feat(tools): support dynamic ntk rope in transformers (#470)

* support dynamic ntk in transformers

* support dynamic ntk in transformers

* support dynamic ntk in transformers

* add rope doc

* add rotary config in configuration_internlm.py

---------

Co-authored-by: YWMditto <862779238@qq.com>

tools/transformers/internlm_model/configuration_internlm.py

@@ -19,9 +19,8 @@
 # limitations under the License.
 """ InternLM model configuration"""
 
-from transformers.utils import logging
 from transformers.configuration_utils import PretrainedConfig
-
+from transformers.utils import logging
 
 logger = logging.get_logger(__name__)
 
@@ -30,9 +29,9 @@ INTERNLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
 
 class InternLMConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate an InternLM
+    This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate
-    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    an InternLM model according to the specified arguments, defining the model architecture. Instantiating a
-    defaults will yield a similar configuration to that of the InternLM-7B.
+    configuration with the defaults will yield a similar configuration to that of the InternLM-7B.
 
     Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PretrainedConfig`] for more information.
@@ -81,7 +80,7 @@ class InternLMConfig(PretrainedConfig):
     model_type = "internlm"
     _auto_class = "AutoConfig"
 
-    def __init__(
+    def __init__(  # pylint: disable=W0102
         self,
         vocab_size=103168,
         hidden_size=4096,
@@ -98,6 +97,7 @@ class InternLMConfig(PretrainedConfig):
         eos_token_id=2,
         tie_word_embeddings=False,
         bias=True,
+        rotary={"base": 10000, "type": "dynamic"},  # pylint: disable=W0102
         **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -111,6 +111,7 @@ class InternLMConfig(PretrainedConfig):
         self.rms_norm_eps = rms_norm_eps
         self.use_cache = use_cache
         self.bias = bias
+        self.rotary = rotary
         super().__init__(
             pad_token_id=pad_token_id,
             bos_token_id=bos_token_id,

tools/transformers/internlm_model/modeling_internlm.py

@@ -19,26 +19,36 @@
 # limitations under the License.
 """ PyTorch InternLM model."""
 import math
+import queue
+import threading
 from typing import List, Optional, Tuple, Union
-import threading, queue
 
 import torch
 import torch.utils.checkpoint
 from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
 from transformers.activations import ACT2FN
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
-from transformers.modeling_utils import PreTrainedModel
 from transformers.generation.streamers import BaseStreamer
-from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from transformers.modeling_outputs import (
-from .configuration_internlm import InternLMConfig
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
 
+from .configuration_internlm import InternLMConfig
 
 logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "InternLMConfig"
 
+
 # Copied from transformers.models.bart.modeling_bart._make_causal_mask
 def _make_causal_mask(
     input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
@@ -73,6 +83,8 @@ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int]
 
 
 class InternLMRMSNorm(nn.Module):
+    """RMSNorm implemention."""
+
     def __init__(self, hidden_size, eps=1e-6):
         """
         InternLMRMSNorm is equivalent to T5LayerNorm
@@ -93,6 +105,14 @@ class InternLMRMSNorm(nn.Module):
 
 
 class InternLMRotaryEmbedding(torch.nn.Module):
+    """Implement InternLM's rotary embedding.
+
+    Args:
+        dim (int): Characteristic dimension of each self-attentional head.
+        max_position_embeddings (int, optional): Model's training length. Defaults to 2048.
+        base (int, optional): The rotation position encodes the rotation Angle base number. Defaults to 10000.
+        device (Any, optional): Running device. Defaults to None.
+    """
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()
         inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
@@ -124,6 +144,66 @@ class InternLMRotaryEmbedding(torch.nn.Module):
         )
 
 
+class InternLMDynamicNTKScalingRotaryEmbedding(torch.nn.Module):
+    """Implement InternLM's DyanmicNTK extrapolation method, thereby broadening the model support context to 16K.
+
+    Args:
+        dim (int): Characteristic dimension of each self-attentional head.
+        max_position_embeddings (int, optional): Model's training length. Defaults to 2048.
+        base (int, optional): The rotation position encodes the rotation Angle base number. Defaults to 10000.
+        device (Any, optional): Running device. Defaults to None.
+        scaling_factor (float, optional): NTK method extrapolation coefficient. Defaults to 1.0.
+    """
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+        self.register_buffer("inv_freq", inv_freq)
+        self.dim = dim
+        self.base = base
+        self.scaling_factor = scaling_factor
+
+        # Build here to make `torch.jit.trace` work.
+        self.max_position_embeddings = max_position_embeddings
+        self.max_seq_len_cached = max_position_embeddings
+        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+
+    def _update_cached(self, x, seq_len=None):
+        self.max_seq_len_cached = max(seq_len, self.max_position_embeddings)
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(x.device) / self.dim))
+        else:
+            inv_freq = self.inv_freq
+        t = torch.arange(self.max_seq_len_cached, device=inv_freq.device, dtype=inv_freq.dtype)
+        freqs = torch.einsum("i,j->ij", t, inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
+        if seq_len <= self.max_position_embeddings:
+            # Reset the tables if the sequence length has changed,
+            if self.max_seq_len_cached > self.max_position_embeddings:
+                self._update_cached(x, seq_len)
+        else:
+            self._update_cached(x, seq_len)
+
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
     x1 = x[..., : x.shape[-1] // 2]
@@ -135,10 +215,18 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
     # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
     cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
     sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
-    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    cos = cos.unsqueeze(0).unsqueeze(0).expand(len(position_ids), -1, -1, -1)
-    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin = sin.unsqueeze(0).unsqueeze(0).expand(len(position_ids), -1, -1, -1)
+    if q.size(2) == 1:
+        q_embed = (q * cos[:, :, -1, :]) + (rotate_half(q) * sin[:, :, -1, :])
+    else:
         q_embed = (q * cos) + (rotate_half(q) * sin)
+
+    if k.size(2) == 1:
+        k_embed = (k * cos[:, :, -1, :]) + (rotate_half(k) * sin[:, :, -1, :])
+    else:
         k_embed = (k * cos) + (rotate_half(k) * sin)
+
     return q_embed, k_embed
 
 
@@ -179,7 +267,25 @@ class InternLMAttention(nn.Module):
         self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias)
         self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias)
         self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
-        self.rotary_emb = InternLMRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+        self.rotary_emb = self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rotary["type"] == "origin":
+            self.rotary_emb = InternLMRotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.config.rotary["base"],
+            )
+        elif self.config.rotary["type"] == "dynamic":
+            self.rotary_emb = InternLMDynamicNTKScalingRotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.config.rotary["base"],
+                scaling_factor=self.config.rotary.get("scaling_factor", 1.0),
+            )
+        else:
+            raise ValueError("Currently we only support rotary embedding's type being one of ('origin', 'dynamic').")
+        return self.rotary_emb
 
     def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
         return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
@@ -199,20 +305,18 @@ class InternLMAttention(nn.Module):
         key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
         value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
 
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            kv_seq_len += past_key_value[0].shape[-2]
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-        # [bsz, nh, t, hd]
-
         if past_key_value is not None:
             # reuse k, v, self_attention
             key_states = torch.cat([past_key_value[0], key_states], dim=2)
             value_states = torch.cat([past_key_value[1], value_states], dim=2)
 
+        # print(use_cache)
         past_key_value = (key_states, value_states) if use_cache else None
 
+        kv_seq_len = key_states.shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
         attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
 
         if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
@@ -386,7 +490,8 @@ INTERNLM_INPUTS_DOCSTRING = r"""
             Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
             config.n_positions - 1]`.
             [What are position IDs?](../glossary#position-ids)
-        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
+            when `config.use_cache=True`):
             Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
             `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
             `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
@@ -423,6 +528,7 @@ class InternLMModel(InternLMPreTrainedModel):
     Args:
         config: InternLMConfig
     """
+
     _auto_class = "AutoModel"
 
     def __init__(self, config: InternLMConfig):
@@ -754,7 +860,8 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
         return tokenizer([prompt], return_tensors="pt")
 
     @torch.no_grad()
-    def chat(self, 
+    def chat(
+        self,
         tokenizer,
         query: str,
         history: List[Tuple[str, str]] = [],
@@ -763,16 +870,19 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
         do_sample: bool = True,
         temperature: float = 0.8,
         top_p: float = 0.8,
-             **kwargs):
+        **kwargs,
+    ):
         inputs = self.build_inputs(tokenizer, query, history)
         inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
-        outputs = self.generate(**inputs, 
+        outputs = self.generate(
+            **inputs,
             streamer=streamer,
             max_new_tokens=max_new_tokens,
             do_sample=do_sample,
             temperature=temperature,
             top_p=top_p,
-                                **kwargs)
+            **kwargs,
+        )
         outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
         response = tokenizer.decode(outputs, skip_special_tokens=True)
         response = response.split("<eoa>")[0]
@@ -780,7 +890,8 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
         return response, history
 
     @torch.no_grad()
-    def stream_chat(self, 
+    def stream_chat(
+        self,
         tokenizer,
         query: str,
         history: List[Tuple[str, str]] = [],
@@ -788,7 +899,8 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
         do_sample: bool = True,
         temperature: float = 0.8,
         top_p: float = 0.8,
-                    **kwargs):
+        **kwargs,
+    ):
         """
         Return a generator in format: (response, history)
         Eg.
@@ -839,7 +951,7 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
                 do_sample=do_sample,
                 temperature=temperature,
                 top_p=top_p,
-                **kwargs
+                **kwargs,
             )
 
         def consumer():