Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
|
446 |
+
attn_weights = torch.matmul(
|
447 |
+
query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
|
448 |
+
) / math.sqrt(self.head_dim)
|
449 |
+
|
450 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
451 |
+
raise ValueError(
|
452 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
453 |
+
f" {attn_weights.size()}"
|
454 |
+
)
|
455 |
+
|
456 |
+
if attention_mask is not None:
|
457 |
+
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
458 |
+
attn_weights += causal_mask
|
459 |
+
|
460 |
+
# upcast attention to fp32
|
461 |
+
attn_weights = nn.functional.softmax(
|
462 |
+
attn_weights, dim=-1, dtype=torch.float32
|
463 |
+
).to(value_states.dtype)
|
464 |
+
attn_weights = nn.functional.dropout(
|
465 |
+
attn_weights, p=self.attention_dropout, training=self.training
|
466 |
)
|
467 |
|
468 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
469 |
+
|
470 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
471 |
+
raise ValueError(
|
472 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
473 |
+
f" {attn_output.size()}"
|
474 |
+
)
|
475 |
+
|
476 |
attn_output = attn_output.transpose(1, 2).contiguous()
|
477 |
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
478 |
|
|
|
508 |
past_key_value: Optional[Cache] = None,
|
509 |
output_attentions: bool = False,
|
510 |
use_cache: bool = False,
|
511 |
+
cache_position: Optional[torch.LongTensor] = None,
|
512 |
**kwargs,
|
513 |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
514 |
# PhiFlashAttention2 attention does not support output_attentions
|
|
|
562 |
"sin": sin,
|
563 |
"cos": cos,
|
564 |
"partial_rotation_size": self.rotary_emb.dim,
|
565 |
+
"cache_position": cache_position,
|
566 |
}
|
567 |
key_states, value_states = past_key_value.update(
|
568 |
key_states, value_states, self.layer_idx, cache_kwargs
|
|
|
601 |
key_states = key_states.to(target_dtype)
|
602 |
value_states = value_states.to(target_dtype)
|
603 |
|
604 |
+
attn_output = _flash_attention_forward(
|
605 |
query_states,
|
606 |
key_states,
|
607 |
value_states,
|
608 |
attention_mask,
|
609 |
q_len,
|
610 |
+
position_ids=position_ids,
|
611 |
dropout=attn_dropout,
|
612 |
softmax_scale=None,
|
613 |
+
use_top_left_mask=self._flash_attn_uses_top_left_mask,
|
614 |
+
is_causal=self.is_causal,
|
615 |
)
|
616 |
|
617 |
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
|
|
622 |
|
623 |
return attn_output, attn_weights, past_key_value
|
624 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
625 |
|
626 |
+
class PhiSdpaAttention(PhiAttention):
|
627 |
+
def __init__(self, *args, **kwargs):
|
628 |
+
super().__init__(*args, **kwargs)
|
629 |
+
self.require_contiguous_qkv = version.parse(
|
630 |
+
get_torch_version()
|
631 |
+
) < version.parse("2.2.0")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
632 |
|
633 |
+
"""
|
634 |
+
SDPA attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
635 |
+
`PhiAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
636 |
+
SDPA API.
|
637 |
+
"""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
638 |
|
639 |
+
# Adapted from PhiAttention.forward
|
640 |
+
def forward(
|
641 |
+
self,
|
642 |
+
hidden_states: torch.Tensor,
|
643 |
+
attention_mask: Optional[torch.Tensor] = None,
|
644 |
+
position_ids: Optional[torch.LongTensor] = None,
|
645 |
+
past_key_value: Optional[Cache] = None,
|
646 |
+
output_attentions: bool = False,
|
647 |
+
use_cache: bool = False,
|
648 |
+
cache_position: Optional[torch.LongTensor] = None,
|
649 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
650 |
+
if output_attentions:
|
651 |
+
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
652 |
+
logger.warning_once(
|
653 |
+
"PhiModel is using PhiSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not "
|
654 |
+
"support `output_attentions=True`. Falling back to the manual attention implementation, but specifying "
|
655 |
+
"the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can "
|
656 |
+
'be removed using the argument `attn_implementation="eager"` when loading the model.'
|
657 |
)
|
658 |
+
return super().forward(
|
659 |
+
hidden_states=hidden_states,
|
660 |
+
attention_mask=attention_mask,
|
661 |
+
position_ids=position_ids,
|
662 |
+
past_key_value=past_key_value,
|
663 |
+
output_attentions=output_attentions,
|
664 |
+
use_cache=use_cache,
|
|
|
665 |
)
|
666 |
|
667 |
+
bsz, q_len, _ = hidden_states.size()
|
668 |
|
669 |
+
query_states, key_states, value_states = self.Wqkv(hidden_states).chunk(
|
670 |
+
3, dim=-1
|
671 |
+
)
|
|
|
|
|
|
|
672 |
|
673 |
+
query_states = query_states.view(
|
674 |
+
bsz, q_len, self.num_heads, self.head_dim
|
675 |
+
).transpose(1, 2)
|
676 |
+
key_states = key_states.view(
|
677 |
+
bsz, q_len, self.num_key_value_heads, self.head_dim
|
678 |
+
).transpose(1, 2)
|
679 |
+
value_states = value_states.view(
|
680 |
+
bsz, q_len, self.num_key_value_heads, self.head_dim
|
681 |
+
).transpose(1, 2)
|
682 |
+
|
683 |
+
kv_seq_len = key_states.shape[-2]
|
684 |
+
if past_key_value is not None:
|
685 |
+
if self.layer_idx is None:
|
686 |
+
raise ValueError(
|
687 |
+
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
688 |
+
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
689 |
+
"with a layer index."
|
690 |
+
)
|
691 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
692 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
693 |
+
|
694 |
+
# Partial rotary embedding
|
695 |
+
query_rot, query_pass = (
|
696 |
+
query_states[..., : self.rotary_emb.dim],
|
697 |
+
query_states[..., self.rotary_emb.dim :],
|
698 |
)
|
699 |
+
key_rot, key_pass = (
|
700 |
+
key_states[..., : self.rotary_emb.dim],
|
701 |
+
key_states[..., self.rotary_emb.dim :],
|
702 |
)
|
703 |
+
# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
|
704 |
+
query_rot, key_rot = apply_rotary_pos_emb(
|
705 |
+
query_rot, key_rot, cos, sin, position_ids
|
706 |
+
)
|
707 |
+
|
708 |
+
# [batch_size, seq_length, num_heads, head_dim]
|
709 |
+
query_states = torch.cat((query_rot, query_pass), dim=-1)
|
710 |
+
key_states = torch.cat((key_rot, key_pass), dim=-1)
|
711 |
+
|
712 |
+
if past_key_value is not None:
|
713 |
+
cache_kwargs = {
|
714 |
+
"sin": sin,
|
715 |
+
"cos": cos,
|
716 |
+
"partial_rotation_size": self.rotary_emb.dim,
|
717 |
+
"cache_position": cache_position,
|
718 |
+
}
|
719 |
+
key_states, value_states = past_key_value.update(
|
720 |
+
key_states, value_states, self.layer_idx, cache_kwargs
|
|
|
|
|
721 |
)
|
722 |
|
723 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
724 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
725 |
+
|
726 |
+
causal_mask = attention_mask
|
727 |
+
if attention_mask is not None:
|
728 |
+
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
|
729 |
+
|
730 |
+
# SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom
|
731 |
+
# attn_mask, so we need to call `.contiguous()` here. This was fixed in torch==2.2.0.
|
732 |
+
# Reference: https://github.com/pytorch/pytorch/issues/112577
|
733 |
+
if (
|
734 |
+
self.require_contiguous_qkv
|
735 |
+
and query_states.device.type == "cuda"
|
736 |
+
and attention_mask is not None
|
737 |
+
):
|
738 |
+
query_states = query_states.contiguous()
|
739 |
+
key_states = key_states.contiguous()
|
740 |
+
value_states = value_states.contiguous()
|
741 |
+
|
742 |
+
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
|
743 |
+
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
|
744 |
+
is_causal = True if causal_mask is None and q_len > 1 else False
|
745 |
+
|
746 |
+
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
747 |
+
query_states,
|
748 |
+
key_states,
|
749 |
+
value_states,
|
750 |
+
attn_mask=causal_mask,
|
751 |
+
dropout_p=self.attention_dropout if self.training else 0.0,
|
752 |
+
is_causal=is_causal,
|
753 |
)
|
754 |
|
755 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
756 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
757 |
+
|
758 |
+
attn_output = self.out_proj(attn_output)
|
759 |
+
|
760 |
+
return attn_output, None, past_key_value
|
761 |
+
|
762 |
|
763 |
PHI_ATTENTION_CLASSES = {
|
764 |
"eager": PhiAttention,
|
765 |
"flash_attention_2": PhiFlashAttention2,
|
766 |
+
"sdpa": PhiSdpaAttention,
|
767 |
}
|
768 |
|
769 |
|
|
|
785 |
output_attentions: Optional[bool] = False,
|
786 |
use_cache: Optional[bool] = False,
|
787 |
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
788 |
+
cache_position: Optional[torch.LongTensor] = None,
|
789 |
+
**kwargs,
|
790 |
) -> Tuple[
|
791 |
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
|
792 |
]:
|
|
|
806 |
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
807 |
(see `past_key_values`).
|
808 |
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
809 |
+
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
810 |
+
Indices depicting the position of the input sequence tokens in the sequence
|
811 |
+
kwargs (`dict`, *optional*):
|
812 |
+
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
|
813 |
+
into the model
|
814 |
"""
|
815 |
|
816 |
residual = hidden_states
|
|
|
825 |
past_key_value=past_key_value,
|
826 |
output_attentions=output_attentions,
|
827 |
use_cache=use_cache,
|
828 |
+
cache_position=cache_position,
|
829 |
)
|
830 |
attn_outputs = self.resid_dropout(attn_outputs)
|
831 |
|
|
|
842 |
return outputs
|
843 |
|
844 |
|
845 |
+
PHI_START_DOCSTRING = r"""
|
846 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
847 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
848 |
+
etc.)
|
849 |
+
|
850 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
851 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
852 |
+
and behavior.
|
853 |
+
|
854 |
+
Parameters:
|
855 |
+
config ([`PhiConfig`]):
|
856 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
857 |
+
load the weights associated with the model, only the configuration. Check out the
|
858 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
859 |
+
"""
|
860 |
+
|
861 |
+
|
862 |
+
@add_start_docstrings(
|
863 |
+
"The bare Phi Model outputting raw hidden-states without any specific head on top.",
|
864 |
+
PHI_START_DOCSTRING,
|
865 |
+
)
|
866 |
class PhiPreTrainedModel(PreTrainedModel):
|
867 |
config_class = PhiConfig
|
868 |
base_model_prefix = "model"
|
|
|
870 |
_no_split_modules = ["PhiDecoderLayer"]
|
871 |
_skip_keys_device_placement = "past_key_values"
|
872 |
_supports_flash_attn_2 = True
|
873 |
+
_supports_sdpa = True
|
874 |
_supports_cache_class = True
|
875 |
|
876 |
def _init_weights(self, module):
|
|
|
895 |
def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
|
896 |
return self.wte(input_ids)
|
897 |
|
898 |
+
PHI_INPUTS_DOCSTRING = r"""
|
899 |
+
Args:
|
900 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
901 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
902 |
+
it.
|
903 |
+
|
904 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
905 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
906 |
+
|
907 |
+
[What are input IDs?](../glossary#input-ids)
|
908 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
909 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
910 |
+
|
911 |
+
- 1 for tokens that are **not masked**,
|
912 |
+
- 0 for tokens that are **masked**.
|
913 |
+
|
914 |
+
[What are attention masks?](../glossary#attention-mask)
|
915 |
+
|
916 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
917 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
918 |
+
|
919 |
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
920 |
+
`past_key_values`).
|
921 |
+
|
922 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
923 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
924 |
+
information on the default strategy.
|
925 |
+
|
926 |
+
- 1 indicates the head is **not masked**,
|
927 |
+
- 0 indicates the head is **masked**.
|
928 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
929 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
930 |
+
config.n_positions - 1]`.
|
931 |
+
|
932 |
+
[What are position IDs?](../glossary#position-ids)
|
933 |
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
934 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
935 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
936 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
937 |
+
|
938 |
+
Two formats are allowed:
|
939 |
+
- a [`~cache_utils.Cache`] instance;
|
940 |
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
941 |
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
942 |
+
cache format.
|
943 |
+
|
944 |
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
945 |
+
legacy cache format will be returned.
|
946 |
+
|
947 |
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
948 |
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
949 |
+
of shape `(batch_size, sequence_length)`.
|
950 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
951 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
952 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
953 |
+
model's internal embedding lookup matrix.
|
954 |
+
use_cache (`bool`, *optional*):
|
955 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
956 |
+
`past_key_values`).
|
957 |
+
output_attentions (`bool`, *optional*):
|
958 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
959 |
+
tensors for more detail.
|
960 |
+
output_hidden_states (`bool`, *optional*):
|
961 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
962 |
+
more detail.
|
963 |
+
return_dict (`bool`, *optional*):
|
964 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
965 |
+
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
966 |
+
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
|
967 |
+
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
|
968 |
+
the complete sequence length.
|
969 |
+
"""
|
970 |
+
|
971 |
+
|
972 |
+
@add_start_docstrings(
|
973 |
+
"The bare Phi Model outputting raw hidden-states without any specific head on top.",
|
974 |
+
PHI_START_DOCSTRING,
|
975 |
+
)
|
976 |
class PhiModel(PhiPreTrainedModel):
|
977 |
"""
|
978 |
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
|
|
|
994 |
for layer_idx in range(config.num_hidden_layers)
|
995 |
]
|
996 |
)
|
997 |
+
|
998 |
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
999 |
+
self._use_sdpa = config._attn_implementation == "sdpa"
|
1000 |
|
1001 |
self.gradient_checkpointing = False
|
1002 |
# Initialize weights and apply final processing
|
|
|
1008 |
def set_input_embeddings(self, value):
|
1009 |
self.embd.wte = value
|
1010 |
|
1011 |
+
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
|
1012 |
def forward(
|
1013 |
self,
|
1014 |
input_ids: torch.LongTensor = None,
|
|
|
1020 |
output_attentions: Optional[bool] = None,
|
1021 |
output_hidden_states: Optional[bool] = None,
|
1022 |
return_dict: Optional[bool] = None,
|
1023 |
+
cache_position: Optional[torch.LongTensor] = None,
|
1024 |
) -> Union[Tuple, BaseModelOutputWithPast]:
|
1025 |
output_attentions = (
|
1026 |
output_attentions
|
|
|
1038 |
return_dict if return_dict is not None else self.config.use_return_dict
|
1039 |
)
|
1040 |
|
1041 |
+
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
|
1042 |
raise ValueError(
|
1043 |
+
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
|
1044 |
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1045 |
|
1046 |
if self.gradient_checkpointing and self.training:
|
1047 |
if use_cache:
|
|
|
1050 |
)
|
1051 |
use_cache = False
|
1052 |
|
1053 |
+
use_legacy_cache = False
|
1054 |
+
if use_cache and not isinstance(past_key_values, Cache) and not self.training:
|
1055 |
+
use_legacy_cache = True
|
1056 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
1057 |
+
logger.warning_once(
|
1058 |
+
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
|
1059 |
+
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/internal/generation_utils#transformers.Cache)"
|
|
|
|
|
|
|
|
|
|
|
|
|
1060 |
)
|
|
|
1061 |
|
1062 |
if inputs_embeds is None:
|
1063 |
inputs_embeds = self.embd(input_ids)
|
1064 |
|
1065 |
+
if cache_position is None:
|
1066 |
+
past_seen_tokens = (
|
1067 |
+
past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
|
|
|
|
|
|
|
|
|
|
|
1068 |
)
|
1069 |
+
cache_position = torch.arange(
|
1070 |
+
past_seen_tokens,
|
1071 |
+
past_seen_tokens + inputs_embeds.shape[1],
|
1072 |
+
device=inputs_embeds.device,
|
|
|
|
|
|
|
1073 |
)
|
1074 |
+
if position_ids is None:
|
1075 |
+
position_ids = cache_position.unsqueeze(0)
|
1076 |
+
|
1077 |
+
causal_mask = self._update_causal_mask(
|
1078 |
+
attention_mask,
|
1079 |
+
inputs_embeds,
|
1080 |
+
cache_position,
|
1081 |
+
past_key_values,
|
1082 |
+
output_attentions,
|
1083 |
+
)
|
1084 |
|
1085 |
hidden_states = inputs_embeds
|
1086 |
|
|
|
1097 |
layer_outputs = self._gradient_checkpointing_func(
|
1098 |
decoder_layer.__call__,
|
1099 |
hidden_states,
|
1100 |
+
causal_mask,
|
1101 |
position_ids,
|
|
|
1102 |
output_attentions,
|
1103 |
+
use_cache,
|
1104 |
+
past_key_values,
|
1105 |
+
cache_position,
|
1106 |
)
|
1107 |
else:
|
1108 |
layer_outputs = decoder_layer(
|
1109 |
hidden_states,
|
1110 |
+
attention_mask=causal_mask,
|
1111 |
position_ids=position_ids,
|
1112 |
past_key_value=past_key_values,
|
1113 |
output_attentions=output_attentions,
|
1114 |
use_cache=use_cache,
|
1115 |
+
cache_position=cache_position,
|
1116 |
)
|
1117 |
|
1118 |
hidden_states = layer_outputs[0]
|
|
|
1147 |
attentions=all_self_attns,
|
1148 |
)
|
1149 |
|
1150 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
|
1151 |
+
def _update_causal_mask(
|
1152 |
+
self,
|
1153 |
+
attention_mask: torch.Tensor,
|
1154 |
+
input_tensor: torch.Tensor,
|
1155 |
+
cache_position: torch.Tensor,
|
1156 |
+
past_key_values: Cache,
|
1157 |
+
output_attentions: bool,
|
1158 |
+
):
|
1159 |
+
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
|
1160 |
+
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
|
1161 |
+
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
|
1162 |
+
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
|
1163 |
+
|
1164 |
+
if self.config._attn_implementation == "flash_attention_2":
|
1165 |
+
if attention_mask is not None and 0.0 in attention_mask:
|
1166 |
+
return attention_mask
|
1167 |
+
return None
|
1168 |
+
|
1169 |
+
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
1170 |
+
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
1171 |
+
# to infer the attention mask.
|
1172 |
+
past_seen_tokens = (
|
1173 |
+
past_key_values.get_seq_length() if past_key_values is not None else 0
|
1174 |
+
)
|
1175 |
+
using_static_cache = isinstance(past_key_values, StaticCache)
|
1176 |
+
|
1177 |
+
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
1178 |
+
if (
|
1179 |
+
self.config._attn_implementation == "sdpa"
|
1180 |
+
and not using_static_cache
|
1181 |
+
and not output_attentions
|
1182 |
+
):
|
1183 |
+
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
1184 |
+
attention_mask,
|
1185 |
+
inputs_embeds=input_tensor,
|
1186 |
+
past_key_values_length=past_seen_tokens,
|
1187 |
+
is_training=self.training,
|
1188 |
+
):
|
1189 |
+
return None
|
1190 |
+
|
1191 |
+
dtype, device = input_tensor.dtype, input_tensor.device
|
1192 |
+
min_dtype = torch.finfo(dtype).min
|
1193 |
+
sequence_length = input_tensor.shape[1]
|
1194 |
+
if using_static_cache:
|
1195 |
+
target_length = past_key_values.get_max_length()
|
1196 |
+
else:
|
1197 |
+
target_length = (
|
1198 |
+
attention_mask.shape[-1]
|
1199 |
+
if isinstance(attention_mask, torch.Tensor)
|
1200 |
+
else past_seen_tokens + sequence_length + 1
|
1201 |
+
)
|
1202 |
+
|
1203 |
+
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
1204 |
+
causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
|
1205 |
+
attention_mask,
|
1206 |
+
sequence_length=sequence_length,
|
1207 |
+
target_length=target_length,
|
1208 |
+
dtype=dtype,
|
1209 |
+
device=device,
|
1210 |
+
min_dtype=min_dtype,
|
1211 |
+
cache_position=cache_position,
|
1212 |
+
batch_size=input_tensor.shape[0],
|
1213 |
+
)
|
1214 |
+
|
1215 |
+
if (
|
1216 |
+
self.config._attn_implementation == "sdpa"
|
1217 |
+
and attention_mask is not None
|
1218 |
+
and attention_mask.device.type == "cuda"
|
1219 |
+
and not output_attentions
|
1220 |
+
):
|
1221 |
+
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
1222 |
+
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
1223 |
+
# Details: https://github.com/pytorch/pytorch/issues/110213
|
1224 |
+
causal_mask = AttentionMaskConverter._unmask_unattended(
|
1225 |
+
causal_mask, min_dtype
|
1226 |
+
)
|
1227 |
+
|
1228 |
+
return causal_mask
|
1229 |
+
|
1230 |
|
1231 |
class CausalLMHead(nn.Module):
|
1232 |
"""Causal Language Modeling head. Simplified version."""
|
|
|
1241 |
|
1242 |
|
1243 |
class PhiForCausalLM(PhiPreTrainedModel):
|
|
|
1244 |
|
1245 |
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
|
1246 |
def __init__(self, config):
|
|
|
1258 |
|
1259 |
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
|
1260 |
def set_input_embeddings(self, value):
|
1261 |
+
self.transformer.embd.wte = value
|
1262 |
|
1263 |
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
|
1264 |
def get_output_embeddings(self):
|
|
|
1276 |
def get_decoder(self):
|
1277 |
return self.model
|
1278 |
|
1279 |
+
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
|
1280 |
+
@replace_return_docstrings(
|
1281 |
+
output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
|
1282 |
+
)
|
1283 |
def forward(
|
1284 |
self,
|
1285 |
input_ids: torch.LongTensor = None,
|
|
|
1292 |
output_attentions: Optional[bool] = None,
|
1293 |
output_hidden_states: Optional[bool] = None,
|
1294 |
return_dict: Optional[bool] = None,
|
1295 |
+
cache_position: Optional[torch.LongTensor] = None,
|
1296 |
+
num_logits_to_keep: int = 0,
|
1297 |
) -> Union[Tuple, CausalLMOutputWithPast]:
|
1298 |
r"""
|
1299 |
Args:
|
|
|
1302 |
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
1303 |
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
1304 |
|
1305 |
+
num_logits_to_keep (`int`, *optional*):
|
1306 |
+
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
|
1307 |
+
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
|
1308 |
+
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
|
1309 |
+
|
1310 |
Returns:
|
1311 |
|
1312 |
Example:
|
|
|
1351 |
output_attentions=output_attentions,
|
1352 |
output_hidden_states=output_hidden_states,
|
1353 |
return_dict=return_dict,
|
1354 |
+
cache_position=cache_position,
|
1355 |
)
|
1356 |
|
1357 |
hidden_states = outputs[0]
|
1358 |
+
logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
|
1359 |
|
1360 |
loss = None
|
1361 |
if labels is not None:
|
1362 |
+
# Upcast to float if we need to compute the loss to avoid potential precision issues
|
1363 |
+
logits = logits.float()
|
1364 |
# Shift so that tokens < n predict n
|
1365 |
shift_logits = logits[..., :-1, :].contiguous()
|
1366 |
shift_labels = labels[..., 1:].contiguous()
|
|
|
1391 |
past_key_values=None,
|
1392 |
attention_mask=None,
|
1393 |
inputs_embeds=None,
|
1394 |
+
cache_position=None,
|
1395 |
+
position_ids=None,
|
1396 |
+
use_cache=True,
|
1397 |
+
num_logits_to_keep=0,
|
1398 |
**kwargs,
|
1399 |
):
|
1400 |
+
# If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
|
1401 |
+
# Exception 1: when passing input_embeds, input_ids may be missing entries
|
1402 |
+
# Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
|
1403 |
if past_key_values is not None:
|
1404 |
+
if inputs_embeds is not None: # Exception 1
|
1405 |
+
input_ids = input_ids[:, -cache_position.shape[0] :]
|
1406 |
+
elif (
|
1407 |
+
input_ids.shape[1] != cache_position.shape[0]
|
1408 |
+
): # Default case (the "else", a no op, is Exception 2)
|
1409 |
+
input_ids = input_ids[:, cache_position]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1410 |
|
|
|
1411 |
if attention_mask is not None and position_ids is None:
|
1412 |
# create position_ids on the fly for batch generation
|
1413 |
position_ids = attention_mask.long().cumsum(-1) - 1
|
|
|
1415 |
if past_key_values:
|
1416 |
position_ids = position_ids[:, -input_ids.shape[1] :]
|
1417 |
|
1418 |
+
# This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
|
1419 |
+
position_ids = position_ids.clone(memory_format=torch.contiguous_format)
|
1420 |
+
|
1421 |
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
1422 |
+
if inputs_embeds is not None and cache_position[0] == 0:
|
1423 |
+
model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
|
1424 |
else:
|
1425 |
+
# The clone here is for the same reason as for `position_ids`.
|
1426 |
+
model_inputs = {
|
1427 |
+
"input_ids": input_ids.clone(memory_format=torch.contiguous_format),
|
1428 |
+
"inputs_embeds": None,
|
1429 |
+
}
|
1430 |
+
|
1431 |
+
if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
|
1432 |
+
if model_inputs["inputs_embeds"] is not None:
|
1433 |
+
batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
|
1434 |
+
device = model_inputs["inputs_embeds"].device
|
1435 |
+
else:
|
1436 |
+
batch_size, sequence_length = model_inputs["input_ids"].shape
|
1437 |
+
device = model_inputs["input_ids"].device
|
1438 |
+
|
1439 |
+
dtype = self.lm_head.weight.dtype
|
1440 |
+
min_dtype = torch.finfo(dtype).min
|
1441 |
+
|
1442 |
+
attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
|
1443 |
+
attention_mask,
|
1444 |
+
sequence_length=sequence_length,
|
1445 |
+
target_length=past_key_values.get_max_length(),
|
1446 |
+
dtype=dtype,
|
1447 |
+
device=device,
|
1448 |
+
min_dtype=min_dtype,
|
1449 |
+
cache_position=cache_position,
|
1450 |
+
batch_size=batch_size,
|
1451 |
+
)
|
1452 |
|
1453 |
model_inputs.update(
|
1454 |
{
|
1455 |
"position_ids": position_ids,
|
1456 |
+
"cache_position": cache_position,
|
1457 |
"past_key_values": past_key_values,
|
1458 |
+
"use_cache": use_cache,
|
1459 |
"attention_mask": attention_mask,
|
1460 |
+
"num_logits_to_keep": num_logits_to_keep,
|
1461 |
}
|
1462 |
)
|
1463 |
return model_inputs
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
moondream.py
CHANGED
@@ -1,10 +1,14 @@
|
|
1 |
import torch
|
2 |
-
|
3 |
-
from
|
4 |
from transformers import PreTrainedModel
|
|
|
5 |
|
6 |
-
from .modeling_phi import PhiForCausalLM
|
7 |
from .configuration_moondream import PhiConfig
|
|
|
|
|
|
|
|
|
8 |
|
9 |
class Moondream(PreTrainedModel):
|
10 |
config_class = MoondreamConfig
|
@@ -15,6 +19,7 @@ class Moondream(PreTrainedModel):
|
|
15 |
self.vision_encoder = VisionEncoder(
|
16 |
use_flash_attn=config._attn_implementation == "flash_attention_2"
|
17 |
)
|
|
|
18 |
|
19 |
if type(config.text_config) == dict:
|
20 |
phi_config = PhiConfig(
|
@@ -80,12 +85,55 @@ class Moondream(PreTrainedModel):
|
|
80 |
|
81 |
with torch.no_grad():
|
82 |
inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
|
|
|
83 |
output_ids = self.text_model.generate(
|
84 |
-
inputs_embeds=inputs_embeds,
|
|
|
|
|
85 |
)
|
86 |
|
87 |
return tokenizer.batch_decode(output_ids, skip_special_tokens=True)
|
88 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
89 |
def answer_question(
|
90 |
self,
|
91 |
image_embeds,
|
@@ -93,6 +141,7 @@ class Moondream(PreTrainedModel):
|
|
93 |
tokenizer,
|
94 |
chat_history="",
|
95 |
result_queue=None,
|
|
|
96 |
**kwargs,
|
97 |
):
|
98 |
prompt = f"<image>\n\n{chat_history}Question: {question}\n\nAnswer:"
|
@@ -100,7 +149,7 @@ class Moondream(PreTrainedModel):
|
|
100 |
image_embeds,
|
101 |
prompt,
|
102 |
tokenizer=tokenizer,
|
103 |
-
max_new_tokens=
|
104 |
**kwargs,
|
105 |
)[0]
|
106 |
cleaned_answer = answer.strip()
|
@@ -176,3 +225,6 @@ class Moondream(PreTrainedModel):
|
|
176 |
x.strip()
|
177 |
for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
|
178 |
]
|
|
|
|
|
|
|
|
1 |
import torch
|
2 |
+
|
3 |
+
from typing import List, Union, Literal, Optional
|
4 |
from transformers import PreTrainedModel
|
5 |
+
from PIL import Image
|
6 |
|
|
|
7 |
from .configuration_moondream import PhiConfig
|
8 |
+
from .configuration_moondream import MoondreamConfig
|
9 |
+
from .vision_encoder import VisionEncoder
|
10 |
+
from .region_model import RegionModel
|
11 |
+
from .modeling_phi import PhiForCausalLM
|
12 |
|
13 |
class Moondream(PreTrainedModel):
|
14 |
config_class = MoondreamConfig
|
|
|
19 |
self.vision_encoder = VisionEncoder(
|
20 |
use_flash_attn=config._attn_implementation == "flash_attention_2"
|
21 |
)
|
22 |
+
self.region_model = RegionModel()
|
23 |
|
24 |
if type(config.text_config) == dict:
|
25 |
phi_config = PhiConfig(
|
|
|
85 |
|
86 |
with torch.no_grad():
|
87 |
inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
|
88 |
+
attention_mask = torch.ones((inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device)
|
89 |
output_ids = self.text_model.generate(
|
90 |
+
inputs_embeds=inputs_embeds,
|
91 |
+
attention_mask=attention_mask,
|
92 |
+
**generate_config,
|
93 |
)
|
94 |
|
95 |
return tokenizer.batch_decode(output_ids, skip_special_tokens=True)
|
96 |
|
97 |
+
# Note: Not ready for use yet, intended for September release.
|
98 |
+
def caption(
|
99 |
+
self,
|
100 |
+
images: List[Image.Image],
|
101 |
+
tokenizer,
|
102 |
+
length: Optional[Literal["short"]] = None,
|
103 |
+
**kwargs,
|
104 |
+
):
|
105 |
+
image_embeds = self.encode_image(images)
|
106 |
+
|
107 |
+
templated_prompts = [
|
108 |
+
f"<image>\n\n{'Short caption' if length == 'short' else 'Caption'}:" for _ in images
|
109 |
+
]
|
110 |
+
inputs_embeds = torch.stack([
|
111 |
+
self.input_embeds(prompt, image_embed.unsqueeze(0), tokenizer)[0]
|
112 |
+
for prompt, image_embed in zip(templated_prompts, image_embeds)
|
113 |
+
])
|
114 |
+
attention_mask = torch.ones((inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device)
|
115 |
+
|
116 |
+
generate_config = {
|
117 |
+
"eos_token_id": tokenizer.eos_token_id,
|
118 |
+
"bos_token_id": tokenizer.bos_token_id,
|
119 |
+
"pad_token_id": tokenizer.bos_token_id,
|
120 |
+
"repetition_penalty": 1.2,
|
121 |
+
"max_new_tokens": 512,
|
122 |
+
**kwargs,
|
123 |
+
}
|
124 |
+
|
125 |
+
with torch.no_grad():
|
126 |
+
output_ids = self.text_model.generate(
|
127 |
+
inputs_embeds=inputs_embeds,
|
128 |
+
attention_mask=attention_mask,
|
129 |
+
**generate_config,
|
130 |
+
)
|
131 |
+
|
132 |
+
return [
|
133 |
+
x.strip()
|
134 |
+
for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
|
135 |
+
]
|
136 |
+
|
137 |
def answer_question(
|
138 |
self,
|
139 |
image_embeds,
|
|
|
141 |
tokenizer,
|
142 |
chat_history="",
|
143 |
result_queue=None,
|
144 |
+
max_new_tokens=256,
|
145 |
**kwargs,
|
146 |
):
|
147 |
prompt = f"<image>\n\n{chat_history}Question: {question}\n\nAnswer:"
|
|
|
149 |
image_embeds,
|
150 |
prompt,
|
151 |
tokenizer=tokenizer,
|
152 |
+
max_new_tokens=max_new_tokens,
|
153 |
**kwargs,
|
154 |
)[0]
|
155 |
cleaned_answer = answer.strip()
|
|
|
225 |
x.strip()
|
226 |
for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
|
227 |
]
|
228 |
+
|
229 |
+
def detect(self, image: Image.Image, query: str, tokenizer):
|
230 |
+
pass
|
region_model.py
ADDED
@@ -0,0 +1,43 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
from .fourier_features import FourierFeatures
|
4 |
+
|
5 |
+
class RegionModel(nn.Module):
|
6 |
+
def __init__(self):
|
7 |
+
super().__init__()
|
8 |
+
|
9 |
+
self.position_features = FourierFeatures(2, 256)
|
10 |
+
self.position_encoder = nn.Linear(256, 2048)
|
11 |
+
self.size_features = FourierFeatures(2, 256)
|
12 |
+
self.size_encoder = nn.Linear(256, 2048)
|
13 |
+
|
14 |
+
self.position_decoder = nn.Linear(2048, 2)
|
15 |
+
self.size_decoder = nn.Linear(2048, 2)
|
16 |
+
self.confidence_decoder = nn.Linear(2048, 1)
|
17 |
+
|
18 |
+
def encode_position(self, position):
|
19 |
+
return self.position_encoder(self.position_features(position))
|
20 |
+
|
21 |
+
def encode_size(self, size):
|
22 |
+
return self.size_encoder(self.size_features(size))
|
23 |
+
|
24 |
+
def decode_position(self, x):
|
25 |
+
return self.position_decoder(x)
|
26 |
+
|
27 |
+
def decode_size(self, x):
|
28 |
+
return self.size_decoder(x)
|
29 |
+
|
30 |
+
def decode_confidence(self, x):
|
31 |
+
return self.confidence_decoder(x)
|
32 |
+
|
33 |
+
def encode(self, position, size):
|
34 |
+
return torch.stack(
|
35 |
+
[self.encode_position(position), self.encode_size(size)], dim=0
|
36 |
+
)
|
37 |
+
|
38 |
+
def decode(self, position_logits, size_logits):
|
39 |
+
return (
|
40 |
+
self.decode_position(position_logits),
|
41 |
+
self.decode_size(size_logits),
|
42 |
+
self.decode_confidence(size_logits),
|
43 |
+
)
|