update flashattn, fix ppo save model
This commit is contained in:
hiyouga
parent
b218c271ed
commit
0fbece85a7
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@ -25,16 +25,16 @@ class SavePeftModelCallback(TrainerCallback):
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r"""
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Event called after a checkpoint save.
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"""
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output_dir = os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step))
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getattr(kwargs.get("model"), "pretrained_model").save_pretrained(output_dir)
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return control
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if args.should_save:
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output_dir = os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step))
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getattr(kwargs.get("model"), "pretrained_model").save_pretrained(output_dir)
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def on_train_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
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def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
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r"""
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Event called at the end of training.
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"""
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getattr(kwargs.get("model"), "pretrained_model").save_pretrained(args.output_dir)
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return control
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if args.should_save:
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getattr(kwargs.get("model"), "pretrained_model").save_pretrained(args.output_dir)
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class LogCallback(TrainerCallback):
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@ -1,22 +1,14 @@
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# coding=utf-8
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# Modified from:
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# [1] https://huggingface.co/Birchlabs/flash_llama/blob/main/modeling_flash_llama.py
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# [2] https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/blob/main/modeling_flash_llama.py
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# [3] https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
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# [2] https://github.com/lm-sys/FastChat/blob/main/fastchat/train/llama2_flash_attn_monkey_patch.py
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# [3] https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/blob/main/modeling_flash_llama.py
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# [4] https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
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# With fix from Alex Birch: https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/discussions/17
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from transformers.activations import ACT2FN
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
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from typing import Optional, Tuple
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from transformers.utils import logging
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from transformers.models.llama.configuration_llama import LlamaConfig
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@ -43,69 +35,34 @@ except ImportError:
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "LlamaConfig"
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class LlamaRMSNorm(torch.nn.Module):
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def rmsnorm_func(hidden_states, weight, variance_epsilon):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + variance_epsilon)
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return (weight * hidden_states).to(input_dtype)
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class LlamaRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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LlamaRMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.register_buffer(
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"variance_epsilon",
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torch.tensor(eps),
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persistent=False,
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)
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self.weight = torch.nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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return rmsnorm_func(hidden_states, self.weight, self.variance_epsilon)
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return (self.weight * hidden_states).to(input_dtype) # for fp32 weight
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class FlashRotaryEmbedding(torch.nn.Module):
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"""
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The rotary position embeddings from RoFormer_ (Su et. al).
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A crucial insight from the method is that the query and keys are
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transformed by rotation matrices which depend on the relative positions.
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Other implementations are available in the Rotary Transformer repo_ and in
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GPT-NeoX_, GPT-NeoX was an inspiration
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.. _RoFormer: https://arxiv.org/abs/2104.09864
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.. _repo: https://github.com/ZhuiyiTechnology/roformer
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.. _GPT-NeoX: https://github.com/EleutherAI/gpt-neox
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If scale_base is not None, this implements XPos (Sun et al., https://arxiv.org/abs/2212.10554).
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A recommended value for scale_base is 512: https://github.com/HazyResearch/flash-attention/issues/96
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Reference: https://github.com/sunyt32/torchscale/blob/main/torchscale/component/xpos_relative_position.py
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"""
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def __init__(self, dim: int, base=10000.0, interleaved=False, scale_base=None,
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scaling_factor=1.0, pos_idx_in_fp32=True, device=None):
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"""
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interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
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of 1st half and 2nd half (GPT-NeoX style).
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pos_idx_in_fp32: if True, the position indices [0.0, ..., seqlen - 1] are in fp32,
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otherwise they might be in lower precision.
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This option was added because previously (before 2023-07-02), when we construct
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the position indices, we use the dtype of self.inv_freq. In most cases this would
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be fp32, but if the model is trained in pure bf16 (not mixed precision), then
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self.inv_freq would be bf16, and the position indices are also in bf16.
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Because of the limited precision of bf16 (e.g. 1995.0 is rounded to 2000.0), the
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embeddings for some positions will coincide.
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To maintain compatibility with models previously trained in pure bf16,
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we add this option.
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scaling_factor: RotaryEmbedding extended with linear scaling.
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"""
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def __init__(
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self,
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dim: int,
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base=10000.0,
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interleaved=False,
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scale_base=None,
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scaling_factor=1.0,
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pos_idx_in_fp32=True,
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device=None
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):
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super().__init__()
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self.dim = dim
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self.base = float(base)
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@ -116,8 +73,10 @@ class FlashRotaryEmbedding(torch.nn.Module):
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self.interleaved = interleaved
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self.scale_base = scale_base
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self.scaling_factor = scaling_factor
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scale = ((torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim)
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/ (1.4 * dim) if scale_base is not None else None)
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scale = (
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(torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
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if scale_base is not None else None
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)
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self.register_buffer("scale", scale)
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self._seq_len_cached = 0
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@ -127,28 +86,18 @@ class FlashRotaryEmbedding(torch.nn.Module):
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self._sin_k_cached = None
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def _compute_inv_freq(self, device=None):
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return 1 / (self.base ** (torch.arange(0, self.dim, 2, device=device,
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dtype=torch.float32) / self.dim))
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return 1 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
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def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):
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# Reset the tables if the sequence length has changed,
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# if we're on a new device (possibly due to tracing for instance),
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# or if we're switching from inference mode to training
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if (seqlen > self._seq_len_cached or self._cos_cached.device != device
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if (
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seqlen > self._seq_len_cached or self._cos_cached.device != device
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or self._cos_cached.dtype != dtype
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or (self.training and self._cos_cached.is_inference())):
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or (self.training and self._cos_cached.is_inference())
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):
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self._seq_len_cached = seqlen
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# We want fp32 here, not self.inv_freq.dtype, since the model could be loaded in bf16
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# And the output of arange can be quite large, so bf16 would lose a lot of precision.
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# However, for compatibility reason, we add an option to use the dtype of self.inv_freq.
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if self.pos_idx_in_fp32:
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t = torch.arange(seqlen, device=device, dtype=torch.float32)
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t /= self.scaling_factor
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# We want fp32 here as well since inv_freq will be multiplied with t, and the output
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# will be large. Having it in bf16 will lose a lot of precision and cause the
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# cos & sin output to change significantly.
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# We want to recompute self.inv_freq if it was not loaded in fp32
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if self.inv_freq.dtype != torch.float32:
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inv_freq = self.inv_freq.to(torch.float32)
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else:
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@ -157,15 +106,14 @@ class FlashRotaryEmbedding(torch.nn.Module):
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t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
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t /= self.scaling_factor
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inv_freq = self.inv_freq
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# Don't do einsum, it converts fp32 to fp16 under AMP
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# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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freqs = torch.outer(t, inv_freq)
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if self.scale is None:
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self._cos_cached = torch.cos(freqs).to(dtype)
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self._sin_cached = torch.sin(freqs).to(dtype)
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else:
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power = ((torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device)
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- seqlen // 2) / self.scale_base)
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power = (
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(torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2) / self.scale_base
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)
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scale = self.scale.to(device=power.device) ** power.unsqueeze(-1)
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# We want the multiplication by scale to happen in fp32
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self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
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@ -174,7 +122,7 @@ class FlashRotaryEmbedding(torch.nn.Module):
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self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
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def forward(self, q: torch.Tensor, k: torch.Tensor, seqlen_offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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r"""
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q: (batch, seqlen, nheads, headdim)
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k: (batch, seqlen, nheads, headdim)
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seqlen_offset: can be used in generation where the qkv being passed in is only the last
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@ -193,23 +141,8 @@ class FlashRotaryEmbedding(torch.nn.Module):
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assert False
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class LlamaMLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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r"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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@ -220,10 +153,9 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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return hidden_states.reshape(batch, slen, 2, num_key_value_heads * n_rep, head_dim)
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class LlamaAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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class LlamaAttention(torch.nn.Module):
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def __init__(self, config: LlamaConfig):
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def __init__(self, config: "LlamaConfig"):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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@ -238,10 +170,11 @@ class LlamaAttention(nn.Module):
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads})."
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)
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
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self.q_proj = torch.nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
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self.k_proj = torch.nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = torch.nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = torch.nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
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self.register_buffer(
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"norm_factor",
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@ -254,10 +187,10 @@ class LlamaAttention(nn.Module):
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else:
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scaling_type = self.config.rope_scaling["type"]
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scaling_factor = self.config.rope_scaling["factor"]
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assert scaling_type == 'linear'
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assert scaling_type == "linear"
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self.rotary_emb = FlashRotaryEmbedding(
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self.head_dim, base=10000, interleaved=False, scaling_factor=scaling_factor,
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self.head_dim, base=10000, interleaved=False, scaling_factor=scaling_factor
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)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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@ -270,8 +203,7 @@ class LlamaAttention(nn.Module):
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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is_padded_inputs: Optional[bool] = False,
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use_cache: bool = False
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, h_size = hidden_states.size()
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@ -290,9 +222,9 @@ class LlamaAttention(nn.Module):
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q = q.view(bsz, q_len, self.num_heads, self.head_dim)
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k = k.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
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v = v.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
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q, k = self.rotary_emb(q, k, past_len)
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kv = torch.stack([k, v], 2)
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kv = repeat_kv(kv, self.num_key_value_groups)
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@ -300,27 +232,26 @@ class LlamaAttention(nn.Module):
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if has_layer_past:
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new_len = past_len+q.size(1)
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if new_len > past_kv.size(1):
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past_kv = torch.cat([past_kv, torch.empty(bsz, 256, 2, kv.size(3), kv.size(4), dtype=kv.dtype, device=kv.device)], 1)
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past_kv = torch.cat(
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[past_kv, torch.empty(bsz, 256, 2, kv.size(3), kv.size(4), dtype=kv.dtype, device=kv.device)], 1
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)
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past_kv[:, past_len:new_len] = kv
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kv = past_kv[:, :new_len]
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else:
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past_kv = kv
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past_key_value = (past_kv, past_len+q.size(1)) if use_cache else None
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if is_padded_inputs:
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past_key_value = (past_kv, past_len + q.size(1)) if use_cache else None
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if attention_mask is not None:
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# varlen, ignore padding tokens, efficient for large batch with many paddings
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logger.warning_once("padded")
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logger.warning_once("padded sequences is less efficient")
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assert attention_mask is not None
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unpadded_kv, indices_k, cu_seqlens_k, max_seqlen_k = unpad_input(kv, attention_mask)
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unpadded_q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, attention_mask[:, -q.size(1):])
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attn_outputs = flash_attn_varlen_kvpacked_func(
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unpadded_q, unpadded_kv, cu_seqlens_q, cu_seqlens_k,
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unpadded_q, unpadded_kv, cu_seqlens_q, cu_seqlens_k,
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max_seqlen_q, max_seqlen_k,
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dropout_p=0.0, softmax_scale=1.0/self.norm_factor,
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dropout_p=0.0, softmax_scale=1.0/self.norm_factor,
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causal=(not has_layer_past), return_attn_probs=output_attentions
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)
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@ -329,14 +260,13 @@ class LlamaAttention(nn.Module):
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attn_output, indices_q, bsz, q_len
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).reshape(bsz, q_len, h_size)
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attn_weights = attn_outputs[2] if output_attentions else None
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else:
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# no padding tokens, more efficient
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attn_outputs = flash_attn_kvpacked_func(
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q, kv, dropout_p=0.0, softmax_scale=1.0/self.norm_factor, causal=(not has_layer_past), return_attn_probs=output_attentions)
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q, kv, dropout_p=0.0, softmax_scale=1.0/self.norm_factor,
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causal=(not has_layer_past), return_attn_probs=output_attentions
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)
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attn_output = attn_outputs[0] if output_attentions else attn_outputs
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attn_output = attn_output.reshape(bsz, q_len, h_size)
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attn_weights = attn_outputs[2] if output_attentions else None
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|
|
@ -349,377 +279,27 @@ class LlamaAttention(nn.Module):
|
|||
return attn_output, attn_weights, past_key_value
|
||||
|
||||
|
||||
class LlamaDecoderLayer(nn.Module):
|
||||
def __init__(self, config: LlamaConfig):
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
self.self_attn = LlamaAttention(config=config)
|
||||
self.mlp = LlamaMLP(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,
|
||||
is_padded_inputs: Optional[bool] = False,
|
||||
output_attentions: Optional[bool] = False,
|
||||
use_cache: Optional[bool] = False,
|
||||
) -> 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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative 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.
|
||||
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
|
||||
"""
|
||||
|
||||
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,
|
||||
is_padded_inputs=is_padded_inputs,
|
||||
)
|
||||
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, LLAMA_INPUTS_DOCSTRING = "", ""
|
||||
|
||||
|
||||
@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 = LlamaConfig
|
||||
base_model_prefix = "model"
|
||||
supports_gradient_checkpointing = True
|
||||
_no_split_modules = ["LlamaDecoderLayer"]
|
||||
_skip_keys_device_placement = "past_key_values"
|
||||
|
||||
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_()
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value=False):
|
||||
if isinstance(module, LlamaModel):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
|
||||
@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: LlamaConfig
|
||||
"""
|
||||
|
||||
def __init__(self, config: LlamaConfig):
|
||||
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) for _ in range(config.num_hidden_layers)])
|
||||
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,
|
||||
is_padded_inputs: Optional[bool] = False,
|
||||
) -> 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 decoder_input_ids and decoder_inputs_embeds at the same time")
|
||||
elif input_ids is not None:
|
||||
batch_size, seq_length = input_ids.shape
|
||||
elif inputs_embeds is not None:
|
||||
batch_size, seq_length, _ = inputs_embeds.shape
|
||||
else:
|
||||
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
||||
|
||||
seq_length_with_past = seq_length
|
||||
past_key_values_length = 0
|
||||
|
||||
if past_key_values is not None:
|
||||
past_key_values_length = past_key_values[0][0].shape[2]
|
||||
seq_length_with_past = seq_length_with_past + past_key_values_length
|
||||
|
||||
position_ids = None
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
|
||||
hidden_states = 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
|
||||
|
||||
# decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
next_decoder_cache = () if use_cache else None
|
||||
|
||||
for idx, decoder_layer in enumerate(self.layers):
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
# None for past_key_value
|
||||
return module(*inputs, output_attentions, None)
|
||||
|
||||
return custom_forward
|
||||
|
||||
layer_outputs = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(decoder_layer),
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
None,
|
||||
is_padded_inputs
|
||||
)
|
||||
else:
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
is_padded_inputs=is_padded_inputs,
|
||||
)
|
||||
|
||||
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 = next_decoder_cache if use_cache else None
|
||||
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,
|
||||
# Disable the transformation of the attention mask in LlamaModel as flash attention
|
||||
# takes a boolean key_padding_mask. Fills in the past kv length for use in forward.
|
||||
def _prepare_decoder_attention_mask(
|
||||
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
|
||||
):
|
||||
# [bsz, seq_len]
|
||||
if past_key_values_length > 0 and attention_mask is not None:
|
||||
attention_mask = torch.cat(
|
||||
(
|
||||
torch.full(
|
||||
(input_shape[0], past_key_values_length),
|
||||
True,
|
||||
dtype=attention_mask.dtype,
|
||||
device=attention_mask.device
|
||||
),
|
||||
attention_mask
|
||||
),
|
||||
dim=-1
|
||||
)
|
||||
|
||||
if attention_mask is not None and torch.all(attention_mask):
|
||||
return None # This uses the faster call when training with full samples
|
||||
|
||||
class LlamaForCausalLM(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)
|
||||
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,
|
||||
is_padded_inputs: Optional[bool] = None,
|
||||
) -> Union[Tuple, CausalLMOutputWithPast]:
|
||||
|
||||
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
|
||||
|
||||
is_padded_inputs = ((attention_mask is not None) and (not attention_mask.all().item()))
|
||||
|
||||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
||||
outputs: "CausalLMOutputWithPast" = 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,
|
||||
is_padded_inputs=is_padded_inputs,
|
||||
)
|
||||
|
||||
hidden_states = outputs[0]
|
||||
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:
|
||||
input_ids = input_ids[:, -1:]
|
||||
|
||||
position_ids = kwargs.get("position_ids", None)
|
||||
|
||||
# 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,
|
||||
"is_padded_inputs": ((attention_mask is not None) and (not attention_mask.all().item()))
|
||||
}
|
||||
)
|
||||
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
|
||||
return attention_mask
|
||||
|
|
|
|||
|
|
@ -132,8 +132,11 @@ def load_model_and_tokenizer(
|
|||
|
||||
# Set flash attention
|
||||
if model_args.flash_attn and getattr(config, "model_type", None) == "llama":
|
||||
from llmtuner.extras.models.flash_llama import LlamaForCausalLM
|
||||
transformers.models.llama.modeling_llama.LlamaForCausalLM = LlamaForCausalLM
|
||||
import transformers.models.llama.modeling_llama as LlamaModule
|
||||
from llmtuner.extras.models.flash_llama import LlamaRMSNorm, LlamaAttention, _prepare_decoder_attention_mask
|
||||
LlamaModule.LlamaRMSNorm = LlamaRMSNorm
|
||||
LlamaModule.LlamaAttention = LlamaAttention
|
||||
LlamaModule.LlamaModel._prepare_decoder_attention_mask = _prepare_decoder_attention_mask
|
||||
if not hasattr(config, "num_key_value_heads"):
|
||||
setattr(config, "num_key_value_heads", getattr(config, "num_attention_heads"))
|
||||
if getattr(config, "pretraining_tp", 1) != 1:
|
||||
|
|
|
|||
|
|
@ -26,6 +26,7 @@ class CustomDPOTrainer(DPOTrainer):
|
|||
if ref_model is not None:
|
||||
disable_dropout_in_model(ref_model)
|
||||
|
||||
self.is_encoder_decoder = model.config.is_encoder_decoder
|
||||
self.ref_model = ref_model
|
||||
self.use_dpo_data_collator = True # hack to avoid warning
|
||||
self.label_pad_token_id = IGNORE_INDEX
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ from tqdm import tqdm
|
|||
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
|
||||
|
||||
from transformers import GenerationConfig, Trainer, TrainerState, TrainerControl
|
||||
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
|
||||
|
||||
from trl import PPOTrainer
|
||||
from trl.core import LengthSampler, PPODecorators, logprobs_from_logits
|
||||
|
|
@ -96,7 +97,6 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
|
|||
# Cast to inference mode
|
||||
unwrapped_model.gradient_checkpointing_disable()
|
||||
unwrapped_model.config.use_cache = True
|
||||
unwrapped_model, layer_norm_params = cast_layernorm_dtype(unwrapped_model, self.compute_dtype)
|
||||
self.model.eval()
|
||||
|
||||
# Get inputs
|
||||
|
|
@ -107,7 +107,6 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
|
|||
# Cast to training mode
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unwrapped_model.gradient_checkpointing_enable()
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unwrapped_model.config.use_cache = False
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||||
unwrapped_model, _ = cast_layernorm_dtype(unwrapped_model, self.compute_dtype, layer_norm_params)
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||||
self.model.train()
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|
||||
# Run PPO step
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|
|
@ -134,7 +133,12 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
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|||
reward_meter.reset()
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||||
|
||||
if (step+1) % self.args.save_steps == 0: # save checkpoint
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||||
self.save_model(os.path.join(self.args.output_dir, f"checkpoint-{step+1}"))
|
||||
self.save_model(os.path.join(
|
||||
self.args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, self.state.global_step)
|
||||
))
|
||||
self.save_callback.on_save(
|
||||
self.args, self.state, self.control, model=self.accelerator.unwrap_model(self.model)
|
||||
)
|
||||
|
||||
if self.control.should_epoch_stop or self.control.should_training_stop:
|
||||
break
|
||||
|
|
@ -165,8 +169,10 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
|
|||
)
|
||||
|
||||
input_ids = batch["input_ids"]
|
||||
self.model, layer_norm_params = cast_layernorm_dtype(self.model, self.compute_dtype)
|
||||
unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)
|
||||
response: torch.Tensor = unwrapped_model.generate(**gen_kwargs)
|
||||
self.model, _ = cast_layernorm_dtype(self.model, self.compute_dtype, layer_norm_params)
|
||||
query, response = input_ids.detach().cpu(), response[:, input_ids.size(-1):].detach().cpu()
|
||||
|
||||
queries, responses = [], []
|
||||
|
|
@ -294,6 +300,3 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
|
|||
"""
|
||||
if self.args.should_save:
|
||||
self._save(output_dir)
|
||||
self.save_callback.on_save(
|
||||
self.args, self.state, self.control, model=self.accelerator.unwrap_model(self.model)
|
||||
)
|
||||
|
|
|
|||
Loading…
Reference in New Issue