x54-729
commited on
Commit
•
2e503ec
1
Parent(s):
aafebc5
update modeling file to newest
Browse files- configuration_internlm2.py +42 -13
- modeling_internlm2.py +769 -352
configuration_internlm2.py
CHANGED
@@ -44,9 +44,9 @@ class InternLM2Config(PretrainedConfig):
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intermediate_size (`int`, *optional*, defaults to 11008):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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@@ -58,22 +58,42 @@ class InternLM2Config(PretrainedConfig):
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with.
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just in case (e.g., 512 or 1024 or 2048).
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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-
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Whether to tie weight embeddings
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"""
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-
model_type = "internlm2"
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_auto_class = "AutoConfig"
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def __init__( # pylint: disable=W0102
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self,
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@@ -91,11 +111,12 @@ class InternLM2Config(PretrainedConfig):
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=False,
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bias=True,
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rope_theta=10000,
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rope_scaling=None,
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-
attn_implementation=
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**kwargs,
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):
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self.vocab_size = vocab_size
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@@ -113,14 +134,15 @@ class InternLM2Config(PretrainedConfig):
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self._rope_scaling_validation()
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-
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self.attn_implementation = attn_implementation
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if self.attn_implementation is None:
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self.attn_implementation = "eager"
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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@@ -147,5 +169,12 @@ class InternLM2Config(PretrainedConfig):
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raise ValueError(
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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
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)
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if
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-
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intermediate_size (`int`, *optional*, defaults to 11008):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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+
Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. InternLM2 supports up to 32768 tokens.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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+
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 1):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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pretraining_tp (`int`, *optional*, defaults to 1):
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Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
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document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism)
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to understand more about it. This value is necessary to ensure exact reproducibility
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of the pretraining results. Please refer to [this
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issue](https://github.com/pytorch/pytorch/issues/76232).
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
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experimental feature, subject to breaking API changes in future versions.
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"""
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_auto_class = "AutoConfig"
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model_type = "internlm2"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__( # pylint: disable=W0102
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self,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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pretraining_tp=1,
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tie_word_embeddings=False,
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bias=True,
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rope_theta=10000,
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rope_scaling=None,
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attn_implementation=None,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.pretraining_tp = pretraining_tp
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self._rope_scaling_validation()
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self.attn_implementation = attn_implementation
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if self.attn_implementation is None:
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self.attn_implementation = "eager"
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+
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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raise ValueError(
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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
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)
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if (
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rope_scaling_factor is None
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or not isinstance(rope_scaling_factor, (float, int))
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or rope_scaling_factor < 1.0
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):
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raise ValueError(
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f"`rope_scaling`'s factor field must be a number >= 1, got {rope_scaling_factor} "
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f"of type {type(rope_scaling_factor)}"
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)
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modeling_internlm2.py
CHANGED
@@ -13,11 +13,10 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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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import math
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import queue
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import threading
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import warnings
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from typing import List, Optional, Tuple, Union
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import torch
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.activations import ACT2FN
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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SequenceClassifierOutputWithPast,
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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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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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try:
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from transformers.generation.streamers import BaseStreamer
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except
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BaseStreamer = None
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from .configuration_internlm2 import InternLM2Config
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "InternLM2Config"
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pad_input, index_first_axis, unpad_input = None, None, None
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def _import_flash_attn():
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global flash_attn_func, flash_attn_varlen_func
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global pad_input, index_first_axis, unpad_input
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try:
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from flash_attn import flash_attn_func as _flash_attn_func, flash_attn_varlen_func as _flash_attn_varlen_func
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from flash_attn.bert_padding import pad_input as _pad_input, index_first_axis as _index_first_axis, unpad_input as _unpad_input
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flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
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pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
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except ImportError:
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raise ImportError("flash_attn is not installed.")
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# Copied from transformers.models.llama.modeling_llama._get_unpad_data
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def _get_unpad_data(attention_mask):
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seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
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indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
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max_seqlen_in_batch = seqlens_in_batch.max().item()
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cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.
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return (
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indices,
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cu_seqlens,
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)
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# Copied from transformers.models.bart.modeling_bart._make_causal_mask
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def _make_causal_mask(
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input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
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):
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"""
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Make causal mask used for bi-directional self-attention.
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"""
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bsz, tgt_len = input_ids_shape
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mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
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mask_cond = torch.arange(mask.size(-1), device=device)
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mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
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mask = mask.to(dtype)
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if past_key_values_length > 0:
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mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
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return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
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# Copied from transformers.models.bart.modeling_bart._expand_mask
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def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
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"""
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Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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"""
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bsz, src_len = mask.size()
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tgt_len = tgt_len if tgt_len is not None else src_len
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expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
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inverted_mask = 1.0 - expanded_mask
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return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
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-
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# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
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class InternLM2RMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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InternLM2RMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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return self.weight * hidden_states.to(input_dtype)
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class InternLM2RotaryEmbedding(nn.Module):
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super().__init__()
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self.dim = dim
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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 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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-
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seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
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)
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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(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
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-
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freqs = torch.einsum("i,j->ij", 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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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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-
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def forward(self, x, seq_len=None):
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# x: [bs, num_attention_heads, seq_len, head_size]
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-
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)
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# Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
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class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
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"""InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
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def
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self.max_seq_len_cached = seq_len
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
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t = t / self.scaling_factor
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freqs = torch.einsum("i,j->ij", 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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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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-
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# Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
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class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
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"""InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
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Credits to the Reddit users /u/bloc97 and /u/emozilla
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"""
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
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self.scaling_factor = scaling_factor
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super().__init__(dim, max_position_embeddings, base, device)
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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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if seq_len > self.max_position_embeddings:
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base = self.base * (
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(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
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) ** (self.dim / (self.dim - 2))
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inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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# Copied from transformers.model.llama.modeling_llama.rotate_half
|
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def rotate_half(x):
|
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"""Rotates half the hidden dims of the input."""
|
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x1 = x[..., : x.shape[-1] // 2]
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return torch.cat((-x2, x1), dim=-1)
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#
|
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-
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-
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-
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
|
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return q_embed, k_embed
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class InternLM2MLP(nn.Module):
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def __init__(self, config):
|
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super().__init__()
|
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self.config = config
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@@ -249,7 +212,6 @@ class InternLM2MLP(nn.Module):
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return down_proj
|
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|
252 |
-
# Copied from transformers.model.llama.modeling_llama.repeat_kv
|
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
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"""
|
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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@@ -262,19 +224,27 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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|
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|
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-
# Modified from transformers.model.llama.modeling_llama.LlamaAttention
|
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class InternLM2Attention(nn.Module):
|
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
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|
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-
def __init__(self, config: InternLM2Config):
|
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super().__init__()
|
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self.config = config
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
|
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self.head_dim = self.hidden_size // self.num_heads
|
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self.num_key_value_heads = config.num_key_value_heads
|
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
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self.max_position_embeddings = config.max_position_embeddings
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self.is_causal = True
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|
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if (self.head_dim * self.num_heads) != self.hidden_size:
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@@ -288,8 +258,8 @@ class InternLM2Attention(nn.Module):
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(self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
|
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bias=config.bias,
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)
|
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-
|
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self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
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|
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self._init_rope()
|
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|
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def _init_rope(self):
|
@@ -297,51 +267,49 @@ class InternLM2Attention(nn.Module):
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self.rotary_emb = InternLM2RotaryEmbedding(
|
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self.head_dim,
|
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max_position_embeddings=self.max_position_embeddings,
|
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-
base=self.
|
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)
|
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else:
|
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scaling_type = self.config.rope_scaling["type"]
|
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scaling_factor = self.config.rope_scaling["factor"]
|
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-
if scaling_type == "
|
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-
self.rotary_emb =
|
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self.head_dim,
|
308 |
max_position_embeddings=self.max_position_embeddings,
|
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-
base=self.config.rope_theta,
|
310 |
scaling_factor=scaling_factor,
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|
311 |
)
|
312 |
-
elif scaling_type == "
|
313 |
-
self.rotary_emb =
|
314 |
self.head_dim,
|
315 |
max_position_embeddings=self.max_position_embeddings,
|
316 |
-
base=self.config.rope_theta,
|
317 |
scaling_factor=scaling_factor,
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|
318 |
)
|
319 |
else:
|
320 |
-
raise ValueError("
|
321 |
-
return self.rotary_emb
|
322 |
-
|
323 |
-
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
324 |
-
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
325 |
|
326 |
def forward(
|
327 |
self,
|
328 |
hidden_states: torch.Tensor,
|
329 |
attention_mask: Optional[torch.Tensor] = None,
|
330 |
position_ids: Optional[torch.LongTensor] = None,
|
331 |
-
past_key_value: Optional[
|
332 |
output_attentions: bool = False,
|
333 |
-
use_cache: bool = False,
|
334 |
-
|
335 |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
336 |
-
if "padding_mask" in kwargs:
|
337 |
-
warnings.warn(
|
338 |
-
"Passing `padding_mask` is deprecated and will be removed in v4.37. "
|
339 |
-
"Please make sure use `attention_mask` instead.`"
|
340 |
-
)
|
341 |
-
|
342 |
bsz, q_len, _ = hidden_states.size()
|
343 |
|
344 |
-
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345 |
|
346 |
qkv_states = rearrange(
|
347 |
qkv_states,
|
@@ -351,44 +319,26 @@ class InternLM2Attention(nn.Module):
|
|
351 |
)
|
352 |
|
353 |
query_states = qkv_states[..., : self.num_key_value_groups, :]
|
354 |
-
query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
|
355 |
-
key_states = qkv_states[..., -2, :]
|
356 |
-
value_states = qkv_states[..., -1, :]
|
357 |
|
358 |
-
|
359 |
-
key_states = key_states.transpose(1, 2)
|
360 |
-
value_states = value_states.transpose(1, 2)
|
361 |
-
|
362 |
-
kv_seq_len = key_states.shape[-2]
|
363 |
-
if past_key_value is not None:
|
364 |
-
kv_seq_len += past_key_value[0].shape[-2]
|
365 |
-
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
366 |
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
367 |
|
368 |
if past_key_value is not None:
|
369 |
-
#
|
370 |
-
|
371 |
-
value_states =
|
372 |
-
|
373 |
-
past_key_value = (key_states, value_states) if use_cache else None
|
374 |
|
375 |
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
376 |
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
377 |
|
378 |
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
379 |
|
380 |
-
if
|
381 |
-
|
382 |
-
|
383 |
-
f" {attn_weights.size()}"
|
384 |
-
)
|
385 |
-
|
386 |
-
if attention_mask is not None:
|
387 |
-
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
388 |
-
raise ValueError(
|
389 |
-
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
390 |
-
)
|
391 |
-
attn_weights = attn_weights + attention_mask
|
392 |
|
393 |
# upcast attention to fp32
|
394 |
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
@@ -401,9 +351,20 @@ class InternLM2Attention(nn.Module):
|
|
401 |
)
|
402 |
|
403 |
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
|
404 |
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
405 |
|
406 |
-
|
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|
407 |
|
408 |
if not output_attentions:
|
409 |
attn_weights = None
|
@@ -411,7 +372,6 @@ class InternLM2Attention(nn.Module):
|
|
411 |
return attn_output, attn_weights, past_key_value
|
412 |
|
413 |
|
414 |
-
# Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
|
415 |
class InternLM2FlashAttention2(InternLM2Attention):
|
416 |
"""
|
417 |
InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
|
@@ -419,26 +379,34 @@ class InternLM2FlashAttention2(InternLM2Attention):
|
|
419 |
flash attention and deal with padding tokens in case the input contains any of them.
|
420 |
"""
|
421 |
|
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|
422 |
def forward(
|
423 |
self,
|
424 |
hidden_states: torch.Tensor,
|
425 |
attention_mask: Optional[torch.LongTensor] = None,
|
426 |
position_ids: Optional[torch.LongTensor] = None,
|
427 |
-
past_key_value: Optional[
|
428 |
output_attentions: bool = False,
|
429 |
use_cache: bool = False,
|
430 |
-
|
431 |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
432 |
-
|
433 |
-
|
434 |
-
|
435 |
-
"
|
436 |
-
"
|
437 |
)
|
438 |
|
439 |
-
# overwrite attention_mask with padding_mask
|
440 |
-
attention_mask = kwargs.pop("padding_mask")
|
441 |
-
|
442 |
output_attentions = False
|
443 |
|
444 |
bsz, q_len, _ = hidden_states.size()
|
@@ -461,35 +429,61 @@ class InternLM2FlashAttention2(InternLM2Attention):
|
|
461 |
key_states = key_states.transpose(1, 2)
|
462 |
value_states = value_states.transpose(1, 2)
|
463 |
|
464 |
-
|
465 |
-
|
466 |
-
kv_seq_len += past_key_value[0].shape[-2]
|
467 |
-
|
468 |
-
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
469 |
-
|
470 |
-
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
471 |
|
472 |
if past_key_value is not None:
|
473 |
-
#
|
474 |
-
|
475 |
-
value_states =
|
476 |
-
|
477 |
-
past_key_value = (key_states, value_states) if use_cache else None
|
478 |
|
|
|
|
|
|
|
479 |
query_states = query_states.transpose(1, 2)
|
480 |
key_states = key_states.transpose(1, 2)
|
481 |
value_states = value_states.transpose(1, 2)
|
482 |
|
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|
483 |
attn_output = self._flash_attention_forward(
|
484 |
-
query_states, key_states, value_states, attention_mask, q_len
|
485 |
)
|
|
|
486 |
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
487 |
attn_output = self.wo(attn_output)
|
488 |
|
489 |
if not output_attentions:
|
490 |
attn_weights = None
|
491 |
|
492 |
-
return attn_output, attn_weights, past_key_value
|
493 |
|
494 |
def _flash_attention_forward(
|
495 |
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
|
@@ -508,23 +502,29 @@ class InternLM2FlashAttention2(InternLM2Attention):
|
|
508 |
attention_mask (`torch.Tensor`):
|
509 |
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
510 |
position of padding tokens and 1 for the position of non-padding tokens.
|
511 |
-
dropout (`
|
512 |
Attention dropout
|
513 |
softmax_scale (`float`, *optional*):
|
514 |
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
515 |
"""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
516 |
# Contains at least one padding token in the sequence
|
517 |
-
causal = self.is_causal and query_length != 1
|
518 |
if attention_mask is not None:
|
519 |
batch_size = query_states.shape[0]
|
520 |
-
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self.
|
521 |
query_states, key_states, value_states, attention_mask, query_length
|
522 |
)
|
523 |
|
524 |
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
525 |
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
526 |
|
527 |
-
attn_output_unpad = flash_attn_varlen_func(
|
528 |
query_states,
|
529 |
key_states,
|
530 |
value_states,
|
@@ -537,27 +537,26 @@ class InternLM2FlashAttention2(InternLM2Attention):
|
|
537 |
causal=causal,
|
538 |
)
|
539 |
|
540 |
-
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
541 |
else:
|
542 |
-
attn_output = flash_attn_func(
|
543 |
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
544 |
)
|
545 |
|
546 |
return attn_output
|
547 |
|
548 |
-
def
|
549 |
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
550 |
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
551 |
|
552 |
-
key_layer = index_first_axis(
|
553 |
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
554 |
)
|
555 |
-
value_layer = index_first_axis(
|
556 |
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
557 |
)
|
558 |
-
|
559 |
if query_length == kv_seq_len:
|
560 |
-
query_layer = index_first_axis(
|
561 |
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
562 |
)
|
563 |
cu_seqlens_q = cu_seqlens_k
|
@@ -573,29 +572,139 @@ class InternLM2FlashAttention2(InternLM2Attention):
|
|
573 |
else:
|
574 |
# The -q_len: slice assumes left padding.
|
575 |
attention_mask = attention_mask[:, -query_length:]
|
576 |
-
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
|
|
|
|
|
577 |
|
578 |
return (
|
579 |
query_layer,
|
580 |
key_layer,
|
581 |
value_layer,
|
582 |
-
indices_q
|
583 |
(cu_seqlens_q, cu_seqlens_k),
|
584 |
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
585 |
)
|
586 |
|
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|
587 |
INTERNLM2_ATTENTION_CLASSES = {
|
588 |
"eager": InternLM2Attention,
|
589 |
"flash_attention_2": InternLM2FlashAttention2,
|
|
|
590 |
}
|
591 |
|
592 |
-
|
|
|
593 |
class InternLM2DecoderLayer(nn.Module):
|
594 |
-
|
|
|
|
|
595 |
super().__init__()
|
596 |
self.hidden_size = config.hidden_size
|
|
|
597 |
|
598 |
-
self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
|
599 |
|
600 |
self.feed_forward = InternLM2MLP(config)
|
601 |
self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
@@ -606,10 +715,10 @@ class InternLM2DecoderLayer(nn.Module):
|
|
606 |
hidden_states: torch.Tensor,
|
607 |
attention_mask: Optional[torch.Tensor] = None,
|
608 |
position_ids: Optional[torch.LongTensor] = None,
|
609 |
-
past_key_value: Optional[
|
610 |
output_attentions: Optional[bool] = False,
|
611 |
use_cache: Optional[bool] = False,
|
612 |
-
|
613 |
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
614 |
"""
|
615 |
Args:
|
@@ -625,12 +734,6 @@ class InternLM2DecoderLayer(nn.Module):
|
|
625 |
(see `past_key_values`).
|
626 |
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
627 |
"""
|
628 |
-
if "padding_mask" in kwargs:
|
629 |
-
warnings.warn(
|
630 |
-
"Passing `padding_mask` is deprecated and will be removed in v4.37. "
|
631 |
-
"Please make sure use `attention_mask` instead.`"
|
632 |
-
)
|
633 |
-
|
634 |
residual = hidden_states
|
635 |
|
636 |
hidden_states = self.attention_norm(hidden_states)
|
@@ -643,7 +746,7 @@ class InternLM2DecoderLayer(nn.Module):
|
|
643 |
past_key_value=past_key_value,
|
644 |
output_attentions=output_attentions,
|
645 |
use_cache=use_cache,
|
646 |
-
|
647 |
)
|
648 |
hidden_states = residual + hidden_states
|
649 |
|
@@ -687,11 +790,20 @@ InternLM2_START_DOCSTRING = r"""
|
|
687 |
InternLM2_START_DOCSTRING,
|
688 |
)
|
689 |
class InternLM2PreTrainedModel(PreTrainedModel):
|
|
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|
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|
690 |
config_class = InternLM2Config
|
691 |
base_model_prefix = "model"
|
692 |
supports_gradient_checkpointing = True
|
693 |
_no_split_modules = ["InternLM2DecoderLayer"]
|
694 |
-
_skip_keys_device_placement = "past_key_values"
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|
695 |
|
696 |
def _init_weights(self, module):
|
697 |
std = self.config.initializer_range
|
@@ -740,14 +852,19 @@ InternLM2_INPUTS_DOCSTRING = r"""
|
|
740 |
config.n_positions - 1]`.
|
741 |
|
742 |
[What are position IDs?](../glossary#position-ids)
|
743 |
-
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional
|
744 |
-
|
745 |
-
|
746 |
-
|
747 |
-
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|
748 |
|
749 |
-
|
750 |
-
|
751 |
|
752 |
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
753 |
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
@@ -767,10 +884,14 @@ InternLM2_INPUTS_DOCSTRING = r"""
|
|
767 |
more detail.
|
768 |
return_dict (`bool`, *optional*):
|
769 |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
|
|
|
|
|
|
|
770 |
"""
|
771 |
|
772 |
|
773 |
-
# Modified from transformers.
|
774 |
@add_start_docstrings(
|
775 |
"The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
|
776 |
InternLM2_START_DOCSTRING,
|
@@ -793,7 +914,9 @@ class InternLM2Model(InternLM2PreTrainedModel):
|
|
793 |
|
794 |
self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
795 |
|
796 |
-
self.layers = nn.ModuleList(
|
|
|
|
|
797 |
self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
798 |
|
799 |
self.gradient_checkpointing = False
|
@@ -806,142 +929,96 @@ class InternLM2Model(InternLM2PreTrainedModel):
|
|
806 |
def set_input_embeddings(self, value):
|
807 |
self.tok_embeddings = value
|
808 |
|
809 |
-
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
810 |
-
# create causal mask
|
811 |
-
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
812 |
-
combined_attention_mask = None
|
813 |
-
if input_shape[-1] > 1:
|
814 |
-
combined_attention_mask = _make_causal_mask(
|
815 |
-
input_shape,
|
816 |
-
inputs_embeds.dtype,
|
817 |
-
device=inputs_embeds.device,
|
818 |
-
past_key_values_length=past_key_values_length,
|
819 |
-
)
|
820 |
-
|
821 |
-
if attention_mask is not None:
|
822 |
-
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
823 |
-
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
|
824 |
-
inputs_embeds.device
|
825 |
-
)
|
826 |
-
combined_attention_mask = (
|
827 |
-
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
828 |
-
)
|
829 |
-
|
830 |
-
return combined_attention_mask
|
831 |
-
|
832 |
@add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
|
833 |
def forward(
|
834 |
self,
|
835 |
input_ids: torch.LongTensor = None,
|
836 |
attention_mask: Optional[torch.Tensor] = None,
|
837 |
position_ids: Optional[torch.LongTensor] = None,
|
838 |
-
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
839 |
inputs_embeds: Optional[torch.FloatTensor] = None,
|
840 |
use_cache: Optional[bool] = None,
|
841 |
output_attentions: Optional[bool] = None,
|
842 |
output_hidden_states: Optional[bool] = None,
|
843 |
return_dict: Optional[bool] = None,
|
|
|
844 |
) -> Union[Tuple, BaseModelOutputWithPast]:
|
845 |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
846 |
output_hidden_states = (
|
847 |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
848 |
)
|
849 |
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
850 |
-
|
851 |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
852 |
|
853 |
-
if
|
854 |
-
|
855 |
-
|
856 |
-
|
857 |
-
if input_ids is not None and inputs_embeds is not None:
|
858 |
-
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
859 |
-
elif input_ids is not None:
|
860 |
-
batch_size, seq_length = input_ids.shape[:2]
|
861 |
-
elif inputs_embeds is not None:
|
862 |
-
batch_size, seq_length = inputs_embeds.shape[:2]
|
863 |
-
else:
|
864 |
-
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
865 |
-
|
866 |
-
seq_length_with_past = seq_length
|
867 |
-
past_key_values_length = 0
|
868 |
-
if past_key_values is not None:
|
869 |
-
past_key_values_length = past_key_values[0][0].shape[2]
|
870 |
-
seq_length_with_past = seq_length_with_past + past_key_values_length
|
871 |
|
872 |
-
if
|
873 |
-
|
874 |
-
|
875 |
-
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
876 |
)
|
877 |
-
|
878 |
|
879 |
if inputs_embeds is None:
|
880 |
inputs_embeds = self.tok_embeddings(input_ids)
|
881 |
|
882 |
-
|
883 |
-
|
884 |
-
|
885 |
-
|
886 |
-
|
887 |
-
|
888 |
-
|
889 |
-
|
890 |
-
|
891 |
-
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
892 |
)
|
|
|
|
|
|
|
|
|
|
|
|
|
893 |
|
894 |
# embed positions
|
895 |
hidden_states = inputs_embeds
|
896 |
|
897 |
-
if self.gradient_checkpointing and self.training:
|
898 |
-
if use_cache:
|
899 |
-
logger.warning_once(
|
900 |
-
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
901 |
-
)
|
902 |
-
use_cache = False
|
903 |
-
|
904 |
# decoder layers
|
905 |
all_hidden_states = () if output_hidden_states else None
|
906 |
all_self_attns = () if output_attentions else None
|
907 |
-
next_decoder_cache =
|
908 |
|
909 |
-
for
|
910 |
if output_hidden_states:
|
911 |
all_hidden_states += (hidden_states,)
|
912 |
|
913 |
-
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
914 |
-
|
915 |
if self.gradient_checkpointing and self.training:
|
916 |
-
|
917 |
-
|
918 |
-
def custom_forward(*inputs):
|
919 |
-
# None for past_key_value
|
920 |
-
return module(*inputs, output_attentions, None)
|
921 |
-
|
922 |
-
return custom_forward
|
923 |
-
|
924 |
-
layer_outputs = torch.utils.checkpoint.checkpoint(
|
925 |
-
create_custom_forward(decoder_layer),
|
926 |
hidden_states,
|
927 |
-
|
928 |
position_ids,
|
929 |
-
|
|
|
|
|
|
|
930 |
)
|
931 |
else:
|
932 |
layer_outputs = decoder_layer(
|
933 |
hidden_states,
|
934 |
-
attention_mask=
|
935 |
position_ids=position_ids,
|
936 |
-
past_key_value=
|
937 |
output_attentions=output_attentions,
|
938 |
use_cache=use_cache,
|
|
|
939 |
)
|
940 |
|
941 |
hidden_states = layer_outputs[0]
|
942 |
|
943 |
if use_cache:
|
944 |
-
next_decoder_cache
|
945 |
|
946 |
if output_attentions:
|
947 |
all_self_attns += (layer_outputs[1],)
|
@@ -953,6 +1030,9 @@ class InternLM2Model(InternLM2PreTrainedModel):
|
|
953 |
all_hidden_states += (hidden_states,)
|
954 |
|
955 |
next_cache = next_decoder_cache if use_cache else None
|
|
|
|
|
|
|
956 |
if not return_dict:
|
957 |
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
958 |
return BaseModelOutputWithPast(
|
@@ -962,11 +1042,95 @@ class InternLM2Model(InternLM2PreTrainedModel):
|
|
962 |
attentions=all_self_attns,
|
963 |
)
|
964 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
965 |
|
966 |
-
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|
|
|
|
|
|
|
|
|
967 |
class InternLM2ForCausalLM(InternLM2PreTrainedModel):
|
968 |
-
|
969 |
|
|
|
970 |
_tied_weights_keys = ["output.weight"]
|
971 |
|
972 |
def __init__(self, config):
|
@@ -1003,13 +1167,14 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
|
|
1003 |
input_ids: torch.LongTensor = None,
|
1004 |
attention_mask: Optional[torch.Tensor] = None,
|
1005 |
position_ids: Optional[torch.LongTensor] = None,
|
1006 |
-
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1007 |
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1008 |
labels: Optional[torch.LongTensor] = None,
|
1009 |
use_cache: Optional[bool] = None,
|
1010 |
output_attentions: Optional[bool] = None,
|
1011 |
output_hidden_states: Optional[bool] = None,
|
1012 |
return_dict: Optional[bool] = None,
|
|
|
1013 |
) -> Union[Tuple, CausalLMOutputWithPast]:
|
1014 |
r"""
|
1015 |
Args:
|
@@ -1025,8 +1190,8 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
|
|
1025 |
```python
|
1026 |
>>> from transformers import AutoTokenizer, InternLM2ForCausalLM
|
1027 |
|
1028 |
-
>>> model = InternLM2ForCausalLM.from_pretrained(
|
1029 |
-
>>> tokenizer = AutoTokenizer.from_pretrained(
|
1030 |
|
1031 |
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
1032 |
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
@@ -1054,10 +1219,19 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
|
|
1054 |
output_attentions=output_attentions,
|
1055 |
output_hidden_states=output_hidden_states,
|
1056 |
return_dict=return_dict,
|
|
|
1057 |
)
|
1058 |
|
1059 |
hidden_states = outputs[0]
|
1060 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1061 |
logits = logits.float()
|
1062 |
|
1063 |
loss = None
|
@@ -1086,19 +1260,48 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
|
|
1086 |
)
|
1087 |
|
1088 |
def prepare_inputs_for_generation(
|
1089 |
-
self,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1090 |
):
|
|
|
1091 |
if past_key_values is not None:
|
1092 |
-
|
1093 |
-
|
1094 |
-
|
1095 |
-
|
1096 |
-
|
|
|
|
|
|
|
|
|
1097 |
else:
|
1098 |
-
|
1099 |
-
|
1100 |
-
|
1101 |
-
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
1102 |
|
1103 |
position_ids = kwargs.get("position_ids", None)
|
1104 |
if attention_mask is not None and position_ids is None:
|
@@ -1112,13 +1315,24 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
|
|
1112 |
if inputs_embeds is not None and past_key_values is None:
|
1113 |
model_inputs = {"inputs_embeds": inputs_embeds}
|
1114 |
else:
|
1115 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1116 |
|
1117 |
model_inputs.update(
|
1118 |
{
|
1119 |
"position_ids": position_ids,
|
|
|
1120 |
"past_key_values": past_key_values,
|
1121 |
-
"use_cache":
|
1122 |
"attention_mask": attention_mask,
|
1123 |
}
|
1124 |
)
|
@@ -1133,7 +1347,9 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
|
|
1133 |
)
|
1134 |
return reordered_past
|
1135 |
|
1136 |
-
def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] =
|
|
|
|
|
1137 |
if tokenizer.add_bos_token:
|
1138 |
prompt = ""
|
1139 |
else:
|
@@ -1150,17 +1366,21 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
|
|
1150 |
self,
|
1151 |
tokenizer,
|
1152 |
query: str,
|
1153 |
-
history: List[Tuple[str, str]] =
|
1154 |
streamer: Optional[BaseStreamer] = None,
|
1155 |
max_new_tokens: int = 1024,
|
1156 |
do_sample: bool = True,
|
1157 |
temperature: float = 0.8,
|
1158 |
top_p: float = 0.8,
|
1159 |
meta_instruction: str = "You are an AI assistant whose name is InternLM (书生·浦语).\n"
|
1160 |
-
"- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory
|
1161 |
-
"
|
|
|
|
|
1162 |
**kwargs,
|
1163 |
):
|
|
|
|
|
1164 |
inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
|
1165 |
inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
|
1166 |
# also add end-of-assistant token in eos token id to avoid unnecessary generation
|
@@ -1186,13 +1406,15 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
|
|
1186 |
self,
|
1187 |
tokenizer,
|
1188 |
query: str,
|
1189 |
-
history: List[Tuple[str, str]] =
|
1190 |
max_new_tokens: int = 1024,
|
1191 |
do_sample: bool = True,
|
1192 |
temperature: float = 0.8,
|
1193 |
top_p: float = 0.8,
|
1194 |
**kwargs,
|
1195 |
):
|
|
|
|
|
1196 |
"""
|
1197 |
Return a generator in format: (response, history)
|
1198 |
Eg.
|
@@ -1208,6 +1430,10 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
|
|
1208 |
response_queue = queue.Queue(maxsize=20)
|
1209 |
|
1210 |
class ChatStreamer(BaseStreamer):
|
|
|
|
|
|
|
|
|
1211 |
def __init__(self, tokenizer) -> None:
|
1212 |
super().__init__()
|
1213 |
self.tokenizer = tokenizer
|
@@ -1268,13 +1494,13 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
|
|
1268 |
return consumer()
|
1269 |
|
1270 |
|
1271 |
-
# Copied from transformers.
|
1272 |
@add_start_docstrings(
|
1273 |
"""
|
1274 |
The InternLM2 Model transformer with a sequence classification head on top (linear layer).
|
1275 |
|
1276 |
-
[`InternLM2ForSequenceClassification`] uses the last token in order to do the classification,
|
1277 |
-
|
1278 |
|
1279 |
Since it does classification on the last token, it requires to know the position of the last token. If a
|
1280 |
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
@@ -1285,6 +1511,8 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
|
|
1285 |
InternLM2_START_DOCSTRING,
|
1286 |
)
|
1287 |
class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
|
|
|
|
|
1288 |
def __init__(self, config):
|
1289 |
super().__init__(config)
|
1290 |
self.num_labels = config.num_labels
|
@@ -1306,7 +1534,7 @@ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
|
|
1306 |
input_ids: torch.LongTensor = None,
|
1307 |
attention_mask: Optional[torch.Tensor] = None,
|
1308 |
position_ids: Optional[torch.LongTensor] = None,
|
1309 |
-
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1310 |
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1311 |
labels: Optional[torch.LongTensor] = None,
|
1312 |
use_cache: Optional[bool] = None,
|
@@ -1347,9 +1575,10 @@ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
|
|
1347 |
sequence_lengths = -1
|
1348 |
else:
|
1349 |
if input_ids is not None:
|
1350 |
-
|
1351 |
-
|
1352 |
-
|
|
|
1353 |
else:
|
1354 |
sequence_lengths = -1
|
1355 |
|
@@ -1361,7 +1590,7 @@ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
|
|
1361 |
if self.config.problem_type is None:
|
1362 |
if self.num_labels == 1:
|
1363 |
self.config.problem_type = "regression"
|
1364 |
-
elif self.num_labels > 1 and (labels.dtype
|
1365 |
self.config.problem_type = "single_label_classification"
|
1366 |
else:
|
1367 |
self.config.problem_type = "multi_label_classification"
|
@@ -1389,3 +1618,191 @@ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
|
|
1389 |
hidden_states=transformer_outputs.hidden_states,
|
1390 |
attentions=transformer_outputs.attentions,
|
1391 |
)
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13 |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
14 |
# See the License for the specific language governing permissions and
|
15 |
# limitations under the License.
|
16 |
+
"""PyTorch InternLM2 model."""
|
17 |
import math
|
18 |
import queue
|
19 |
import threading
|
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|
20 |
from typing import List, Optional, Tuple, Union
|
21 |
|
22 |
import torch
|
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|
26 |
from torch import nn
|
27 |
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
28 |
from transformers.activations import ACT2FN
|
29 |
+
from transformers.cache_utils import Cache, DynamicCache, StaticCache
|
30 |
+
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
|
31 |
from transformers.modeling_outputs import (
|
32 |
BaseModelOutputWithPast,
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33 |
CausalLMOutputWithPast,
|
34 |
+
QuestionAnsweringModelOutput,
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35 |
SequenceClassifierOutputWithPast,
|
36 |
+
TokenClassifierOutput,
|
37 |
)
|
38 |
from transformers.modeling_utils import PreTrainedModel
|
39 |
+
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
|
40 |
from transformers.utils import (
|
41 |
add_start_docstrings,
|
42 |
add_start_docstrings_to_model_forward,
|
43 |
+
is_flash_attn_greater_or_equal_2_10,
|
44 |
logging,
|
45 |
replace_return_docstrings,
|
46 |
)
|
47 |
|
48 |
try:
|
49 |
from transformers.generation.streamers import BaseStreamer
|
50 |
+
except Exception:
|
51 |
BaseStreamer = None
|
52 |
|
53 |
from .configuration_internlm2 import InternLM2Config
|
54 |
|
55 |
+
|
56 |
+
try:
|
57 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
58 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
|
59 |
+
except:
|
60 |
+
pass
|
61 |
+
|
62 |
+
try:
|
63 |
+
support_bf16_triu = torch.__version__ >= "2.1.0"
|
64 |
+
except Exception:
|
65 |
+
support_bf16_triu = False
|
66 |
+
|
67 |
logger = logging.get_logger(__name__)
|
68 |
|
69 |
_CONFIG_FOR_DOC = "InternLM2Config"
|
70 |
|
71 |
+
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|
72 |
def _get_unpad_data(attention_mask):
|
73 |
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
74 |
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
75 |
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
76 |
+
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) # pylint: disable=E1102
|
77 |
return (
|
78 |
indices,
|
79 |
cu_seqlens,
|
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|
81 |
)
|
82 |
|
83 |
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|
84 |
class InternLM2RMSNorm(nn.Module):
|
85 |
+
"""InternLM2RMSNorm is equivalent to T5LayerNorm."""
|
86 |
+
|
87 |
def __init__(self, hidden_size, eps=1e-6):
|
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|
|
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|
88 |
super().__init__()
|
89 |
self.weight = nn.Parameter(torch.ones(hidden_size))
|
90 |
self.variance_epsilon = eps
|
|
|
97 |
return self.weight * hidden_states.to(input_dtype)
|
98 |
|
99 |
|
100 |
+
ALL_LAYERNORM_LAYERS.append(InternLM2RMSNorm)
|
101 |
+
|
102 |
+
|
103 |
class InternLM2RotaryEmbedding(nn.Module):
|
104 |
+
"""Rotary Position Embedding for the InternLM2 model. Credits to the Reddit user /u/lucidrains."""
|
|
|
105 |
|
106 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
107 |
+
super().__init__()
|
108 |
+
self.scaling_factor = scaling_factor
|
109 |
self.dim = dim
|
110 |
self.max_position_embeddings = max_position_embeddings
|
111 |
self.base = base
|
112 |
+
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
|
113 |
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
114 |
+
# For BC we register cos and sin cached
|
115 |
+
self.max_seq_len_cached = max_position_embeddings
|
116 |
|
117 |
+
@torch.no_grad()
|
118 |
+
def forward(self, x, position_ids):
|
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|
119 |
# x: [bs, num_attention_heads, seq_len, head_size]
|
120 |
+
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
|
121 |
+
position_ids_expanded = position_ids[:, None, :].float()
|
122 |
+
# Force float32 since bfloat16 loses precision on long contexts
|
123 |
+
# See https://github.com/huggingface/transformers/pull/29285
|
124 |
+
device_type = x.device.type
|
125 |
+
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
|
126 |
+
with torch.autocast(device_type=device_type, enabled=False):
|
127 |
+
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
128 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
129 |
+
cos = emb.cos()
|
130 |
+
sin = emb.sin()
|
131 |
+
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
132 |
|
133 |
|
|
|
134 |
class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
|
135 |
"""InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
136 |
|
137 |
+
def forward(self, x, position_ids):
|
138 |
+
# difference to the original RoPE: a scaling factor is aplied to the position ids
|
139 |
+
position_ids = position_ids.float() / self.scaling_factor
|
140 |
+
cos, sin = super().forward(x, position_ids)
|
141 |
+
return cos, sin
|
|
|
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|
142 |
|
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|
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|
143 |
|
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|
|
|
144 |
class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
|
145 |
"""InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
|
146 |
+
Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
147 |
|
148 |
+
def forward(self, x, position_ids):
|
149 |
+
# difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
|
150 |
+
seq_len = torch.max(position_ids) + 1
|
151 |
if seq_len > self.max_position_embeddings:
|
152 |
base = self.base * (
|
153 |
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
|
154 |
) ** (self.dim / (self.dim - 2))
|
155 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim))
|
156 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: this may break with compilation
|
|
|
|
|
157 |
|
158 |
+
cos, sin = super().forward(x, position_ids)
|
159 |
+
return cos, sin
|
|
|
|
|
|
|
160 |
|
161 |
|
|
|
162 |
def rotate_half(x):
|
163 |
"""Rotates half the hidden dims of the input."""
|
164 |
x1 = x[..., : x.shape[-1] // 2]
|
|
|
166 |
return torch.cat((-x2, x1), dim=-1)
|
167 |
|
168 |
|
169 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): # pylint: disable=unused-argument
|
170 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
171 |
+
|
172 |
+
Args:
|
173 |
+
q (`torch.Tensor`): The query tensor.
|
174 |
+
k (`torch.Tensor`): The key tensor.
|
175 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
176 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
177 |
+
position_ids (`torch.Tensor`, *optional*):
|
178 |
+
Deprecated and unused.
|
179 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
180 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
181 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
182 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
183 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
184 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
185 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
186 |
+
Returns:
|
187 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
188 |
+
"""
|
189 |
+
cos = cos.unsqueeze(unsqueeze_dim)
|
190 |
+
sin = sin.unsqueeze(unsqueeze_dim)
|
191 |
q_embed = (q * cos) + (rotate_half(q) * sin)
|
192 |
k_embed = (k * cos) + (rotate_half(k) * sin)
|
193 |
return q_embed, k_embed
|
194 |
|
195 |
|
196 |
class InternLM2MLP(nn.Module):
|
197 |
+
"""MLP for InternLM2 model."""
|
198 |
+
|
199 |
def __init__(self, config):
|
200 |
super().__init__()
|
201 |
self.config = config
|
|
|
212 |
return down_proj
|
213 |
|
214 |
|
|
|
215 |
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
216 |
"""
|
217 |
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
|
|
224 |
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
225 |
|
226 |
|
|
|
227 |
class InternLM2Attention(nn.Module):
|
228 |
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
229 |
|
230 |
+
def __init__(self, config: InternLM2Config, layer_idx: Optional[int] = None):
|
231 |
super().__init__()
|
232 |
self.config = config
|
233 |
+
self.layer_idx = layer_idx
|
234 |
+
if layer_idx is None:
|
235 |
+
logger.warning_once(
|
236 |
+
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
|
237 |
+
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
|
238 |
+
"when creating this class."
|
239 |
+
)
|
240 |
+
|
241 |
self.hidden_size = config.hidden_size
|
242 |
self.num_heads = config.num_attention_heads
|
243 |
self.head_dim = self.hidden_size // self.num_heads
|
244 |
self.num_key_value_heads = config.num_key_value_heads
|
245 |
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
246 |
self.max_position_embeddings = config.max_position_embeddings
|
247 |
+
self.rope_theta = config.rope_theta
|
248 |
self.is_causal = True
|
249 |
|
250 |
if (self.head_dim * self.num_heads) != self.hidden_size:
|
|
|
258 |
(self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
|
259 |
bias=config.bias,
|
260 |
)
|
|
|
261 |
self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
|
262 |
+
|
263 |
self._init_rope()
|
264 |
|
265 |
def _init_rope(self):
|
|
|
267 |
self.rotary_emb = InternLM2RotaryEmbedding(
|
268 |
self.head_dim,
|
269 |
max_position_embeddings=self.max_position_embeddings,
|
270 |
+
base=self.rope_theta,
|
271 |
)
|
272 |
else:
|
273 |
scaling_type = self.config.rope_scaling["type"]
|
274 |
scaling_factor = self.config.rope_scaling["factor"]
|
275 |
+
if scaling_type == "linear":
|
276 |
+
self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
|
277 |
self.head_dim,
|
278 |
max_position_embeddings=self.max_position_embeddings,
|
|
|
279 |
scaling_factor=scaling_factor,
|
280 |
+
base=self.rope_theta,
|
281 |
)
|
282 |
+
elif scaling_type == "dynamic":
|
283 |
+
self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
|
284 |
self.head_dim,
|
285 |
max_position_embeddings=self.max_position_embeddings,
|
|
|
286 |
scaling_factor=scaling_factor,
|
287 |
+
base=self.rope_theta,
|
288 |
)
|
289 |
else:
|
290 |
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
|
|
|
|
|
|
|
|
291 |
|
292 |
def forward(
|
293 |
self,
|
294 |
hidden_states: torch.Tensor,
|
295 |
attention_mask: Optional[torch.Tensor] = None,
|
296 |
position_ids: Optional[torch.LongTensor] = None,
|
297 |
+
past_key_value: Optional[Cache] = None,
|
298 |
output_attentions: bool = False,
|
299 |
+
use_cache: bool = False, # pylint: disable=unused-argument
|
300 |
+
cache_position: Optional[torch.LongTensor] = None,
|
301 |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
|
|
|
|
|
|
|
|
|
|
|
302 |
bsz, q_len, _ = hidden_states.size()
|
303 |
|
304 |
+
if self.config.pretraining_tp > 1:
|
305 |
+
# split qkv_states by tp size
|
306 |
+
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
|
307 |
+
qkv_slices = self.wqkv.weight.split(key_value_slicing, dim=0)
|
308 |
+
qkv_states = torch.cat(
|
309 |
+
[F.linear(hidden_states, qkv_slice) for qkv_slice in qkv_slices], dim=-1 # pylint: disable=E1102
|
310 |
+
)
|
311 |
+
else:
|
312 |
+
qkv_states = self.wqkv(hidden_states)
|
313 |
|
314 |
qkv_states = rearrange(
|
315 |
qkv_states,
|
|
|
319 |
)
|
320 |
|
321 |
query_states = qkv_states[..., : self.num_key_value_groups, :]
|
322 |
+
query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d").transpose(1, 2)
|
323 |
+
key_states = qkv_states[..., -2, :].transpose(1, 2)
|
324 |
+
value_states = qkv_states[..., -1, :].transpose(1, 2)
|
325 |
|
326 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
327 |
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
328 |
|
329 |
if past_key_value is not None:
|
330 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
331 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
332 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
|
333 |
|
334 |
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
335 |
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
336 |
|
337 |
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
338 |
|
339 |
+
if attention_mask is not None: # no matter the length, we just slice it
|
340 |
+
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
341 |
+
attn_weights = attn_weights + causal_mask
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
342 |
|
343 |
# upcast attention to fp32
|
344 |
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
|
|
351 |
)
|
352 |
|
353 |
attn_output = attn_output.transpose(1, 2).contiguous()
|
354 |
+
|
355 |
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
356 |
|
357 |
+
if self.config.pretraining_tp > 1:
|
358 |
+
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
|
359 |
+
o_proj_slices = self.wo.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
|
360 |
+
attn_output = sum(
|
361 |
+
[
|
362 |
+
F.linear(attn_output[i], o_proj_slices[i]) # pylint: disable=E1102
|
363 |
+
for i in range(self.config.pretraining_tp)
|
364 |
+
]
|
365 |
+
)
|
366 |
+
else:
|
367 |
+
attn_output = self.wo(attn_output)
|
368 |
|
369 |
if not output_attentions:
|
370 |
attn_weights = None
|
|
|
372 |
return attn_output, attn_weights, past_key_value
|
373 |
|
374 |
|
|
|
375 |
class InternLM2FlashAttention2(InternLM2Attention):
|
376 |
"""
|
377 |
InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
|
|
|
379 |
flash attention and deal with padding tokens in case the input contains any of them.
|
380 |
"""
|
381 |
|
382 |
+
def __init__(self, *args, **kwargs):
|
383 |
+
super().__init__(*args, **kwargs)
|
384 |
+
|
385 |
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
386 |
+
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement,
|
387 |
+
# that was made default for flash_attn>=2.1. This attribute is used to handle this difference.
|
388 |
+
# Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
389 |
+
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1)
|
390 |
+
# produces a wrong mask (top-left).
|
391 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
392 |
+
|
393 |
def forward(
|
394 |
self,
|
395 |
hidden_states: torch.Tensor,
|
396 |
attention_mask: Optional[torch.LongTensor] = None,
|
397 |
position_ids: Optional[torch.LongTensor] = None,
|
398 |
+
past_key_value: Optional[Cache] = None,
|
399 |
output_attentions: bool = False,
|
400 |
use_cache: bool = False,
|
401 |
+
cache_position: Optional[torch.LongTensor] = None,
|
402 |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
403 |
+
if isinstance(past_key_value, StaticCache):
|
404 |
+
raise ValueError(
|
405 |
+
"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
|
406 |
+
"make sure to use `sdpa` in the mean time, and open an issue at "
|
407 |
+
"https://github.com/huggingface/transformers"
|
408 |
)
|
409 |
|
|
|
|
|
|
|
410 |
output_attentions = False
|
411 |
|
412 |
bsz, q_len, _ = hidden_states.size()
|
|
|
429 |
key_states = key_states.transpose(1, 2)
|
430 |
value_states = value_states.transpose(1, 2)
|
431 |
|
432 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
433 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
|
|
|
|
|
|
|
|
|
|
434 |
|
435 |
if past_key_value is not None:
|
436 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
437 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
438 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
|
439 |
|
440 |
+
# TODO: These transpose are quite inefficient but Flash Attention requires the layout
|
441 |
+
# [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
442 |
+
# to be able to avoid many of these transpose/reshape/view.
|
443 |
query_states = query_states.transpose(1, 2)
|
444 |
key_states = key_states.transpose(1, 2)
|
445 |
value_states = value_states.transpose(1, 2)
|
446 |
|
447 |
+
# dropout_rate = self.attention_dropout if self.training else 0.0
|
448 |
+
dropout_rate = 0.0
|
449 |
+
|
450 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
451 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
452 |
+
# cast them back in the correct dtype just to be sure everything works as expected.
|
453 |
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
454 |
+
# in fp32. (InternLM2RMSNorm handles it correctly)
|
455 |
+
|
456 |
+
input_dtype = query_states.dtype
|
457 |
+
if input_dtype == torch.float32:
|
458 |
+
if torch.is_autocast_enabled():
|
459 |
+
target_dtype = torch.get_autocast_gpu_dtype()
|
460 |
+
# Handle the case where the model is quantized
|
461 |
+
elif hasattr(self.config, "_pre_quantization_dtype"):
|
462 |
+
target_dtype = self.config._pre_quantization_dtype
|
463 |
+
else:
|
464 |
+
target_dtype = self.wqkv.weight.dtype
|
465 |
+
|
466 |
+
logger.warning_once(
|
467 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
468 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
469 |
+
f" {target_dtype}."
|
470 |
+
)
|
471 |
+
|
472 |
+
query_states = query_states.to(target_dtype)
|
473 |
+
key_states = key_states.to(target_dtype)
|
474 |
+
value_states = value_states.to(target_dtype)
|
475 |
+
|
476 |
attn_output = self._flash_attention_forward(
|
477 |
+
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
|
478 |
)
|
479 |
+
|
480 |
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
481 |
attn_output = self.wo(attn_output)
|
482 |
|
483 |
if not output_attentions:
|
484 |
attn_weights = None
|
485 |
|
486 |
+
return attn_output, attn_weights, past_key_value # pylint: disable=E0606
|
487 |
|
488 |
def _flash_attention_forward(
|
489 |
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
|
|
|
502 |
attention_mask (`torch.Tensor`):
|
503 |
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
504 |
position of padding tokens and 1 for the position of non-padding tokens.
|
505 |
+
dropout (`float`):
|
506 |
Attention dropout
|
507 |
softmax_scale (`float`, *optional*):
|
508 |
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
509 |
"""
|
510 |
+
if not self._flash_attn_uses_top_left_mask:
|
511 |
+
causal = self.is_causal
|
512 |
+
else:
|
513 |
+
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1.
|
514 |
+
# For details, please see the comment in InternLM2FlashAttention2 __init__.
|
515 |
+
causal = self.is_causal and query_length != 1
|
516 |
+
|
517 |
# Contains at least one padding token in the sequence
|
|
|
518 |
if attention_mask is not None:
|
519 |
batch_size = query_states.shape[0]
|
520 |
+
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
521 |
query_states, key_states, value_states, attention_mask, query_length
|
522 |
)
|
523 |
|
524 |
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
525 |
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
526 |
|
527 |
+
attn_output_unpad = flash_attn_varlen_func( # pylint: disable=E0606
|
528 |
query_states,
|
529 |
key_states,
|
530 |
value_states,
|
|
|
537 |
causal=causal,
|
538 |
)
|
539 |
|
540 |
+
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) # pylint: disable=E0606
|
541 |
else:
|
542 |
+
attn_output = flash_attn_func( # pylint: disable=E0606
|
543 |
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
544 |
)
|
545 |
|
546 |
return attn_output
|
547 |
|
548 |
+
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
549 |
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
550 |
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
551 |
|
552 |
+
key_layer = index_first_axis( # pylint: disable=E0606
|
553 |
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
554 |
)
|
555 |
+
value_layer = index_first_axis( # pylint: disable=E0606
|
556 |
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
557 |
)
|
|
|
558 |
if query_length == kv_seq_len:
|
559 |
+
query_layer = index_first_axis( # pylint: disable=E0606
|
560 |
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
561 |
)
|
562 |
cu_seqlens_q = cu_seqlens_k
|
|
|
572 |
else:
|
573 |
# The -q_len: slice assumes left padding.
|
574 |
attention_mask = attention_mask[:, -query_length:]
|
575 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( # pylint: disable=E0606
|
576 |
+
query_layer, attention_mask
|
577 |
+
)
|
578 |
|
579 |
return (
|
580 |
query_layer,
|
581 |
key_layer,
|
582 |
value_layer,
|
583 |
+
indices_q,
|
584 |
(cu_seqlens_q, cu_seqlens_k),
|
585 |
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
586 |
)
|
587 |
|
588 |
+
|
589 |
+
# Copied from transformers.models.llama.modeling_llama.LllamaSdpaAttention with Llama->InternLM2
|
590 |
+
class InternLM2SdpaAttention(InternLM2Attention):
|
591 |
+
"""
|
592 |
+
InternLM2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
593 |
+
`InternLM2Attention` as the weights of the module stays untouched. The only changes are on the forward pass
|
594 |
+
to adapt to SDPA API.
|
595 |
+
"""
|
596 |
+
|
597 |
+
# Adapted from InternLM2Attention.forward
|
598 |
+
def forward(
|
599 |
+
self,
|
600 |
+
hidden_states: torch.Tensor,
|
601 |
+
attention_mask: Optional[torch.Tensor] = None,
|
602 |
+
position_ids: Optional[torch.LongTensor] = None,
|
603 |
+
past_key_value: Optional[Cache] = None,
|
604 |
+
output_attentions: bool = False,
|
605 |
+
use_cache: bool = False,
|
606 |
+
cache_position: Optional[torch.LongTensor] = None,
|
607 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
608 |
+
if output_attentions:
|
609 |
+
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"`
|
610 |
+
# once this is implemented.
|
611 |
+
logger.warning_once(
|
612 |
+
"InternLM2Model uses InternLM2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` "
|
613 |
+
"does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
614 |
+
"but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. "
|
615 |
+
'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
616 |
+
)
|
617 |
+
return super().forward(
|
618 |
+
hidden_states=hidden_states,
|
619 |
+
attention_mask=attention_mask,
|
620 |
+
position_ids=position_ids,
|
621 |
+
past_key_value=past_key_value,
|
622 |
+
output_attentions=output_attentions,
|
623 |
+
use_cache=use_cache,
|
624 |
+
cache_position=cache_position,
|
625 |
+
)
|
626 |
+
|
627 |
+
bsz, q_len, _ = hidden_states.size()
|
628 |
+
|
629 |
+
qkv_states = self.wqkv(hidden_states)
|
630 |
+
|
631 |
+
qkv_states = rearrange(
|
632 |
+
qkv_states,
|
633 |
+
"b q (h gs d) -> b q h gs d",
|
634 |
+
gs=2 + self.num_key_value_groups,
|
635 |
+
d=self.head_dim,
|
636 |
+
)
|
637 |
+
|
638 |
+
query_states = qkv_states[..., : self.num_key_value_groups, :]
|
639 |
+
query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
|
640 |
+
key_states = qkv_states[..., -2, :]
|
641 |
+
value_states = qkv_states[..., -1, :]
|
642 |
+
|
643 |
+
query_states = query_states.transpose(1, 2)
|
644 |
+
key_states = key_states.transpose(1, 2)
|
645 |
+
value_states = value_states.transpose(1, 2)
|
646 |
+
|
647 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
648 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
649 |
+
|
650 |
+
if past_key_value is not None:
|
651 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
652 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
653 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
654 |
+
|
655 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
656 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
657 |
+
|
658 |
+
causal_mask = attention_mask
|
659 |
+
if attention_mask is not None:
|
660 |
+
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
|
661 |
+
|
662 |
+
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with
|
663 |
+
# custom attn_mask, Reference: https://github.com/pytorch/pytorch/issues/112577.
|
664 |
+
if query_states.device.type == "cuda" and causal_mask is not None:
|
665 |
+
query_states = query_states.contiguous()
|
666 |
+
key_states = key_states.contiguous()
|
667 |
+
value_states = value_states.contiguous()
|
668 |
+
|
669 |
+
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of
|
670 |
+
# an inline conditional assignment in SDPA to support both torch.compile's dynamic shapes and full graph
|
671 |
+
# options. An inline conditional prevents dynamic shapes from compiling.
|
672 |
+
is_causal = bool(causal_mask is None and q_len > 1)
|
673 |
+
|
674 |
+
attn_output = torch.nn.functional.scaled_dot_product_attention( # pylint: disable=E1102
|
675 |
+
query_states,
|
676 |
+
key_states,
|
677 |
+
value_states,
|
678 |
+
attn_mask=causal_mask,
|
679 |
+
dropout_p=0.0,
|
680 |
+
is_causal=is_causal,
|
681 |
+
)
|
682 |
+
|
683 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
684 |
+
attn_output = attn_output.view(bsz, q_len, self.hidden_size)
|
685 |
+
|
686 |
+
attn_output = self.wo(attn_output)
|
687 |
+
|
688 |
+
return attn_output, None, past_key_value
|
689 |
+
|
690 |
+
|
691 |
INTERNLM2_ATTENTION_CLASSES = {
|
692 |
"eager": InternLM2Attention,
|
693 |
"flash_attention_2": InternLM2FlashAttention2,
|
694 |
+
"sdpa": InternLM2SdpaAttention,
|
695 |
}
|
696 |
|
697 |
+
|
698 |
+
# Modified from transformers.models.llama.modeling_llama.LlamaDecoderLayer with Llama->InternLM2
|
699 |
class InternLM2DecoderLayer(nn.Module):
|
700 |
+
"""InternLM2 Decoder Layer. This module is a single layer of the InternLM2 model."""
|
701 |
+
|
702 |
+
def __init__(self, config: InternLM2Config, layer_idx: int):
|
703 |
super().__init__()
|
704 |
self.hidden_size = config.hidden_size
|
705 |
+
self.layer_idx = layer_idx
|
706 |
|
707 |
+
self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config, layer_idx=layer_idx)
|
708 |
|
709 |
self.feed_forward = InternLM2MLP(config)
|
710 |
self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
715 |
hidden_states: torch.Tensor,
|
716 |
attention_mask: Optional[torch.Tensor] = None,
|
717 |
position_ids: Optional[torch.LongTensor] = None,
|
718 |
+
past_key_value: Optional[Cache] = None,
|
719 |
output_attentions: Optional[bool] = False,
|
720 |
use_cache: Optional[bool] = False,
|
721 |
+
cache_position: Optional[torch.LongTensor] = None,
|
722 |
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
723 |
"""
|
724 |
Args:
|
|
|
734 |
(see `past_key_values`).
|
735 |
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
736 |
"""
|
|
|
|
|
|
|
|
|
|
|
|
|
737 |
residual = hidden_states
|
738 |
|
739 |
hidden_states = self.attention_norm(hidden_states)
|
|
|
746 |
past_key_value=past_key_value,
|
747 |
output_attentions=output_attentions,
|
748 |
use_cache=use_cache,
|
749 |
+
cache_position=cache_position,
|
750 |
)
|
751 |
hidden_states = residual + hidden_states
|
752 |
|
|
|
790 |
InternLM2_START_DOCSTRING,
|
791 |
)
|
792 |
class InternLM2PreTrainedModel(PreTrainedModel):
|
793 |
+
"""
|
794 |
+
InternLM2 pretraiend model's base class.
|
795 |
+
"""
|
796 |
+
|
797 |
config_class = InternLM2Config
|
798 |
base_model_prefix = "model"
|
799 |
supports_gradient_checkpointing = True
|
800 |
_no_split_modules = ["InternLM2DecoderLayer"]
|
801 |
+
_skip_keys_device_placement = ["past_key_values"]
|
802 |
+
_supports_flash_attn_2 = True
|
803 |
+
_supports_sdpa = True
|
804 |
+
_supports_cache_class = True
|
805 |
+
_supports_quantized_cache = True
|
806 |
+
_supports_static_cache = True
|
807 |
|
808 |
def _init_weights(self, module):
|
809 |
std = self.config.initializer_range
|
|
|
852 |
config.n_positions - 1]`.
|
853 |
|
854 |
[What are position IDs?](../glossary#position-ids)
|
855 |
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
856 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
857 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
858 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
859 |
+
|
860 |
+
Two formats are allowed:
|
861 |
+
- a [`~cache_utils.Cache`] instance;
|
862 |
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
863 |
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
864 |
+
cache format.
|
865 |
|
866 |
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
867 |
+
legacy cache format will be returned.
|
868 |
|
869 |
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
870 |
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
|
|
884 |
more detail.
|
885 |
return_dict (`bool`, *optional*):
|
886 |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
887 |
+
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
888 |
+
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
|
889 |
+
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
|
890 |
+
the complete sequence length.
|
891 |
"""
|
892 |
|
893 |
|
894 |
+
# Modified from transformers.models.llama.modeling_llama.LlamaModel with Llama->InternLM2
|
895 |
@add_start_docstrings(
|
896 |
"The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
|
897 |
InternLM2_START_DOCSTRING,
|
|
|
914 |
|
915 |
self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
916 |
|
917 |
+
self.layers = nn.ModuleList(
|
918 |
+
[InternLM2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
919 |
+
)
|
920 |
self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
921 |
|
922 |
self.gradient_checkpointing = False
|
|
|
929 |
def set_input_embeddings(self, value):
|
930 |
self.tok_embeddings = value
|
931 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
932 |
@add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
|
933 |
def forward(
|
934 |
self,
|
935 |
input_ids: torch.LongTensor = None,
|
936 |
attention_mask: Optional[torch.Tensor] = None,
|
937 |
position_ids: Optional[torch.LongTensor] = None,
|
938 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
939 |
inputs_embeds: Optional[torch.FloatTensor] = None,
|
940 |
use_cache: Optional[bool] = None,
|
941 |
output_attentions: Optional[bool] = None,
|
942 |
output_hidden_states: Optional[bool] = None,
|
943 |
return_dict: Optional[bool] = None,
|
944 |
+
cache_position: Optional[torch.LongTensor] = None,
|
945 |
) -> Union[Tuple, BaseModelOutputWithPast]:
|
946 |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
947 |
output_hidden_states = (
|
948 |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
949 |
)
|
950 |
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
951 |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
952 |
|
953 |
+
if (input_ids is None) ^ (inputs_embeds is not None):
|
954 |
+
raise ValueError(
|
955 |
+
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
|
956 |
+
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
957 |
|
958 |
+
if self.gradient_checkpointing and self.training and use_cache:
|
959 |
+
logger.warning_once(
|
960 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
|
|
961 |
)
|
962 |
+
use_cache = False
|
963 |
|
964 |
if inputs_embeds is None:
|
965 |
inputs_embeds = self.tok_embeddings(input_ids)
|
966 |
|
967 |
+
return_legacy_cache = False
|
968 |
+
if use_cache and not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs)
|
969 |
+
return_legacy_cache = True
|
970 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
971 |
+
|
972 |
+
if cache_position is None:
|
973 |
+
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
974 |
+
cache_position = torch.arange(
|
975 |
+
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
|
|
976 |
)
|
977 |
+
if position_ids is None:
|
978 |
+
position_ids = cache_position.unsqueeze(0)
|
979 |
+
|
980 |
+
causal_mask = self._update_causal_mask(
|
981 |
+
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
982 |
+
)
|
983 |
|
984 |
# embed positions
|
985 |
hidden_states = inputs_embeds
|
986 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
987 |
# decoder layers
|
988 |
all_hidden_states = () if output_hidden_states else None
|
989 |
all_self_attns = () if output_attentions else None
|
990 |
+
next_decoder_cache = None
|
991 |
|
992 |
+
for decoder_layer in self.layers:
|
993 |
if output_hidden_states:
|
994 |
all_hidden_states += (hidden_states,)
|
995 |
|
|
|
|
|
996 |
if self.gradient_checkpointing and self.training:
|
997 |
+
layer_outputs = self._gradient_checkpointing_func(
|
998 |
+
decoder_layer.__call__,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
999 |
hidden_states,
|
1000 |
+
causal_mask,
|
1001 |
position_ids,
|
1002 |
+
past_key_values,
|
1003 |
+
output_attentions,
|
1004 |
+
use_cache,
|
1005 |
+
cache_position,
|
1006 |
)
|
1007 |
else:
|
1008 |
layer_outputs = decoder_layer(
|
1009 |
hidden_states,
|
1010 |
+
attention_mask=causal_mask,
|
1011 |
position_ids=position_ids,
|
1012 |
+
past_key_value=past_key_values,
|
1013 |
output_attentions=output_attentions,
|
1014 |
use_cache=use_cache,
|
1015 |
+
cache_position=cache_position,
|
1016 |
)
|
1017 |
|
1018 |
hidden_states = layer_outputs[0]
|
1019 |
|
1020 |
if use_cache:
|
1021 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
1022 |
|
1023 |
if output_attentions:
|
1024 |
all_self_attns += (layer_outputs[1],)
|
|
|
1030 |
all_hidden_states += (hidden_states,)
|
1031 |
|
1032 |
next_cache = next_decoder_cache if use_cache else None
|
1033 |
+
if return_legacy_cache:
|
1034 |
+
next_cache = next_cache.to_legacy_cache()
|
1035 |
+
|
1036 |
if not return_dict:
|
1037 |
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
1038 |
return BaseModelOutputWithPast(
|
|
|
1042 |
attentions=all_self_attns,
|
1043 |
)
|
1044 |
|
1045 |
+
def _update_causal_mask(
|
1046 |
+
self,
|
1047 |
+
attention_mask: torch.Tensor,
|
1048 |
+
input_tensor: torch.Tensor,
|
1049 |
+
cache_position: torch.Tensor,
|
1050 |
+
past_key_values: Cache,
|
1051 |
+
output_attentions: bool,
|
1052 |
+
):
|
1053 |
+
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length
|
1054 |
+
# even when the static KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at
|
1055 |
+
# each decode steps due to the dynamic shapes. (`recording cudagraph tree for symint key 13`, etc.), which is
|
1056 |
+
# VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using `fullgraph=True`.
|
1057 |
+
# See more context in https://github.com/huggingface/transformers/pull/29114
|
1058 |
|
1059 |
+
if self.config.attn_implementation == "flash_attention_2":
|
1060 |
+
if attention_mask is not None and 0.0 in attention_mask:
|
1061 |
+
return attention_mask
|
1062 |
+
return None
|
1063 |
+
|
1064 |
+
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
1065 |
+
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
1066 |
+
# to infer the attention mask.
|
1067 |
+
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
1068 |
+
using_static_cache = isinstance(past_key_values, StaticCache)
|
1069 |
+
|
1070 |
+
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
1071 |
+
if self.config.attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
|
1072 |
+
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
1073 |
+
attention_mask,
|
1074 |
+
inputs_embeds=input_tensor,
|
1075 |
+
past_key_values_length=past_seen_tokens,
|
1076 |
+
is_training=self.training,
|
1077 |
+
):
|
1078 |
+
return None
|
1079 |
+
|
1080 |
+
dtype, device = input_tensor.dtype, input_tensor.device
|
1081 |
+
min_dtype = torch.finfo(dtype).min
|
1082 |
+
sequence_length = input_tensor.shape[1]
|
1083 |
+
if using_static_cache:
|
1084 |
+
target_length = past_key_values.get_max_length()
|
1085 |
+
else:
|
1086 |
+
target_length = (
|
1087 |
+
attention_mask.shape[-1]
|
1088 |
+
if isinstance(attention_mask, torch.Tensor)
|
1089 |
+
else past_seen_tokens + sequence_length + 1
|
1090 |
+
)
|
1091 |
+
|
1092 |
+
if attention_mask is not None and attention_mask.dim() == 4:
|
1093 |
+
# in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
|
1094 |
+
if attention_mask.max() != 0:
|
1095 |
+
raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
|
1096 |
+
causal_mask = attention_mask
|
1097 |
+
else:
|
1098 |
+
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
|
1099 |
+
if sequence_length != 1:
|
1100 |
+
if support_bf16_triu or dtype == torch.float32:
|
1101 |
+
causal_mask = torch.triu(causal_mask, diagonal=1)
|
1102 |
+
else:
|
1103 |
+
triu_mask = torch.triu(torch.ones(causal_mask.size(), device=device), diagonal=1).bool()
|
1104 |
+
causal_mask.masked_fill_(~triu_mask, 0)
|
1105 |
+
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
|
1106 |
+
causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
|
1107 |
+
if attention_mask is not None:
|
1108 |
+
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
1109 |
+
mask_length = attention_mask.shape[-1]
|
1110 |
+
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
|
1111 |
+
padding_mask = padding_mask == 0
|
1112 |
+
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
1113 |
+
padding_mask, min_dtype
|
1114 |
+
)
|
1115 |
+
if (
|
1116 |
+
self.config.attn_implementation == "sdpa"
|
1117 |
+
and attention_mask is not None
|
1118 |
+
and attention_mask.device.type == "cuda"
|
1119 |
+
and not output_attentions
|
1120 |
+
):
|
1121 |
+
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
1122 |
+
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
1123 |
+
# Details: https://github.com/pytorch/pytorch/issues/110213
|
1124 |
+
|