From 095ebfff9d19fec77a76b6cc7c65e3252f7ef02e Mon Sep 17 00:00:00 2001 From: YWMditto <46778265+YWMditto@users.noreply.github.com> Date: Mon, 6 Nov 2023 23:15:06 +0800 Subject: [PATCH] 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> --- .../internlm_model/configuration_internlm.py | 13 +- .../internlm_model/modeling_internlm.py | 214 +++++++++++++----- 2 files changed, 170 insertions(+), 57 deletions(-) diff --git a/tools/transformers/internlm_model/configuration_internlm.py b/tools/transformers/internlm_model/configuration_internlm.py index 298f913..a76c1b8 100644 --- a/tools/transformers/internlm_model/configuration_internlm.py +++ b/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 - model according to the specified arguments, defining the model architecture. Instantiating a configuration with the - defaults will yield a similar configuration to that of the InternLM-7B. + This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate + an InternLM model according to the specified arguments, defining the model architecture. Instantiating a + 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, diff --git a/tools/transformers/internlm_model/modeling_internlm.py b/tools/transformers/internlm_model/modeling_internlm.py index 269cdd2..e2d52ed 100644 --- a/tools/transformers/internlm_model/modeling_internlm.py +++ b/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 .configuration_internlm import InternLMConfig +from transformers.modeling_outputs import ( + 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] - sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] - q_embed = (q * cos) + (rotate_half(q) * sin) - k_embed = (k * cos) + (rotate_half(k) * sin) + cos = cos.unsqueeze(0).unsqueeze(0).expand(len(position_ids), -1, -1, -1) + 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): @@ -745,50 +851,56 @@ class InternLMForCausalLM(InternLMPreTrainedModel): for layer_past in past_key_values: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past - + def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = []): prompt = "" for record in history: prompt += f"""<|User|>:{record[0]}\n<|Bot|>:{record[1]}\n""" prompt += f"""<|User|>:{query}\n<|Bot|>:""" return tokenizer([prompt], return_tensors="pt") - + @torch.no_grad() - def chat(self, - tokenizer, - query: str, - history: List[Tuple[str, str]] = [], - streamer: Optional[BaseStreamer] = None, - max_new_tokens: int = 1024, - do_sample: bool = True, - temperature: float = 0.8, - top_p: float = 0.8, - **kwargs): + def chat( + self, + tokenizer, + query: str, + history: List[Tuple[str, str]] = [], + streamer: Optional[BaseStreamer] = None, + max_new_tokens: int = 1024, + do_sample: bool = True, + temperature: float = 0.8, + top_p: float = 0.8, + **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, - streamer=streamer, - max_new_tokens=max_new_tokens, - do_sample=do_sample, - temperature=temperature, - top_p=top_p, - **kwargs) - outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]):] + outputs = self.generate( + **inputs, + streamer=streamer, + max_new_tokens=max_new_tokens, + do_sample=do_sample, + temperature=temperature, + top_p=top_p, + **kwargs, + ) + outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :] response = tokenizer.decode(outputs, skip_special_tokens=True) response = response.split("")[0] history = history + [(query, response)] return response, history - + @torch.no_grad() - def stream_chat(self, - tokenizer, - query: str, - history: List[Tuple[str, str]] = [], - max_new_tokens: int = 1024, - do_sample: bool = True, - temperature: float = 0.8, - top_p: float = 0.8, - **kwargs): + def stream_chat( + self, + tokenizer, + query: str, + history: List[Tuple[str, str]] = [], + max_new_tokens: int = 1024, + do_sample: bool = True, + temperature: float = 0.8, + top_p: float = 0.8, + **kwargs, + ): """ Return a generator in format: (response, history) Eg. @@ -834,12 +946,12 @@ class InternLMForCausalLM(InternLMPreTrainedModel): tokenizer=tokenizer, query=query, streamer=ChatStreamer(tokenizer=tokenizer), - history=history, + history=history, max_new_tokens=max_new_tokens, do_sample=do_sample, temperature=temperature, top_p=top_p, - **kwargs + **kwargs, ) def consumer():