From 31911b6300c3447d4e147123a8a920f42b682154 Mon Sep 17 00:00:00 2001 From: Gaurav Kumbhat Date: Fri, 9 Feb 2024 13:01:41 -0600 Subject: [PATCH] Revert ":package: Update transformers to min of 4.36.0 to get new caching module" Signed-off-by: gkumbhat --- .../text_generation/peft_prompt_tuning.py | 5 +- .../pretrained_model/hf_auto_causal_lm.py | 5 - .../text_generation/granite_modeling_llama.py | 1892 ----------------- pyproject.toml | 8 +- 4 files changed, 8 insertions(+), 1902 deletions(-) delete mode 100644 caikit_nlp/toolkit/text_generation/granite_modeling_llama.py diff --git a/caikit_nlp/modules/text_generation/peft_prompt_tuning.py b/caikit_nlp/modules/text_generation/peft_prompt_tuning.py index 1a01bc08..176e85bc 100644 --- a/caikit_nlp/modules/text_generation/peft_prompt_tuning.py +++ b/caikit_nlp/modules/text_generation/peft_prompt_tuning.py @@ -426,7 +426,7 @@ def train( # Remove _name_or_path field as a model can be # saved in different location but still same - base_model_config.pop("_name_or_path", None) + del base_model_config["_name_or_path"] error.value_check( "", "_name_or_path" not in base_model_config, @@ -571,8 +571,7 @@ def load( if peft_config.task_type == "CAUSAL_LM": # get the transformers Causal LM model base_model = AutoModelForCausalLM.from_pretrained( - peft_config.base_model_name_or_path, - torch_dtype=torch_dtype, + peft_config.base_model_name_or_path ) # get the PEFT causal LM model model = PeftModel.from_pretrained(base_model, model_config) diff --git a/caikit_nlp/resources/pretrained_model/hf_auto_causal_lm.py b/caikit_nlp/resources/pretrained_model/hf_auto_causal_lm.py index 7cc62faa..fc09734e 100644 --- a/caikit_nlp/resources/pretrained_model/hf_auto_causal_lm.py +++ b/caikit_nlp/resources/pretrained_model/hf_auto_causal_lm.py @@ -35,11 +35,6 @@ # Local from ...data_model import GenerationTrainRecord, PromptOutputModelType - -# Note: Below module is imported to allow loading of fm stack sphinx models -from ...toolkit.text_generation import ( # pylint: disable=unused-import - granite_modeling_llama, -) from ...toolkit.verbalizer_utils import render_verbalizer from .base import PretrainedModelBase diff --git a/caikit_nlp/toolkit/text_generation/granite_modeling_llama.py b/caikit_nlp/toolkit/text_generation/granite_modeling_llama.py deleted file mode 100644 index 89ffb669..00000000 --- a/caikit_nlp/toolkit/text_generation/granite_modeling_llama.py +++ /dev/null @@ -1,1892 +0,0 @@ -# pylint: skip-file - -# coding=utf-8 -# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. -# -# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX -# and OPT implementations in this library. It has been modified from its -# original forms to accommodate minor architectural differences compared -# to GPT-NeoX and OPT used by the Meta AI team that trained the model. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" PyTorch LLaMA model.""" -# Standard -from pathlib import Path -from typing import List, Optional, Tuple, Union -import json -import math -import os -import re -import warnings - -# Third Party -from torch import nn -from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss -from transformers import AutoConfig, AutoModelForCausalLM -from transformers.activations import ACT2FN -from transformers.cache_utils import Cache, DynamicCache -from transformers.configuration_utils import PretrainedConfig -from transformers.modeling_attn_mask_utils import ( - AttentionMaskConverter, - _prepare_4d_attention_mask, - _prepare_4d_causal_attention_mask, - _prepare_4d_causal_attention_mask_for_sdpa, -) -from transformers.modeling_outputs import ( - BaseModelOutputWithPast, - CausalLMOutputWithPast, - SequenceClassifierOutputWithPast, -) -from transformers.modeling_utils import PreTrainedModel -from transformers.models.llama.configuration_llama import LlamaConfig -from transformers.pytorch_utils import ( - ALL_LAYERNORM_LAYERS, - is_torch_greater_or_equal_than_1_13, -) -from transformers.utils import ( - add_start_docstrings, - add_start_docstrings_to_model_forward, - is_flash_attn_2_available, - is_flash_attn_greater_or_equal_2_10, - logging, - replace_return_docstrings, -) -from transformers.utils.import_utils import is_torch_fx_available -import safetensors -import torch -import torch.nn.functional as F -import torch.utils.checkpoint - -if is_flash_attn_2_available(): - # Third Party - from flash_attn import flash_attn_func, flash_attn_varlen_func - from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa - - -# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph. -# It means that the function will not be traced through and simply appear as a node in the graph. -if is_torch_fx_available(): - if not is_torch_greater_or_equal_than_1_13: - # Third Party - import torch.fx - - _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) - - -logger = logging.get_logger(__name__) - -_CONFIG_FOR_DOC = "BiasedLlamaConfig" - - -def _get_unpad_data(attention_mask): - seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) - indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() - max_seqlen_in_batch = seqlens_in_batch.max().item() - cu_seqlens = F.pad( - torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0) - ) - return ( - indices, - cu_seqlens, - max_seqlen_in_batch, - ) - - -def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): - warnings.warn( - "Calling `transformers.models.llama.modeling_llama._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask" - ) - return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len) - - -def _make_causal_mask( - input_ids_shape: torch.Size, - dtype: torch.dtype, - device: torch.device, - past_key_values_length: int = 0, -): - warnings.warn( - "Calling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_mask" - ) - return AttentionMaskConverter._make_causal_mask( - input_ids_shape=input_ids_shape, - dtype=dtype, - device=device, - past_key_values_length=past_key_values_length, - ) - - -class BiasedLlamaConfig(LlamaConfig): - model_type = "gpt_megatron" - - -class LlamaRMSNorm(nn.Module): - def __init__(self, hidden_size, eps=1e-6): - """ - LlamaRMSNorm is equivalent to T5LayerNorm - """ - super().__init__() - self.weight = nn.Parameter(torch.ones(hidden_size)) - self.variance_epsilon = eps - - def forward(self, hidden_states): - input_dtype = hidden_states.dtype - hidden_states = hidden_states.to(torch.float32) - variance = hidden_states.pow(2).mean(-1, keepdim=True) - hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) - return self.weight * hidden_states.to(input_dtype) - - -ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm) - - -class LlamaRotaryEmbedding(nn.Module): - def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): - super().__init__() - - self.dim = dim - self.max_position_embeddings = max_position_embeddings - self.base = base - inv_freq = 1.0 / ( - self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) - ) - self.register_buffer("inv_freq", inv_freq, persistent=False) - - # Build here to make `torch.jit.trace` work. - self._set_cos_sin_cache( - seq_len=max_position_embeddings, - device=self.inv_freq.device, - dtype=torch.get_default_dtype(), - ) - - def _set_cos_sin_cache(self, seq_len, device, dtype): - self.max_seq_len_cached = seq_len - t = torch.arange( - self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype - ) - - freqs = torch.outer(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().to(dtype), persistent=False) - self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) - - def forward(self, x, seq_len=None): - # x: [bs, num_attention_heads, seq_len, head_size] - if seq_len > self.max_seq_len_cached: - self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) - - return ( - self.cos_cached[:seq_len].to(dtype=x.dtype), - self.sin_cached[:seq_len].to(dtype=x.dtype), - ) - - -class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding): - """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" - - def __init__( - self, - dim, - max_position_embeddings=2048, - base=10000, - device=None, - scaling_factor=1.0, - ): - self.scaling_factor = scaling_factor - super().__init__(dim, max_position_embeddings, base, device) - - def _set_cos_sin_cache(self, seq_len, device, dtype): - self.max_seq_len_cached = seq_len - t = torch.arange( - self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype - ) - t = t / self.scaling_factor - - freqs = torch.outer(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().to(dtype), persistent=False) - self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) - - -class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding): - """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" - - def __init__( - self, - dim, - max_position_embeddings=2048, - base=10000, - device=None, - scaling_factor=1.0, - ): - self.scaling_factor = scaling_factor - super().__init__(dim, max_position_embeddings, base, device) - - def _set_cos_sin_cache(self, seq_len, device, dtype): - self.max_seq_len_cached = seq_len - - 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(device) / self.dim) - ) - self.register_buffer("inv_freq", inv_freq, persistent=False) - - t = torch.arange( - self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype - ) - - freqs = torch.outer(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().to(dtype), persistent=False) - self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) - - -def rotate_half(x): - """Rotates half the hidden dims of the input.""" - x1 = x[..., : x.shape[-1] // 2] - x2 = x[..., x.shape[-1] // 2 :] - return torch.cat((-x2, x1), dim=-1) - - -def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): - """Applies Rotary Position Embedding to the query and key tensors. - - Args: - q (`torch.Tensor`): The query tensor. - k (`torch.Tensor`): The key tensor. - cos (`torch.Tensor`): The cosine part of the rotary embedding. - sin (`torch.Tensor`): The sine part of the rotary embedding. - position_ids (`torch.Tensor`): - The position indices of the tokens corresponding to the query and key tensors. For example, this can be - used to pass offsetted position ids when working with a KV-cache. - unsqueeze_dim (`int`, *optional*, defaults to 1): - The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and - sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note - that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and - k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes - cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have - the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. - Returns: - `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. - """ - cos = cos[position_ids].unsqueeze(unsqueeze_dim) - sin = sin[position_ids].unsqueeze(unsqueeze_dim) - q_embed = (q * cos) + (rotate_half(q) * sin) - k_embed = (k * cos) + (rotate_half(k) * sin) - return q_embed, k_embed - - -class LlamaBiasedMLP(nn.Module): - def __init__(self, config): - super().__init__() - self.config = config - self.hidden_size = config.hidden_size - self.intermediate_size = config.intermediate_size - self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True) - self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True) - self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=True) - self.act_fn = ACT2FN[config.hidden_act] - - def forward(self, x): - if self.config.pretraining_tp > 1: - slice = self.intermediate_size // self.config.pretraining_tp - gate_proj_slices = self.gate_proj.weight.split(slice, dim=0) - up_proj_slices = self.up_proj.weight.split(slice, dim=0) - down_proj_slices = self.down_proj.weight.split(slice, dim=1) - - gate_proj = torch.cat( - [ - F.linear(x, gate_proj_slices[i]) - for i in range(self.config.pretraining_tp) - ], - dim=-1, - ) - up_proj = torch.cat( - [ - F.linear(x, up_proj_slices[i]) - for i in range(self.config.pretraining_tp) - ], - dim=-1, - ) - - intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2) - down_proj = [ - F.linear(intermediate_states[i], down_proj_slices[i]) - for i in range(self.config.pretraining_tp) - ] - down_proj = sum(down_proj) - else: - down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) - - return down_proj - - -def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: - """ - This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, - num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) - """ - batch, num_key_value_heads, slen, head_dim = hidden_states.shape - if n_rep == 1: - return hidden_states - hidden_states = hidden_states[:, :, None, :, :].expand( - batch, num_key_value_heads, n_rep, slen, head_dim - ) - return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) - - -class LlamaAttention(nn.Module): - """Multi-headed attention from 'Attention Is All You Need' paper""" - - def __init__(self, config: BiasedLlamaConfig, layer_idx: Optional[int] = None): - super().__init__() - self.config = config - self.layer_idx = layer_idx - if layer_idx is None: - logger.warning_once( - f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " - "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " - "when creating this class." - ) - - self.attention_dropout = config.attention_dropout - self.hidden_size = config.hidden_size - self.num_heads = config.num_attention_heads - self.head_dim = self.hidden_size // self.num_heads - self.num_key_value_heads = config.num_key_value_heads - self.num_key_value_groups = self.num_heads // self.num_key_value_heads - self.max_position_embeddings = config.max_position_embeddings - self.rope_theta = config.rope_theta - self.is_causal = True - - if (self.head_dim * self.num_heads) != self.hidden_size: - raise ValueError( - f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" - f" and `num_heads`: {self.num_heads})." - ) - - self.q_proj = nn.Linear( - self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias - ) - self.k_proj = nn.Linear( - self.hidden_size, - self.num_key_value_heads * self.head_dim, - bias=config.attention_bias, - ) - self.v_proj = nn.Linear( - self.hidden_size, - self.num_key_value_heads * self.head_dim, - bias=config.attention_bias, - ) - self.o_proj = nn.Linear( - self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias - ) - self._init_rope() - - def _init_rope(self): - if self.config.rope_scaling is None: - self.rotary_emb = LlamaRotaryEmbedding( - self.head_dim, - max_position_embeddings=self.max_position_embeddings, - base=self.rope_theta, - ) - else: - scaling_type = self.config.rope_scaling["type"] - scaling_factor = self.config.rope_scaling["factor"] - if scaling_type == "linear": - self.rotary_emb = LlamaLinearScalingRotaryEmbedding( - self.head_dim, - max_position_embeddings=self.max_position_embeddings, - scaling_factor=scaling_factor, - base=self.rope_theta, - ) - elif scaling_type == "dynamic": - self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding( - self.head_dim, - max_position_embeddings=self.max_position_embeddings, - scaling_factor=scaling_factor, - base=self.rope_theta, - ) - else: - raise ValueError(f"Unknown RoPE scaling type {scaling_type}") - - 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() - ) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.LongTensor] = None, - past_key_value: Optional[Cache] = None, - output_attentions: bool = False, - use_cache: bool = False, - **kwargs, - ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: - if "padding_mask" in kwargs: - warnings.warn( - "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" - ) - - bsz, q_len, _ = hidden_states.size() - - if self.config.pretraining_tp > 1: - key_value_slicing = ( - self.num_key_value_heads * self.head_dim - ) // self.config.pretraining_tp - query_slices = self.q_proj.weight.split( - (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0 - ) - key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) - value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) - - query_states = [ - F.linear(hidden_states, query_slices[i]) - for i in range(self.config.pretraining_tp) - ] - query_states = torch.cat(query_states, dim=-1) - - key_states = [ - F.linear(hidden_states, key_slices[i]) - for i in range(self.config.pretraining_tp) - ] - key_states = torch.cat(key_states, dim=-1) - - value_states = [ - F.linear(hidden_states, value_slices[i]) - for i in range(self.config.pretraining_tp) - ] - value_states = torch.cat(value_states, dim=-1) - - else: - query_states = self.q_proj(hidden_states) - key_states = self.k_proj(hidden_states) - value_states = self.v_proj(hidden_states) - - query_states = query_states.view( - bsz, q_len, self.num_heads, self.head_dim - ).transpose(1, 2) - key_states = key_states.view( - bsz, q_len, self.num_key_value_heads, self.head_dim - ).transpose(1, 2) - value_states = value_states.view( - bsz, q_len, self.num_key_value_heads, self.head_dim - ).transpose(1, 2) - - kv_seq_len = key_states.shape[-2] - if past_key_value is not None: - if self.layer_idx is None: - raise ValueError( - f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " - "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " - "with a layer index." - ) - kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) - 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 - ) - - if past_key_value is not None: - cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models - key_states, value_states = past_key_value.update( - key_states, value_states, self.layer_idx, cache_kwargs - ) - - key_states = repeat_kv(key_states, self.num_key_value_groups) - value_states = repeat_kv(value_states, self.num_key_value_groups) - - 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): - raise ValueError( - f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" - f" {attn_weights.size()}" - ) - - if attention_mask is not None: - if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): - raise ValueError( - f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" - ) - attn_weights = attn_weights + attention_mask - - # upcast attention to fp32 - attn_weights = nn.functional.softmax( - attn_weights, dim=-1, dtype=torch.float32 - ).to(query_states.dtype) - attn_weights = nn.functional.dropout( - attn_weights, p=self.attention_dropout, training=self.training - ) - attn_output = torch.matmul(attn_weights, value_states) - - if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): - raise ValueError( - f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" - f" {attn_output.size()}" - ) - - attn_output = attn_output.transpose(1, 2).contiguous() - - attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) - - if self.config.pretraining_tp > 1: - attn_output = attn_output.split( - self.hidden_size // self.config.pretraining_tp, dim=2 - ) - o_proj_slices = self.o_proj.weight.split( - self.hidden_size // self.config.pretraining_tp, dim=1 - ) - attn_output = sum( - [ - F.linear(attn_output[i], o_proj_slices[i]) - for i in range(self.config.pretraining_tp) - ] - ) - else: - attn_output = self.o_proj(attn_output) - - if not output_attentions: - attn_weights = None - - return attn_output, attn_weights, past_key_value - - -class LlamaFlashAttention2(LlamaAttention): - """ - Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays - untouched. The only required change would be on the forward pass where it needs to correctly call the public API of - flash attention and deal with padding tokens in case the input contains any of them. - """ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. - # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. - # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). - self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - past_key_value: Optional[Cache] = None, - output_attentions: bool = False, - use_cache: bool = False, - **kwargs, - ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: - # LlamaFlashAttention2 attention does not support output_attentions - if "padding_mask" in kwargs: - warnings.warn( - "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" - ) - - # overwrite attention_mask with padding_mask - attention_mask = kwargs.pop("padding_mask") - - output_attentions = False - - bsz, q_len, _ = hidden_states.size() - - query_states = self.q_proj(hidden_states) - key_states = self.k_proj(hidden_states) - value_states = self.v_proj(hidden_states) - - # Flash attention requires the input to have the shape - # batch_size x seq_length x head_dim x hidden_dim - # therefore we just need to keep the original shape - query_states = query_states.view( - bsz, q_len, self.num_heads, self.head_dim - ).transpose(1, 2) - key_states = key_states.view( - bsz, q_len, self.num_key_value_heads, self.head_dim - ).transpose(1, 2) - value_states = value_states.view( - bsz, q_len, self.num_key_value_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.get_usable_length(kv_seq_len, self.layer_idx) - 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 - ) - - if past_key_value is not None: - cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models - key_states, value_states = past_key_value.update( - key_states, value_states, self.layer_idx, cache_kwargs - ) - - # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache - # to be able to avoid many of these transpose/reshape/view. - query_states = query_states.transpose(1, 2) - key_states = key_states.transpose(1, 2) - value_states = value_states.transpose(1, 2) - - dropout_rate = self.attention_dropout if self.training else 0.0 - - # In PEFT, usually we cast the layer norms in float32 for training stability reasons - # therefore the input hidden states gets silently casted in float32. Hence, we need - # cast them back in the correct dtype just to be sure everything works as expected. - # This might slowdown training & inference so it is recommended to not cast the LayerNorms - # in fp32. (LlamaRMSNorm handles it correctly) - - input_dtype = query_states.dtype - if input_dtype == torch.float32: - if torch.is_autocast_enabled(): - target_dtype = torch.get_autocast_gpu_dtype() - # Handle the case where the model is quantized - elif hasattr(self.config, "_pre_quantization_dtype"): - target_dtype = self.config._pre_quantization_dtype - else: - target_dtype = self.q_proj.weight.dtype - - logger.warning_once( - f"The input hidden states seems to be silently casted in float32, this might be related to" - f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" - f" {target_dtype}." - ) - - query_states = query_states.to(target_dtype) - key_states = key_states.to(target_dtype) - value_states = value_states.to(target_dtype) - - attn_output = self._flash_attention_forward( - query_states, - key_states, - value_states, - attention_mask, - q_len, - dropout=dropout_rate, - ) - - attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() - attn_output = self.o_proj(attn_output) - - if not output_attentions: - attn_weights = None - - return attn_output, attn_weights, past_key_value - - def _flash_attention_forward( - self, - query_states, - key_states, - value_states, - attention_mask, - query_length, - dropout=0.0, - softmax_scale=None, - ): - """ - Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token - first unpad the input, then computes the attention scores and pad the final attention scores. - - Args: - query_states (`torch.Tensor`): - Input query states to be passed to Flash Attention API - key_states (`torch.Tensor`): - Input key states to be passed to Flash Attention API - value_states (`torch.Tensor`): - Input value states to be passed to Flash Attention API - attention_mask (`torch.Tensor`): - The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the - position of padding tokens and 1 for the position of non-padding tokens. - dropout (`int`, *optional*): - Attention dropout - softmax_scale (`float`, *optional*): - The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) - """ - if not self._flash_attn_uses_top_left_mask: - causal = self.is_causal - else: - # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. - causal = self.is_causal and query_length != 1 - - # Contains at least one padding token in the sequence - if attention_mask is not None: - batch_size = query_states.shape[0] - ( - query_states, - key_states, - value_states, - indices_q, - cu_seq_lens, - max_seq_lens, - ) = self._upad_input( - query_states, key_states, value_states, attention_mask, query_length - ) - - cu_seqlens_q, cu_seqlens_k = cu_seq_lens - max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens - - attn_output_unpad = flash_attn_varlen_func( - query_states, - key_states, - value_states, - cu_seqlens_q=cu_seqlens_q, - cu_seqlens_k=cu_seqlens_k, - max_seqlen_q=max_seqlen_in_batch_q, - max_seqlen_k=max_seqlen_in_batch_k, - dropout_p=dropout, - softmax_scale=softmax_scale, - causal=causal, - ) - - attn_output = pad_input( - attn_output_unpad, indices_q, batch_size, query_length - ) - else: - attn_output = flash_attn_func( - query_states, - key_states, - value_states, - dropout, - softmax_scale=softmax_scale, - causal=causal, - ) - - return attn_output - - def _upad_input( - self, query_layer, key_layer, value_layer, attention_mask, query_length - ): - indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) - batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape - - key_layer = index_first_axis( - key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - value_layer = index_first_axis( - value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - if query_length == kv_seq_len: - query_layer = index_first_axis( - query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), - indices_k, - ) - cu_seqlens_q = cu_seqlens_k - max_seqlen_in_batch_q = max_seqlen_in_batch_k - indices_q = indices_k - elif query_length == 1: - max_seqlen_in_batch_q = 1 - cu_seqlens_q = torch.arange( - batch_size + 1, dtype=torch.int32, device=query_layer.device - ) # There is a memcpy here, that is very bad. - indices_q = cu_seqlens_q[:-1] - query_layer = query_layer.squeeze(1) - else: - # The -q_len: slice assumes left padding. - attention_mask = attention_mask[:, -query_length:] - query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( - query_layer, attention_mask - ) - - return ( - query_layer, - key_layer, - value_layer, - indices_q, - (cu_seqlens_q, cu_seqlens_k), - (max_seqlen_in_batch_q, max_seqlen_in_batch_k), - ) - - -class LlamaSdpaAttention(LlamaAttention): - """ - Llama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from - `LlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to - SDPA API. - """ - - # Adapted from LlamaAttention.forward - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.LongTensor] = None, - past_key_value: Optional[Cache] = None, - output_attentions: bool = False, - use_cache: bool = False, - ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: - if output_attentions: - # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. - logger.warning_once( - "LlamaModel is using LlamaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " - 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' - ) - return super().forward( - hidden_states=hidden_states, - attention_mask=attention_mask, - position_ids=position_ids, - past_key_value=past_key_value, - output_attentions=output_attentions, - use_cache=use_cache, - ) - - bsz, q_len, _ = hidden_states.size() - - query_states = self.q_proj(hidden_states) - key_states = self.k_proj(hidden_states) - value_states = self.v_proj(hidden_states) - - query_states = query_states.view( - bsz, q_len, self.num_heads, self.head_dim - ).transpose(1, 2) - key_states = key_states.view( - bsz, q_len, self.num_key_value_heads, self.head_dim - ).transpose(1, 2) - value_states = value_states.view( - bsz, q_len, self.num_key_value_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.get_usable_length(kv_seq_len, self.layer_idx) - 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 - ) - - if past_key_value is not None: - cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models - key_states, value_states = past_key_value.update( - key_states, value_states, self.layer_idx, cache_kwargs - ) - - key_states = repeat_kv(key_states, self.num_key_value_groups) - value_states = repeat_kv(value_states, self.num_key_value_groups) - - if attention_mask is not None: - if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): - raise ValueError( - f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" - ) - - # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, - # Reference: https://github.com/pytorch/pytorch/issues/112577. - if query_states.device.type == "cuda" and attention_mask is not None: - query_states = query_states.contiguous() - key_states = key_states.contiguous() - value_states = value_states.contiguous() - - attn_output = torch.nn.functional.scaled_dot_product_attention( - query_states, - key_states, - value_states, - attn_mask=attention_mask, - dropout_p=self.attention_dropout if self.training else 0.0, - # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. - is_causal=self.is_causal and attention_mask is None and q_len > 1, - ) - - attn_output = attn_output.transpose(1, 2).contiguous() - attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) - - attn_output = self.o_proj(attn_output) - - return attn_output, None, past_key_value - - -LLAMA_ATTENTION_CLASSES = { - "eager": LlamaAttention, - "flash_attention_2": LlamaFlashAttention2, - "sdpa": LlamaSdpaAttention, -} - - -class LlamaDecoderLayer(nn.Module): - def __init__(self, config: BiasedLlamaConfig, layer_idx: int): - super().__init__() - self.hidden_size = config.hidden_size - - self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation]( - config=config, layer_idx=layer_idx - ) - - self.mlp = LlamaBiasedMLP(config) - self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) - self.post_attention_layernorm = LlamaRMSNorm( - config.hidden_size, eps=config.rms_norm_eps - ) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.LongTensor] = None, - past_key_value: Optional[Tuple[torch.Tensor]] = None, - output_attentions: Optional[bool] = False, - use_cache: Optional[bool] = False, - **kwargs, - ) -> Tuple[ - torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] - ]: - """ - Args: - hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` - attention_mask (`torch.FloatTensor`, *optional*): - attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, - query_sequence_length, key_sequence_length)` if default attention is used. - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under - returned tensors for more detail. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding - (see `past_key_values`). - past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states - """ - if "padding_mask" in kwargs: - warnings.warn( - "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" - ) - - residual = hidden_states - - hidden_states = self.input_layernorm(hidden_states) - - # Self Attention - hidden_states, self_attn_weights, present_key_value = self.self_attn( - hidden_states=hidden_states, - attention_mask=attention_mask, - position_ids=position_ids, - past_key_value=past_key_value, - output_attentions=output_attentions, - use_cache=use_cache, - **kwargs, - ) - hidden_states = residual + hidden_states - - # Fully Connected - residual = hidden_states - hidden_states = self.post_attention_layernorm(hidden_states) - hidden_states = self.mlp(hidden_states) - hidden_states = residual + hidden_states - - outputs = (hidden_states,) - - if output_attentions: - outputs += (self_attn_weights,) - - if use_cache: - outputs += (present_key_value,) - - return outputs - - -LLAMA_START_DOCSTRING = r""" - This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the - library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads - etc.) - - This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. - Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage - and behavior. - - Parameters: - config ([`BiasedLlamaConfig`]): - Model configuration class with all the parameters of the model. Initializing with a config file does not - load the weights associated with the model, only the configuration. Check out the - [`~PreTrainedModel.from_pretrained`] method to load the model weights. -""" - - -@add_start_docstrings( - "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", - LLAMA_START_DOCSTRING, -) -class LlamaPreTrainedModel(PreTrainedModel): - config_class = BiasedLlamaConfig - - base_model_prefix = "model" - supports_gradient_checkpointing = True - _no_split_modules = ["LlamaDecoderLayer"] - _skip_keys_device_placement = "past_key_values" - _supports_flash_attn_2 = True - _supports_sdpa = True - _supports_cache_class = True - - def _init_weights(self, module): - std = self.config.initializer_range - if isinstance(module, nn.Linear): - module.weight.data.normal_(mean=0.0, std=std) - if module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.Embedding): - module.weight.data.normal_(mean=0.0, std=std) - if module.padding_idx is not None: - module.weight.data[module.padding_idx].zero_() - - -LLAMA_INPUTS_DOCSTRING = r""" - Args: - input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): - Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide - it. - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - [What are input IDs?](../glossary#input-ids) - attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - - [What are attention masks?](../glossary#attention-mask) - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - If `past_key_values` is used, optionally only the last `input_ids` have to be input (see - `past_key_values`). - - If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] - and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more - information on the default strategy. - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - 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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): - Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention - blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` - returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. - - Two formats are allowed: - - a [`~cache_utils.Cache`] instance; - - 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)`). This is also known as the legacy - cache format. - - The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the - legacy cache format will be returned. - - If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't - have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` - of shape `(batch_size, sequence_length)`. - inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This - is useful if you want more control over how to convert `input_ids` indices into associated vectors than the - model's internal embedding lookup matrix. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see - `past_key_values`). - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned - tensors for more detail. - output_hidden_states (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for - more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. -""" - - -@add_start_docstrings( - "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", - LLAMA_START_DOCSTRING, -) -class LlamaModel(LlamaPreTrainedModel): - """ - Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] - - Args: - config: BiasedLlamaConfig - """ - - def __init__(self, config: BiasedLlamaConfig): - super().__init__(config) - self.padding_idx = config.pad_token_id - self.vocab_size = config.vocab_size - - self.embed_tokens = nn.Embedding( - config.vocab_size, config.hidden_size, self.padding_idx - ) - self.layers = nn.ModuleList( - [ - LlamaDecoderLayer(config, layer_idx) - for layer_idx in range(config.num_hidden_layers) - ] - ) - self._use_sdpa = config._attn_implementation == "sdpa" - self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" - self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) - - self.gradient_checkpointing = False - # Initialize weights and apply final processing - self.post_init() - - def get_input_embeddings(self): - return self.embed_tokens - - def set_input_embeddings(self, value): - self.embed_tokens = value - - @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) - def forward( - self, - input_ids: torch.LongTensor = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple, BaseModelOutputWithPast]: - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - use_cache = use_cache if use_cache is not None else self.config.use_cache - - return_dict = ( - return_dict if return_dict is not None else self.config.use_return_dict - ) - - # retrieve input_ids and inputs_embeds - if input_ids is not None and inputs_embeds is not None: - raise ValueError( - "You cannot specify both input_ids and inputs_embeds at the same time" - ) - elif input_ids is not None: - batch_size, seq_length = input_ids.shape[:2] - elif inputs_embeds is not None: - batch_size, seq_length = inputs_embeds.shape[:2] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - past_key_values_length = 0 - if use_cache: - use_legacy_cache = not isinstance(past_key_values, Cache) - if use_legacy_cache: - past_key_values = DynamicCache.from_legacy_cache(past_key_values) - past_key_values_length = past_key_values.get_usable_length(seq_length) - - if position_ids is None: - device = input_ids.device if input_ids is not None else inputs_embeds.device - position_ids = torch.arange( - past_key_values_length, - seq_length + past_key_values_length, - dtype=torch.long, - device=device, - ) - position_ids = position_ids.unsqueeze(0) - - if inputs_embeds is None: - inputs_embeds = self.embed_tokens(input_ids) - - if self._use_flash_attention_2: - # 2d mask is passed through the layers - attention_mask = ( - attention_mask - if (attention_mask is not None and 0 in attention_mask) - else None - ) - elif self._use_sdpa and not output_attentions: - # output_attentions=True can not be supported when using SDPA, and we fall back on - # the manual implementation that requires a 4D causal mask in all cases. - attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( - attention_mask, - (batch_size, seq_length), - inputs_embeds, - past_key_values_length, - ) - else: - # 4d mask is passed through the layers - attention_mask = _prepare_4d_causal_attention_mask( - attention_mask, - (batch_size, seq_length), - inputs_embeds, - past_key_values_length, - ) - - # embed positions - hidden_states = inputs_embeds - - # decoder layers - all_hidden_states = () if output_hidden_states else None - all_self_attns = () if output_attentions else None - next_decoder_cache = None - - for decoder_layer in self.layers: - if output_hidden_states: - all_hidden_states += (hidden_states,) - - if self.gradient_checkpointing and self.training: - layer_outputs = self._gradient_checkpointing_func( - decoder_layer.__call__, - hidden_states, - attention_mask, - position_ids, - past_key_values, - output_attentions, - use_cache, - ) - else: - layer_outputs = decoder_layer( - hidden_states, - attention_mask=attention_mask, - position_ids=position_ids, - past_key_value=past_key_values, - output_attentions=output_attentions, - use_cache=use_cache, - ) - - hidden_states = layer_outputs[0] - - if use_cache: - next_decoder_cache = layer_outputs[2 if output_attentions else 1] - - if output_attentions: - all_self_attns += (layer_outputs[1],) - - hidden_states = self.norm(hidden_states) - - # add hidden states from the last decoder layer - if output_hidden_states: - all_hidden_states += (hidden_states,) - - next_cache = None - if use_cache: - next_cache = ( - next_decoder_cache.to_legacy_cache() - if use_legacy_cache - else next_decoder_cache - ) - if not return_dict: - return tuple( - v - for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] - if v is not None - ) - return BaseModelOutputWithPast( - last_hidden_state=hidden_states, - past_key_values=next_cache, - hidden_states=all_hidden_states, - attentions=all_self_attns, - ) - - -class BiasedLlamaForCausalLM(LlamaPreTrainedModel): - _tied_weights_keys = ["lm_head.weight"] - - def __init__(self, config): - super().__init__(config) - self.model = LlamaModel(config) - self.vocab_size = config.vocab_size - self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - # Initialize weights and apply final processing - self.post_init() - - def get_input_embeddings(self): - return self.model.embed_tokens - - def set_input_embeddings(self, value): - self.model.embed_tokens = value - - def get_output_embeddings(self): - return self.lm_head - - def set_output_embeddings(self, new_embeddings): - self.lm_head = new_embeddings - - def set_decoder(self, decoder): - self.model = decoder - - def get_decoder(self): - return self.model - - @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) - @replace_return_docstrings( - output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC - ) - def forward( - self, - input_ids: torch.LongTensor = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple, CausalLMOutputWithPast]: - r""" - Args: - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., - config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored - (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. - - Returns: - - Example: - - ```python - >>> from transformers import AutoTokenizer, LlamaForCausalLM - - >>> model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") - >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf") - - >>> prompt = "Hey, are you conscious? Can you talk to me?" - >>> inputs = tokenizer(prompt, return_tensors="pt") - - >>> # Generate - >>> generate_ids = model.generate(inputs.input_ids, max_length=30) - >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] - "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." - ```""" - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.use_return_dict - ) - - # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) - outputs = self.model( - input_ids=input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - hidden_states = outputs[0] - if self.config.pretraining_tp > 1: - lm_head_slices = self.lm_head.weight.split( - self.vocab_size // self.config.pretraining_tp, dim=0 - ) - logits = [ - F.linear(hidden_states, lm_head_slices[i]) - for i in range(self.config.pretraining_tp) - ] - logits = torch.cat(logits, dim=-1) - else: - logits = self.lm_head(hidden_states) - logits = logits.float() - - loss = None - if labels is not None: - # Shift so that tokens < n predict n - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - # Flatten the tokens - loss_fct = CrossEntropyLoss() - shift_logits = shift_logits.view(-1, self.config.vocab_size) - shift_labels = shift_labels.view(-1) - # Enable model parallelism - shift_labels = shift_labels.to(shift_logits.device) - loss = loss_fct(shift_logits, shift_labels) - - if not return_dict: - output = (logits,) + outputs[1:] - return (loss,) + output if loss is not None else output - - return CausalLMOutputWithPast( - loss=loss, - logits=logits, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - def prepare_inputs_for_generation( - self, - input_ids, - past_key_values=None, - attention_mask=None, - inputs_embeds=None, - **kwargs, - ): - if past_key_values is not None: - if isinstance(past_key_values, Cache): - cache_length = past_key_values.get_seq_length() - past_length = past_key_values.seen_tokens - max_cache_length = past_key_values.get_max_length() - else: - cache_length = past_length = past_key_values[0][0].shape[2] - max_cache_length = None - - # Keep only the unprocessed tokens: - # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where - # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as - # input) - if ( - attention_mask is not None - and attention_mask.shape[1] > input_ids.shape[1] - ): - input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] - # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard - # input_ids based on the past_length. - elif past_length < input_ids.shape[1]: - input_ids = input_ids[:, past_length:] - # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. - - # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. - if ( - max_cache_length is not None - and attention_mask is not None - and cache_length + input_ids.shape[1] > max_cache_length - ): - attention_mask = attention_mask[:, -max_cache_length:] - - position_ids = kwargs.get("position_ids", None) - if attention_mask is not None and position_ids is None: - # create position_ids on the fly for batch generation - position_ids = attention_mask.long().cumsum(-1) - 1 - position_ids.masked_fill_(attention_mask == 0, 1) - if past_key_values: - position_ids = position_ids[:, -input_ids.shape[1] :] - - # if `inputs_embeds` are passed, we only want to use them in the 1st generation step - if inputs_embeds is not None and past_key_values is None: - model_inputs = {"inputs_embeds": inputs_embeds} - else: - model_inputs = {"input_ids": input_ids} - - model_inputs.update( - { - "position_ids": position_ids, - "past_key_values": past_key_values, - "use_cache": kwargs.get("use_cache"), - "attention_mask": attention_mask, - } - ) - return model_inputs - - @staticmethod - def _reorder_cache(past_key_values, beam_idx): - reordered_past = () - for layer_past in past_key_values: - reordered_past += ( - tuple( - past_state.index_select(0, beam_idx.to(past_state.device)) - for past_state in layer_past - ), - ) - return reordered_past - - @classmethod - def from_pretrained( - cls, - pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], - *model_args, - config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None, - cache_dir: Optional[Union[str, os.PathLike]] = None, - ignore_mismatched_sizes: bool = False, - force_download: bool = False, - local_files_only: bool = False, - token: Optional[Union[str, bool]] = None, - revision: str = "main", - use_safetensors: bool = None, - **kwargs, - ): - pretrained_model_path = Path(pretrained_model_name_or_path) - config_path = pretrained_model_path / "config.json" - with config_path.open() as config_file: - config_json = json.load(config_file) - original_config = _load_llamahf_config(config_json) - device = kwargs["device_map"] if "device_map" in kwargs else torch.device("cpu") - - safetensors_weight_manager = SafeTensorsWeightsManager( - pretrained_model_path, device=device - ) - state_dict = _megatron_sd_to_hf_sd( - safetensors_weight_manager, - original_config.num_attention_heads, - original_config.num_key_value_heads, - original_config.hidden_size // original_config.num_attention_heads, - dtype=kwargs["torch_dtype"], - ) - - with torch.device("meta"): - model = BiasedLlamaForCausalLM(original_config) - model.load_state_dict(state_dict, assign=True) - with device: - [layer.self_attn._init_rope() for layer in model.model.layers] - model.to(dtype=kwargs["torch_dtype"], device=device) - return model - - -@add_start_docstrings( - """ - The LLaMa Model transformer with a sequence classification head on top (linear layer). - - [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models - (e.g. GPT-2) do. - - Since it does classification on the last token, it requires to know the position of the last token. If a - `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If - no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the - padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in - each row of the batch). - """, - LLAMA_START_DOCSTRING, -) -class LlamaForSequenceClassification(LlamaPreTrainedModel): - def __init__(self, config): - super().__init__(config) - self.num_labels = config.num_labels - self.model = LlamaModel(config) - self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) - - # Initialize weights and apply final processing - self.post_init() - - def get_input_embeddings(self): - return self.model.embed_tokens - - def set_input_embeddings(self, value): - self.model.embed_tokens = value - - @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) - def forward( - self, - input_ids: torch.LongTensor = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple, SequenceClassifierOutputWithPast]: - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., - config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If - `config.num_labels > 1` a classification loss is computed (Cross-Entropy). - """ - return_dict = ( - return_dict if return_dict is not None else self.config.use_return_dict - ) - - transformer_outputs = self.model( - input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - hidden_states = transformer_outputs[0] - logits = self.score(hidden_states) - - if input_ids is not None: - batch_size = input_ids.shape[0] - else: - batch_size = inputs_embeds.shape[0] - - if self.config.pad_token_id is None and batch_size != 1: - raise ValueError( - "Cannot handle batch sizes > 1 if no padding token is defined." - ) - if self.config.pad_token_id is None: - sequence_lengths = -1 - else: - if input_ids is not None: - # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility - sequence_lengths = ( - torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 - ) - sequence_lengths = sequence_lengths % input_ids.shape[-1] - sequence_lengths = sequence_lengths.to(logits.device) - else: - sequence_lengths = -1 - - pooled_logits = logits[ - torch.arange(batch_size, device=logits.device), sequence_lengths - ] - - loss = None - if labels is not None: - labels = labels.to(logits.device) - if self.config.problem_type is None: - if self.num_labels == 1: - self.config.problem_type = "regression" - elif self.num_labels > 1 and ( - labels.dtype == torch.long or labels.dtype == torch.int - ): - self.config.problem_type = "single_label_classification" - else: - self.config.problem_type = "multi_label_classification" - - if self.config.problem_type == "regression": - loss_fct = MSELoss() - if self.num_labels == 1: - loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) - else: - loss = loss_fct(pooled_logits, labels) - elif self.config.problem_type == "single_label_classification": - loss_fct = CrossEntropyLoss() - loss = loss_fct( - pooled_logits.view(-1, self.num_labels), labels.view(-1) - ) - elif self.config.problem_type == "multi_label_classification": - loss_fct = BCEWithLogitsLoss() - loss = loss_fct(pooled_logits, labels) - if not return_dict: - output = (pooled_logits,) + transformer_outputs[1:] - return ((loss,) + output) if loss is not None else output - - return SequenceClassifierOutputWithPast( - loss=loss, - logits=pooled_logits, - past_key_values=transformer_outputs.past_key_values, - hidden_states=transformer_outputs.hidden_states, - attentions=transformer_outputs.attentions, - ) - - -# Loading Megatron code -class SafeTensorsWeightsManager: - def __init__(self, model_path: Path, device="cpu") -> None: - if model_path.suffix == ".safetensors": - filenames = [model_path] - else: - filenames = list(model_path.iterdir()) - filenames = filter(lambda f: f.suffix == ".safetensors", filenames) - - self.tensor_filenames = {} - self.file_handles = {} - - for filename in filenames: - f = safetensors.safe_open(filename, framework="pt", device=str(device)) - self.file_handles[filename] = f - - for tensor_name in f.keys(): - self.tensor_filenames[tensor_name] = filename - - def get_slice(self, tensor_name: str): - filename = self.tensor_filenames[tensor_name] - f = self.file_handles[filename] - return f.get_slice(tensor_name) - - def get_tensor( - self, tensor_name: str, dtype: torch.dtype = None, device: torch.device = None - ) -> torch.Tensor: - filename = self.tensor_filenames[tensor_name] - f = self.file_handles[filename] - tensor = f.get_tensor(tensor_name) - tensor = tensor.to(dtype=dtype, device=device) - return tensor - - def get_shape(self, tensor_name: str) -> torch.Tensor: - slice = self.get_slice(tensor_name) - return slice.get_shape() - - def has_tensor(self, tensor_name: str) -> bool: - return tensor_name in self.tensor_filenames - - def __len__(self) -> int: - return len(self.tensor_filenames) - - def __iter__(self) -> str: - for tensor_name in self.tensor_filenames: - yield tensor_name - - def __eq__(self, __value: object) -> bool: - if not isinstance(__value, SafeTensorsWeightsManager): - return False - - if len(self) != len(__value): - return False - - for tn1, tn2 in zip(self, __value): - if tn1 != tn2: - return False - - if not self.get_tensor(tn1).equal(__value.get_tensor(tn2)): - return False - - return True - - -def _load_llamahf_config(config_json): - original_config = BiasedLlamaConfig( - vocab_size=config_json.get("vocab_size", 32000), - max_position_embeddings=config_json.get("n_positions", 2048), - hidden_size=config_json.get("n_embd", 4096), - num_hidden_layers=config_json.get("n_layer", 32), - num_attention_heads=config_json.get("n_head", 32), - num_key_value_heads=config_json.get("num_key_value_heads", None), - intermediate_size=4 * config_json.get("n_embd", 4096) - if config_json["n_inner"] is None - else config_json["n_inner"], - hidden_act="silu", - rms_norm_eps=config_json.get("layer_norm_epsilon", 0.000001), - use_cache=config_json.get("use_cache", True), - attention_bias=config_json.get("add_bias", False), - tie_word_embeddings=config_json.get("tie_word_embeddings", False), - initializer_range=config_json.get("initializer_range", 0.02), - rope_theta=config_json.get("rope_theta", 10000), - rope_scaling=config_json.get("rope_scaling", None), - attention_dropout=config_json.get("attn_pdrop", 0), - pad_token_id=config_json.get("pad_token_id", None), - bos_token_id=config_json.get("bos_token_id", 1), - eos_token_id=config_json.get("eos_token_id", 2), - ) - return original_config - - -def _megatron_sd_to_hf_sd( - safetensors_weight_manager: SafeTensorsWeightsManager, - num_heads: int, - num_key_value_heads: int, - head_dim: int, - device=None, - dtype=None, -): - replacements = [ - # embedding - (r"^transformer\.wte\.weight", "model.embed_tokens.weight"), - # layers - (r"^transformer\.h", "model.layers"), - # attn - (r"attn\.c_proj", "self_attn.o_proj"), - # mlp - (r"mlp\.c_proj", "mlp.down_proj"), - # block ln - (r"ln_1\.weight", "input_layernorm.weight"), - (r"ln_2\.weight", "post_attention_layernorm.weight"), - # model ln - (r"^transformer\.ln_f\.weight", "model.norm.weight"), - ] - - emb_dim = head_dim * num_heads - - attn_splits = [ - (num_heads * head_dim) // num_key_value_heads, - (num_key_value_heads * head_dim) // num_key_value_heads, - (num_key_value_heads * head_dim) // num_key_value_heads, - ] - - lm_head_pattern = re.compile("^transformer.wte.weight") - qkv_weight_pattern = re.compile("transformer.h.[0-9]+.attn.c_attn.weight") - qkv_bias_pattern = re.compile("transformer.h.[0-9]+.attn.c_attn.bias") - mlp_weight_pattern = re.compile("transformer.h.[0-9]+.mlp.c_fc.weight") - mlp_bias_pattern = re.compile("transformer.h.[0-9]+.mlp.c_fc.bias") - new_sd = {} - for name in safetensors_weight_manager: - new_name = name - for pattern, repl in replacements: - new_name = re.sub(pattern, repl, new_name) - new_sd[new_name] = safetensors_weight_manager.get_tensor(name, dtype, device) - - # lm head repeat - if bool(lm_head_pattern.match(name)): - new_sd["lm_head.weight"] = new_sd[new_name] - # qkv fused - if bool(qkv_weight_pattern.match(name)): - tensor = new_sd.pop(new_name) - q, k, v = tensor.view(num_key_value_heads, -1, emb_dim).split( - attn_splits, dim=1 - ) - - prefix = new_name.replace("attn.c_attn.weight", "") - new_sd[f"{prefix}self_attn.q_proj.weight"] = q.reshape(-1, q.size(2)) - new_sd[f"{prefix}self_attn.k_proj.weight"] = k.reshape(-1, k.size(2)) - new_sd[f"{prefix}self_attn.v_proj.weight"] = v.reshape(-1, v.size(2)) - elif bool(qkv_bias_pattern.match(name)): - tensor = new_sd.pop(new_name) - q, k, v = tensor.view(num_key_value_heads, -1).split(attn_splits, dim=1) - - prefix = new_name.replace("attn.c_attn.bias", "") - new_sd[f"{prefix}self_attn.q_proj.bias"] = q.reshape(-1) - new_sd[f"{prefix}self_attn.k_proj.bias"] = k.reshape(-1) - new_sd[f"{prefix}self_attn.v_proj.bias"] = v.reshape(-1) - elif bool(mlp_weight_pattern.match(name)): - tensor = new_sd.pop(new_name) - w1, wg = tensor.chunk(2) - - prefix = new_name.replace("mlp.c_fc.weight", "") - new_sd[f"{prefix}mlp.up_proj.weight"] = w1 - new_sd[f"{prefix}mlp.gate_proj.weight"] = wg - elif bool(mlp_bias_pattern.match(name)): - tensor = new_sd.pop(new_name) - w1, wg = tensor.chunk(2) - - prefix = new_name.replace("mlp.c_fc.bias", "") - new_sd[f"{prefix}mlp.up_proj.bias"] = w1 - new_sd[f"{prefix}mlp.gate_proj.bias"] = wg - - return new_sd - - -AutoConfig.register( - BiasedLlamaForCausalLM.config_class.model_type, BiasedLlamaForCausalLM.config_class -) -AutoModelForCausalLM.register( - BiasedLlamaForCausalLM.config_class, BiasedLlamaForCausalLM -) diff --git a/pyproject.toml b/pyproject.toml index 0f370865..f307cee2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,8 +27,12 @@ dependencies = [ "tokenizers>=0.13.3", "torch>=2.0.1", "tqdm>=4.65.0", - "transformers>=4.36.0", - "peft>=0.7.1,<0.8.0", + "transformers>=4.32.0", + # GK-AUG-25-2023 NOTE: mpt branch on Mayank's fork was merged to peft main on Aug 24 and it got deleted + # which broke caikit-nlp build. peft hasn't released newer version yet, so to get + # the build fix, we pulling peft from main branch commit. In future, we will pull PEFT from + # pypi + "peft@git+https://github.com/huggingface/peft.git@8c17d556a8fe9522e10d73d7bd3fad46a6ecae14" ] [tool.setuptools.packages.find]