XFL-机器学习资源

""" PyTorch ChatGLM model. """ import math import copy import os import warnings import re import sys import torch import torch.utils.checkpoint import torch.nn.functional as F from torch import nn from torch.nn import CrossEntropyLoss, LayerNorm from torch.nn.utils import skip_init from typing import Optional, Tuple, Union, List, Callable, Dict, Any from transformers.utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, ) from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, ) from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging from transformers.generation.logits_process import LogitsProcessor from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput from .configuration_chatglm import ChatGLMConfig # flags required to enable jit fusion kernels if sys.platform != 'darwin': torch._C._jit_set_profiling_mode(False) torch._C._jit_set_profiling_executor(False) torch._C._jit_override_can_fuse_on_cpu(True) torch._C._jit_override_can_fuse_on_gpu(True) logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "THUDM/ChatGLM-6B" _CONFIG_FOR_DOC = "ChatGLM6BConfig" CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ "THUDM/chatglm-6b", # See all ChatGLM-6B models at https://huggingface.co/models?filter=chatglm ] class InvalidScoreLogitsProcessor(LogitsProcessor): def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: if torch.isnan(scores).any() or torch.isinf(scores).any(): scores.zero_() scores[..., 5] = 5e4 return scores def load_tf_weights_in_chatglm_6b(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if any( n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue pointer = model for m_name in name: if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") elif scope_names[0] == "squad": pointer = getattr(pointer, "classifier") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) try: assert ( pointer.shape == array.shape ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched" except AssertionError as e: e.args += (pointer.shape, array.shape) raise logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class PrefixEncoder(torch.nn.Module): """ The torch.nn model to encode the prefix Input shape: (batch-size, prefix-length) Output shape: (batch-size, prefix-length, 2*layers*hidden) """ def __init__(self, config): super().__init__() self.prefix_projection = config.prefix_projection if self.prefix_projection: # Use a two-layer MLP to encode the prefix self.embedding = torch.nn.Embedding(config.pre_seq_len, config.hidden_size) self.trans = torch.nn.Sequential( torch.nn.Linear(config.hidden_size, config.hidden_size), torch.nn.Tanh(), torch.nn.Linear(config.hidden_size, config.num_layers * config.hidden_size * 2) ) else: self.embedding = torch.nn.Embedding(config.pre_seq_len, config.num_layers * config.hidden_size * 2) def forward(self, prefix: torch.Tensor): if self.prefix_projection: prefix_tokens = self.embedding(prefix) past_key_values = self.trans(prefix_tokens) else: past_key_values = self.embedding(prefix) return past_key_values @torch.jit.script def gelu_impl(x): """OpenAI's gelu implementation.""" return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x * (1.0 + 0.044715 * x * x))) def gelu(x): return gelu_impl(x) class RotaryEmbedding(torch.nn.Module): def __init__(self, dim, base=10000, precision=torch.half, learnable=False): super().__init__() inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim)) inv_freq = inv_freq.half() self.learnable = learnable if learnable: self.inv_freq = torch.nn.Parameter(inv_freq) self.max_seq_len_cached = None else: self.register_buffer('inv_freq', inv_freq) self.max_seq_len_cached = None self.cos_cached = None self.sin_cached = None self.precision = precision def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): pass def forward(self, x, seq_dim=1, seq_len=None): if seq_len is None: seq_len = x.shape[seq_dim] if self.max_seq_len_cached is None or (seq_len > self.max_seq_len_cached): self.max_seq_len_cached = None if self.learnable else seq_len t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype) freqs = torch.einsum('i,j->ij', t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1).to(x.device) if self.precision == torch.bfloat16: emb = emb.float() # [sx, 1 (b * np), hn] cos_cached = emb.cos()[:, None, :] sin_cached = emb.sin()[:, None, :] if self.precision == torch.bfloat16: cos_cached = cos_cached.bfloat16() sin_cached = sin_cached.bfloat16() if self.learna