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""" PyTorch ChatGLM model. """ |
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import math |
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import copy |
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import warnings |
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import re |
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import sys |
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import torch |
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import torch.utils.checkpoint |
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import torch.nn.functional as F |
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from torch import nn |
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from torch.nn import CrossEntropyLoss, LayerNorm |
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from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss |
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from torch.nn.utils import skip_init |
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from typing import Optional, Tuple, Union, List, Callable, Dict, Any |
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from transformers.modeling_outputs import ( |
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BaseModelOutputWithPast, |
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CausalLMOutputWithPast, |
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SequenceClassifierOutputWithPast, |
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) |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.utils import logging |
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from transformers.generation.logits_process import LogitsProcessor |
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from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput |
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from .configuration_chatglm import ChatGLMConfig |
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if sys.platform != 'darwin': |
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torch._C._jit_set_profiling_mode(False) |
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torch._C._jit_set_profiling_executor(False) |
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torch._C._jit_override_can_fuse_on_cpu(True) |
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torch._C._jit_override_can_fuse_on_gpu(True) |
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logger = logging.get_logger(__name__) |
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_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM2-6B" |
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_CONFIG_FOR_DOC = "ChatGLM6BConfig" |
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CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ |
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"THUDM/chatglm2-6b", |
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] |
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def default_init(cls, *args, **kwargs): |
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return cls(*args, **kwargs) |
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class InvalidScoreLogitsProcessor(LogitsProcessor): |
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: |
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if torch.isnan(scores).any() or torch.isinf(scores).any(): |
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scores.zero_() |
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scores[..., 5] = 5e4 |
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return scores |
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class PrefixEncoder(torch.nn.Module): |
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""" |
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The torch.nn model to encode the prefix |
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Input shape: (batch-size, prefix-length) |
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Output shape: (batch-size, prefix-length, 2*layers*hidden) |
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""" |
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def __init__(self, config: ChatGLMConfig): |
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super().__init__() |
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self.prefix_projection = config.prefix_projection |
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if self.prefix_projection: |
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kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2 |
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self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) |
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self.trans = torch.nn.Sequential( |
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torch.nn.Linear(kv_size, config.hidden_size), |
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torch.nn.Tanh(), |
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torch.nn.Linear(config.hidden_size, kv_size) |
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) |
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else: |
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self.embedding = torch.nn.Embedding(config.pre_seq_len, |
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config.num_layers * config.kv_channels * config.multi_query_group_num * 2) |
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def forward(self, prefix: torch.Tensor): |
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if self.prefix_projection: |
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prefix_tokens = self.embedding(prefix) |
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past_key_values = self.trans(prefix_tokens) |
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else: |
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past_key_values = self.embedding(prefix) |
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return past_key_values |
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def split_tensor_along_last_dim( |
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tensor: torch.Tensor, |
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num_partitions: int, |
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contiguous_split_chunks: bool = False, |
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) -> List[torch.Tensor]: |
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"""Split a tensor along its last dimension. |
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Arguments: |
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tensor: input tensor. |
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num_partitions: number of partitions to split the tensor |
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contiguous_split_chunks: If True, make each chunk contiguous |
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in memory. |
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Returns: |
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A list of Tensors |
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""" |
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last_dim = tensor.dim() - 1 |
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last_dim_size = tensor.size()[last_dim] // num_partitions |
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tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) |
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if contiguous_split_chunks: |
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return tuple(chunk.contiguous() for chunk in tensor_list) |
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return tensor_list |
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class RotaryEmbedding(nn.Module): |
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def __init__(self, dim, original_impl=False, device=None, dtype=None): |
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super().__init__() |
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inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) |
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self.register_buffer("inv_freq", inv_freq) |
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self.dim = dim |
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self.original_impl = original_impl |
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def forward_impl( |
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self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000 |
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): |
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"""Enhanced Transformer with Rotary Position Embedding. |
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Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ |
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transformers/rope/__init__.py. MIT License: |
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https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. |
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""" |
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theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem)) |
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seq_idx = torch.arange(seq_len, dtype=dtype, device=device) |
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idx_theta = torch.outer(seq_idx, theta).float() |
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cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) |
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if dtype in (torch.float16, torch.bfloat16, torch.int8): |
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cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() |
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return cache |
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def forward(self, max_seq_len, offset=0): |
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return self.forward_impl( |
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max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device |
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) |
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@torch.jit.script |
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def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: |
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sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) |
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rot_dim = rope_cache.shape[-2] * 2 |
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x, x_pass = x[..., :rot_dim], x[..., rot_dim:] |
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rope_cache = rope_cache[:sq] |
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xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) |
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rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) |
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x_out2 = torch.stack( |
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[ |
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xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], |
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xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], |
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], |
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-1, |
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) |
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x_out2 = x_out2.flatten(3) |
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return torch.cat((x_out2, x_pass), dim=-1) |
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class RMSNorm(torch.nn.Module): |
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def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): |
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super().__init__() |
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self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) |
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self.eps = eps |
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def forward(self, hidden_states: torch.Tensor): |
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input_dtype = hidden_states.dtype |
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variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) |
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hidden_states = hidden_states * torch.rsqrt(variance + self.eps) |
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return (self.weight * hidden_states).to(input_dtype) |
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class CoreAttention(torch.nn.Module): |
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def __init__(self, config: ChatGLMConfig, layer_number): |
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super(CoreAttention, self).__init__() |
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self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling |
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self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 |
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if self.apply_query_key_layer_scaling: |
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self.attention_softmax_in_fp32 = True |
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self.layer_number = max(1, layer_number) |
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projection_size = config.kv_channels * config.num_attention_heads |
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self.hidden_size_per_partition = projection_size |
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self.hidden_size_per_attention_head = projection_size // config.num_attention_heads |
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self.num_attention_heads_per_partition = config.num_attention_heads |
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coeff = None |
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self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) |
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if self.apply_query_key_layer_scaling: |
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coeff = self.layer_number |
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self.norm_factor *= coeff |
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self.coeff = coeff |
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self.attention_dropout = torch.nn.Dropout(config.attention_dropout) |
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def forward(self, query_layer, key_layer, value_layer, attention_mask): |
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pytorch_major_version = int(torch.__version__.split('.')[0]) |
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if pytorch_major_version >= 2: |
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query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] |
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if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: |
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context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, |
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is_causal=True) |
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else: |
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if attention_mask is not None: |
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attention_mask = ~attention_mask |
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context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, |
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attention_mask) |
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context_layer = context_layer.permute(2, 0, 1, 3) |
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new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) |
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context_layer = context_layer.reshape(*new_context_layer_shape) |
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else: |
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output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) |
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query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) |
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key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) |
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matmul_input_buffer = torch.empty( |
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output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, |
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device=query_layer.device |
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) |
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matmul_result = torch.baddbmm( |
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matmul_input_buffer, |
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query_layer.transpose(0, 1), |
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key_layer.transpose(0, 1).transpose(1, 2), |
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beta=0.0, |
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alpha=(1.0 / self.norm_factor), |
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) |
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attention_scores = matmul_result.view(*output_size) |
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if self.attention_softmax_in_fp32: |
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attention_scores = attention_scores.float() |
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if self.coeff is not None: |
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attention_scores = attention_scores * self.coeff |
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if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]: |
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attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3], |
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device=attention_scores.device, dtype=torch.bool) |
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attention_mask.tril_() |
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attention_mask = ~attention_mask |
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if attention_mask is not None: |
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attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) |
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attention_probs = F.softmax(attention_scores, dim=-1) |
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attention_probs = attention_probs.type_as(value_layer) |
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attention_probs = self.attention_dropout(attention_probs) |
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output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) |
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value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) |
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attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) |
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context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) |
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context_layer = context_layer.view(*output_size) |
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context_layer = context_layer.permute(2, 0, 1, 3).contiguous() |
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new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) |
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context_layer = context_layer.view(*new_context_layer_shape) |
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return context_layer |
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class SelfAttention(torch.nn.Module): |
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"""Parallel self-attention layer abstract class. |
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Self-attention layer takes input with size [s, b, h] |
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and returns output of the same size. |
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""" |
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def __init__(self, config: ChatGLMConfig, layer_number, device=None): |
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super(SelfAttention, self).__init__() |
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self.layer_number = max(1, layer_number) |
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self.projection_size = config.kv_channels * config.num_attention_heads |
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self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads |
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self.num_attention_heads_per_partition = config.num_attention_heads |
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self.multi_query_attention = config.multi_query_attention |
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self.qkv_hidden_size = 3 * self.projection_size |
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if self.multi_query_attention: |
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self.num_multi_query_groups_per_partition = config.multi_query_group_num |
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self.qkv_hidden_size = ( |
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self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num |
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) |
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self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size, |
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bias=config.add_bias_linear or config.add_qkv_bias, |
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device=device, **_config_to_kwargs(config) |
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) |
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self.core_attention = CoreAttention(config, self.layer_number) |
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self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear, |
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device=device, **_config_to_kwargs(config) |
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) |
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def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): |
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if self.multi_query_attention: |
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num_attention_heads = self.num_multi_query_groups_per_partition |
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else: |
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num_attention_heads = self.num_attention_heads_per_partition |
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return torch.empty( |
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inference_max_sequence_len, |
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batch_size, |
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num_attention_heads, |
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self.hidden_size_per_attention_head, |
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dtype=dtype, |
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device=device, |
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) |
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def forward( |
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self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True |
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): |
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mixed_x_layer = self.query_key_value(hidden_states) |
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if self.multi_query_attention: |
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(query_layer, key_layer, value_layer) = mixed_x_layer.split( |
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[ |
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self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, |
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self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, |
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self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, |
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], |
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dim=-1, |
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) |
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query_layer = query_layer.view( |
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query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) |
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) |
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key_layer = key_layer.view( |
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key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) |
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) |
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value_layer = value_layer.view( |
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value_layer.size()[:-1] |
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+ (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) |
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) |
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else: |
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new_tensor_shape = mixed_x_layer.size()[:-1] + \ |
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(self.num_attention_heads_per_partition, |
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3 * self.hidden_size_per_attention_head) |
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mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) |
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(query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) |
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if rotary_pos_emb is not None: |
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query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) |
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key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) |
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if kv_cache is not None: |
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cache_k, cache_v = kv_cache |
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key_layer = torch.cat((cache_k, key_layer), dim=0) |
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value_layer = torch.cat((cache_v, value_layer), dim=0) |
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if use_cache: |
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kv_cache = (key_layer, value_layer) |
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else: |
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kv_cache = None |
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if self.multi_query_attention: |
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key_layer = key_layer.unsqueeze(-2) |
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key_layer = key_layer.expand( |
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-1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 |
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) |
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key_layer = key_layer.contiguous().view( |
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key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) |
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) |
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value_layer = value_layer.unsqueeze(-2) |
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value_layer = value_layer.expand( |
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-1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 |
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) |
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value_layer = value_layer.contiguous().view( |
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value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) |
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) |
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context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) |
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output = self.dense(context_layer) |
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return output, kv_cache |
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def _config_to_kwargs(args): |
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common_kwargs = { |
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"dtype": args.torch_dtype, |
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} |
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return common_kwargs |
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class MLP(torch.nn.Module): |
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"""MLP. |
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MLP will take the input with h hidden state, project it to 4*h |
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hidden dimension, perform nonlinear transformation, and project the |
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state back into h hidden dimension. |
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""" |
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def __init__(self, config: ChatGLMConfig, device=None): |
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super(MLP, self).__init__() |
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self.add_bias = config.add_bias_linear |
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self.dense_h_to_4h = nn.Linear( |
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config.hidden_size, |
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config.ffn_hidden_size * 2, |
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bias=self.add_bias, |
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device=device, |
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**_config_to_kwargs(config) |
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) |
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def swiglu(x): |
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x = torch.chunk(x, 2, dim=-1) |
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return F.silu(x[0]) * x[1] |
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self.activation_func = swiglu |
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self.dense_4h_to_h = nn.Linear( |
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config.ffn_hidden_size, |
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config.hidden_size, |
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bias=self.add_bias, |
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device=device, |
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**_config_to_kwargs(config) |
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) |
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def forward(self, hidden_states): |
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intermediate_parallel = self.dense_h_to_4h(hidden_states) |
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intermediate_parallel = self.activation_func(intermediate_parallel) |
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output = self.dense_4h_to_h(intermediate_parallel) |
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return output |
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class GLMBlock(torch.nn.Module): |
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"""A single transformer layer. |
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Transformer layer takes input with size [s, b, h] and returns an |
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output of the same size. |
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""" |
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def __init__(self, config: ChatGLMConfig, layer_number, device=None): |
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super(GLMBlock, self).__init__() |
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self.layer_number = layer_number |
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self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm |
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self.fp32_residual_connection = config.fp32_residual_connection |
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LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm |
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self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, |
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dtype=config.torch_dtype) |
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self.self_attention = SelfAttention(config, layer_number, device=device) |
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self.hidden_dropout = config.hidden_dropout |
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self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, |
|
dtype=config.torch_dtype) |
|
|
|
|
|
self.mlp = MLP(config, device=device) |
|
|
|
def forward( |
|
self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True, |
|
): |
|
|
|
|
|
|
|
layernorm_output = self.input_layernorm(hidden_states) |
|
|
|
attention_output, kv_cache = self.self_attention( |
|
layernorm_output, |
|
attention_mask, |
|
rotary_pos_emb, |
|
kv_cache=kv_cache, |
|
use_cache=use_cache |
|
) |
|
|
|
|
|
if self.apply_residual_connection_post_layernorm: |
|
residual = layernorm_output |
|
else: |
|
residual = hidden_states |
|
|
|
layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) |
|
layernorm_input = residual + layernorm_input |
|
|
|
|
|
layernorm_output = self.post_attention_layernorm(layernorm_input) |
|
|
|
|
|
mlp_output = self.mlp(layernorm_output) |
|
|
|
|
|
if self.apply_residual_connection_post_layernorm: |
|
residual = layernorm_output |
|
else: |
|
residual = layernorm_input |
|
|
|
output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) |
|
output = residual + output |
|
|
|
return output, kv_cache |
|
|
|
|
|
class GLMTransformer(torch.nn.Module): |
|
"""Transformer class.""" |
|
|
|
def __init__(self, config: ChatGLMConfig, device=None): |
|
super(GLMTransformer, self).__init__() |
|
|
|
self.fp32_residual_connection = config.fp32_residual_connection |
|
self.post_layer_norm = config.post_layer_norm |
|
|
|
|
|
self.num_layers = config.num_layers |
|
|
|
|
|
def build_layer(layer_number): |
|
return GLMBlock(config, layer_number, device=device) |
|
|
|
self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) |
|
|
|
if self.post_layer_norm: |
|
LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm |
|
|
|
self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, |
|
dtype=config.torch_dtype) |
|
|
|
self.gradient_checkpointing = False |
|
|
|
def _get_layer(self, layer_number): |
|
return self.layers[layer_number] |
|
|
|
def forward( |
|
self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None, |
|
use_cache: Optional[bool] = True, |
|
output_hidden_states: Optional[bool] = False, |
|
): |
|
if not kv_caches: |
|
kv_caches = [None for _ in range(self.num_layers)] |
|
presents = () if use_cache else None |
|
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 |
|
|
|
all_self_attentions = None |
|
all_hidden_states = () if output_hidden_states else None |
|
for index in range(self.num_layers): |
|
if output_hidden_states: |
|
all_hidden_states = all_hidden_states + (hidden_states,) |
|
|
|
layer = self._get_layer(index) |
|
if self.gradient_checkpointing and self.training: |
|
layer_ret = torch.utils.checkpoint.checkpoint( |
|
layer, |
|
hidden_states, |
|
attention_mask, |
|
rotary_pos_emb, |
|
kv_caches[index], |
|
use_cache |
|
) |
|
else: |
|
layer_ret = layer( |
|
hidden_states, |
|
attention_mask, |
|
rotary_pos_emb, |
|
kv_cache=kv_caches[index], |
|
use_cache=use_cache |
|
) |
|
hidden_states, kv_cache = layer_ret |
|
if use_cache: |
|
presents = presents + (kv_cache,) |
|
|
|
if output_hidden_states: |
|
all_hidden_states = all_hidden_states + (hidden_states,) |
|
|
|
|
|
if self.post_layer_norm: |
|
hidden_states = self.final_layernorm(hidden_states) |
|
|
|
return hidden_states, presents, all_hidden_states, all_self_attentions |
|
|
|
|
|
class ChatGLMPreTrainedModel(PreTrainedModel): |
|
""" |
|
An abstract class to handle weights initialization and |
|
a simple interface for downloading and loading pretrained models. |
|
""" |
|
|
|
is_parallelizable = False |
|
supports_gradient_checkpointing = True |
|
config_class = ChatGLMConfig |
|
base_model_prefix = "transformer" |
|
_no_split_modules = ["GLMBlock"] |
|
|
|
def _init_weights(self, module: nn.Module): |
|
"""Initialize the weights.""" |
|
return |
|
|
|
def get_masks(self, input_ids, past_key_values, padding_mask=None): |
|
batch_size, seq_length = input_ids.shape |
|
full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) |
|
full_attention_mask.tril_() |
|
past_length = 0 |
|
if past_key_values: |
|
past_length = past_key_values[0][0].shape[0] |
|
if past_length: |
|
full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length, |
|
device=input_ids.device), full_attention_mask), dim=-1) |
|
if padding_mask is not None: |
|
full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) |
|
if not past_length and padding_mask is not None: |
|
full_attention_mask -= padding_mask.unsqueeze(-1) - 1 |
|
full_attention_mask = (full_attention_mask < 0.5).bool() |
|
full_attention_mask.unsqueeze_(1) |
|
return full_attention_mask |
|
|
|
def get_position_ids(self, input_ids, device): |
|
batch_size, seq_length = input_ids.shape |
|
position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) |
|
return position_ids |
|
|
|
def _set_gradient_checkpointing(self, module, value=False): |
|
if isinstance(module, GLMTransformer): |
|
module.gradient_checkpointing = value |
|
|
|
|
|
class Embedding(torch.nn.Module): |
|
"""Language model embeddings.""" |
|
|
|
def __init__(self, config: ChatGLMConfig, device=None): |
|
super(Embedding, self).__init__() |
|
|
|
self.hidden_size = config.hidden_size |
|
|
|
self.word_embeddings = nn.Embedding( |
|
config.padded_vocab_size, |
|
self.hidden_size, |
|
dtype=config.torch_dtype, |
|
device=device |
|
) |
|
self.fp32_residual_connection = config.fp32_residual_connection |
|
|
|
def forward(self, input_ids): |
|
|
|
words_embeddings = self.word_embeddings(input_ids) |
|
embeddings = words_embeddings |
|
|
|
embeddings = embeddings.transpose(0, 1).contiguous() |
|
|
|
if self.fp32_residual_connection: |
|
embeddings = embeddings.float() |
|
return embeddings |
|
|
|
|
|
class ChatGLMModel(ChatGLMPreTrainedModel): |
|
def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): |
|
super().__init__(config) |
|
if empty_init: |
|
init_method = skip_init |
|
else: |
|
init_method = default_init |
|
init_kwargs = {} |
|
if device is not None: |
|
init_kwargs["device"] = device |
|
self.embedding = init_method(Embedding, config, **init_kwargs) |
|
self.num_layers = config.num_layers |
|
self.multi_query_group_num = config.multi_query_group_num |
|
self.kv_channels = config.kv_channels |
|
|
|
|
|
self.seq_length = config.seq_length |
|
rotary_dim = ( |
|
config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels |
|
) |
|
|
|
self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device, |
|
dtype=config.torch_dtype) |
|
self.encoder = init_method(GLMTransformer, config, **init_kwargs) |
|
self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False, |
|
dtype=config.torch_dtype, **init_kwargs) |
|
self.pre_seq_len = config.pre_seq_len |
|
self.prefix_projection = config.prefix_projection |
|
if self.pre_seq_len is not None: |
|
for param in self.parameters(): |
|
param.requires_grad = False |
|
self.prefix_tokens = torch.arange(self.pre_seq_len).long() |
|
self.prefix_encoder = PrefixEncoder(config) |
|
self.dropout = torch.nn.Dropout(0.1) |
|
|
|
def get_input_embeddings(self): |
|
return self.embedding.word_embeddings |
|
|
|
def get_prompt(self, batch_size, device, dtype=torch.half): |
|
prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device) |
|
past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) |
|
past_key_values = past_key_values.view( |
|
batch_size, |
|
self.pre_seq_len, |
|
self.num_layers * 2, |
|
self.multi_query_group_num, |
|
self.kv_channels |
|
) |
|
|
|
past_key_values = self.dropout(past_key_values) |
|
past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) |
|
return past_key_values |
|
|
|
def forward( |
|
self, |
|
input_ids, |
|
position_ids: Optional[torch.Tensor] = None, |
|
attention_mask: Optional[torch.BoolTensor] = None, |
|
full_attention_mask: Optional[torch.BoolTensor] = None, |
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, |
|
inputs_embeds: Optional[torch.Tensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
): |
|
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 |
|
|
|
batch_size, seq_length = input_ids.shape |
|
|
|
if inputs_embeds is None: |
|
inputs_embeds = self.embedding(input_ids) |
|
|
|
if self.pre_seq_len is not None: |
|
if past_key_values is None: |
|
past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device, |
|
dtype=inputs_embeds.dtype) |
|
if attention_mask is not None: |
|
attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), |
|
attention_mask], dim=-1) |
|
|
|
if full_attention_mask is None: |
|
if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): |
|
full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) |
|
|
|
|
|
rotary_pos_emb = self.rotary_pos_emb(self.seq_length) |
|
if position_ids is not None: |
|
rotary_pos_emb = rotary_pos_emb[position_ids] |
|
else: |
|
rotary_pos_emb = rotary_pos_emb[None, :seq_length] |
|
rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() |
|
|
|
|
|
hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( |
|
inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb, |
|
kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states |
|
) |
|
|
|
if not return_dict: |
|
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) |
|
|
|
return BaseModelOutputWithPast( |
|
last_hidden_state=hidden_states, |
|
past_key_values=presents, |
|
hidden_states=all_hidden_states, |
|
attentions=all_self_attentions, |
|
) |
|
|
|
def quantize(self, weight_bit_width: int): |
|
from .quantization import quantize |
|
quantize(self.encoder, weight_bit_width) |
|
return self |
|
|
|
|
|
class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): |
|
def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): |
|
super().__init__(config) |
|
|
|
self.max_sequence_length = config.max_length |
|
self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) |
|
self.config = config |
|
self.quantized = False |
|
|
|
if self.config.quantization_bit: |
|
self.quantize(self.config.quantization_bit, empty_init=True) |
|
|
|
def _update_model_kwargs_for_generation( |
|
self, |
|
outputs: ModelOutput, |
|
model_kwargs: Dict[str, Any], |
|
is_encoder_decoder: bool = False, |
|
standardize_cache_format: bool = False, |
|
) -> Dict[str, Any]: |
|
|
|
model_kwargs["past_key_values"] = self._extract_past_from_model_output( |
|
outputs, standardize_cache_format=standardize_cache_format |
|
) |
|
|
|
|
|
if "attention_mask" in model_kwargs: |
|
attention_mask = model_kwargs["attention_mask"] |
|
model_kwargs["attention_mask"] = torch.cat( |
|
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 |
|
) |
|
|
|
|
|
if "position_ids" in model_kwargs: |
|
position_ids = model_kwargs["position_ids"] |
|
new_position_id = position_ids[..., -1:].clone() |
|
new_position_id += 1 |
|
model_kwargs["position_ids"] = torch.cat( |
|
[position_ids, new_position_id], dim=-1 |
|
) |
|
|
|
model_kwargs["is_first_forward"] = False |
|
return model_kwargs |
|
|
|
def prepare_inputs_for_generation( |
|
self, |
|
input_ids: torch.LongTensor, |
|
past_key_values: Optional[torch.Tensor] = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.Tensor] = None, |
|
use_cache: Optional[bool] = None, |
|
is_first_forward: bool = True, |
|
**kwargs |
|
) -> dict: |
|
|
|
if position_ids is None: |
|
position_ids = self.get_position_ids(input_ids, device=input_ids.device) |
|
if not is_first_forward: |
|
if past_key_values is not None: |
|
position_ids = position_ids[..., -1:] |
|
input_ids = input_ids[:, -1:] |
|
return { |
|
"input_ids": input_ids, |
|
"past_key_values": past_key_values, |
|
"position_ids": position_ids, |
|
"attention_mask": attention_mask, |
|
"return_last_logit": True, |
|
"use_cache": use_cache |
|
} |
|
|
|
def forward( |
|
self, |
|
input_ids: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.Tensor] = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
past_key_values: Optional[Tuple[torch.FloatTensor]] = None, |
|
inputs_embeds: Optional[torch.Tensor] = None, |
|
labels: Optional[torch.Tensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
return_last_logit: Optional[bool] = False, |
|
): |
|
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 |
|
|
|
transformer_outputs = self.transformer( |
|
input_ids=input_ids, |
|
position_ids=position_ids, |
|
attention_mask=attention_mask, |
|
past_key_values=past_key_values, |
|
inputs_embeds=inputs_embeds, |
|
use_cache=use_cache, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
hidden_states = transformer_outputs[0] |
|
if return_last_logit: |
|
hidden_states = hidden_states[-1:] |
|
lm_logits = self.transformer.output_layer(hidden_states) |
|
lm_logits = lm_logits.transpose(0, 1).contiguous() |
|
|
|
loss = None |
|
if labels is not None: |
|
lm_logits = lm_logits.to(torch.float32) |
|
|
|
|
|
shift_logits = lm_logits[..., :-1, :].contiguous() |
|
shift_labels = labels[..., 1:].contiguous() |
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=-100) |
|
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) |
|
|
|
lm_logits = lm_logits.to(hidden_states.dtype) |
|
loss = loss.to(hidden_states.dtype) |
|
|
|
if not return_dict: |
|
output = (lm_logits,) + transformer_outputs[1:] |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return CausalLMOutputWithPast( |
|
loss=loss, |
|
logits=lm_logits, |
|
past_key_values=transformer_outputs.past_key_values, |
|
hidden_states=transformer_outputs.hidden_states, |
|
attentions=transformer_outputs.attentions, |
|
) |
|
|
|
@staticmethod |
|
def _reorder_cache( |
|
past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor |
|
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: |
|
""" |
|
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or |
|
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct |
|
beam_idx at every generation step. |
|
|
|
Output shares the same memory storage as `past`. |
|
""" |
|
return tuple( |
|
( |
|
layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), |
|
layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), |
|
) |
|
for layer_past in past |
|
) |
|
|
|
def process_response(self, response): |
|
response = response.strip() |
|
response = response.replace("[[训练时间]]", "2023年") |
|
return response |
|
|
|
def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): |
|
prompt = tokenizer.build_prompt(query, history=history) |
|
inputs = tokenizer([prompt], return_tensors="pt") |
|
inputs = inputs.to(self.device) |
|
return inputs |
|
|
|
def build_stream_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): |
|
if history: |
|
prompt = "\n\n[Round {}]\n\n问:{}\n\n答:".format(len(history) + 1, query) |
|
input_ids = tokenizer.encode(prompt, add_special_tokens=False) |
|
input_ids = input_ids[1:] |
|
inputs = tokenizer.batch_encode_plus([(input_ids, None)], return_tensors="pt", add_special_tokens=False) |
|
else: |
|
prompt = "[Round {}]\n\n问:{}\n\n答:".format(len(history) + 1, query) |
|
inputs = tokenizer([prompt], return_tensors="pt") |
|
inputs = inputs.to(self.device) |
|
return inputs |
|
|
|
@torch.inference_mode() |
|
def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 8192, num_beams=1, |
|
do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, **kwargs): |
|
if history is None: |
|
history = [] |
|
if logits_processor is None: |
|
logits_processor = LogitsProcessorList() |
|
logits_processor.append(InvalidScoreLogitsProcessor()) |
|
gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, |
|
"temperature": temperature, "logits_processor": logits_processor, **kwargs} |
|
inputs = self.build_inputs(tokenizer, query, history=history) |
|
outputs = self.generate(**inputs, **gen_kwargs) |
|
outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] |
|
response = tokenizer.decode(outputs) |
|
response = self.process_response(response) |
|
history = history + [(query, response)] |
|
return response, history |
|
|
|
@torch.inference_mode() |
|
def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, past_key_values=None, |
|
max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, |
|
return_past_key_values=False, **kwargs): |
|
if history is None: |
|
history = [] |
|
if logits_processor is None: |
|
logits_processor = LogitsProcessorList() |
|
logits_processor.append(InvalidScoreLogitsProcessor()) |
|
gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p, |
|
"temperature": temperature, "logits_processor": logits_processor, **kwargs} |
|
if past_key_values is None and not return_past_key_values: |
|
inputs = self.build_inputs(tokenizer, query, history=history) |
|
else: |
|
inputs = self.build_stream_inputs(tokenizer, query, history=history) |
|
if past_key_values is not None: |
|
past_length = past_key_values[0][0].shape[0] |
|
if self.transformer.pre_seq_len is not None: |
|
past_length -= self.transformer.pre_seq_len |
|
inputs.position_ids += past_length |
|
attention_mask = inputs.attention_mask |
|
attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) |
|
inputs['attention_mask'] = attention_mask |
|
for outputs in self.stream_generate(**inputs, past_key_values=past_key_values, |
|
return_past_key_values=return_past_key_values, **gen_kwargs): |
|
if return_past_key_values: |
|
outputs, past_key_values = outputs |
|
outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] |
|
response = tokenizer.decode(outputs) |
|
if response and response[-1] != "�": |
|
response = self.process_response(response) |
|
new_history = history + [(query, response)] |
|
if return_past_key_values: |
|
yield response, new_history, past_key_values |
|
else: |
|
yield response, new_history |
|
|
|
@torch.inference_mode() |
|
def stream_generate( |
|
self, |
|
input_ids, |
|
generation_config: Optional[GenerationConfig] = None, |
|
logits_processor: Optional[LogitsProcessorList] = None, |
|
stopping_criteria: Optional[StoppingCriteriaList] = None, |
|
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, |
|
return_past_key_values=False, |
|
**kwargs, |
|
): |
|
batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] |
|
|
|
if generation_config is None: |
|
generation_config = self.generation_config |
|
generation_config = copy.deepcopy(generation_config) |
|
model_kwargs = generation_config.update(**kwargs) |
|
model_kwargs["use_cache"] = generation_config.use_cache |
|
bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id |
|
|
|
if isinstance(eos_token_id, int): |
|
eos_token_id = [eos_token_id] |
|
|
|
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None |
|
if has_default_max_length and generation_config.max_new_tokens is None: |
|
warnings.warn( |
|
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " |
|
"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" |
|
" recommend using `max_new_tokens` to control the maximum length of the generation.", |
|
UserWarning, |
|
) |
|
elif generation_config.max_new_tokens is not None: |
|
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length |
|
if not has_default_max_length: |
|
logger.warn( |
|
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" |
|
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " |
|
"Please refer to the documentation for more information. " |
|
"(https://huggingface.co./docs/transformers/main/en/main_classes/text_generation)", |
|
UserWarning, |
|
) |
|
|
|
if input_ids_seq_length >= generation_config.max_length: |
|
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" |
|
logger.warning( |
|
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" |
|
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" |
|
" increasing `max_new_tokens`." |
|
) |
|
|
|
|
|
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() |
|
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() |
|
|
|
logits_processor = self._get_logits_processor( |
|
generation_config=generation_config, |
|
input_ids_seq_length=input_ids_seq_length, |
|
encoder_input_ids=input_ids, |
|
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, |
|
logits_processor=logits_processor, |
|
) |
|
|
|
stopping_criteria = self._get_stopping_criteria( |
|
generation_config=generation_config, stopping_criteria=stopping_criteria |
|
) |
|
logits_warper = self._get_logits_warper(generation_config) |
|
|
|
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) |
|
scores = None |
|
while True: |
|
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) |
|
|
|
outputs = self( |
|
**model_inputs, |
|
return_dict=True, |
|
output_attentions=False, |
|
output_hidden_states=False, |
|
) |
|
|
|
next_token_logits = outputs.logits[:, -1, :] |
|
|
|
|
|
next_token_scores = logits_processor(input_ids, next_token_logits) |
|
next_token_scores = logits_warper(input_ids, next_token_scores) |
|
|
|
|
|
probs = nn.functional.softmax(next_token_scores, dim=-1) |
|
if generation_config.do_sample: |
|
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) |
|
else: |
|
next_tokens = torch.argmax(probs, dim=-1) |
|
|
|
|
|
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) |
|
model_kwargs = self._update_model_kwargs_for_generation( |
|
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder |
|
) |
|
unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long()) |
|
if return_past_key_values: |
|
yield input_ids, outputs.past_key_values |
|
else: |
|
yield input_ids |
|
|
|
if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): |
|
break |
|
|
|
def quantize(self, bits: int, empty_init=False, device=None, **kwargs): |
|
if bits == 0: |
|
return |
|
|
|
from .quantization import quantize |
|
|
|
if self.quantized: |
|
logger.info("Already quantized.") |
|
return self |
|
|
|
self.quantized = True |
|
|
|
self.config.quantization_bit = bits |
|
|
|
self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device, |
|
**kwargs) |
|
return self |
|
|
|
|
|
class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel): |
|
def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): |
|
super().__init__(config) |
|
|
|
self.num_labels = config.num_labels |
|
self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) |
|
|
|
self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half) |
|
if config.classifier_dropout is not None: |
|
self.dropout = nn.Dropout(config.classifier_dropout) |
|
else: |
|
self.dropout = None |
|
self.config = config |
|
|
|
if self.config.quantization_bit: |
|
self.quantize(self.config.quantization_bit, empty_init=True) |
|
|
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
full_attention_mask: Optional[torch.Tensor] = None, |
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, |
|
inputs_embeds: Optional[torch.LongTensor] = None, |
|
labels: Optional[torch.LongTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]: |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
transformer_outputs = self.transformer( |
|
input_ids=input_ids, |
|
position_ids=position_ids, |
|
attention_mask=attention_mask, |
|
full_attention_mask=full_attention_mask, |
|
past_key_values=past_key_values, |
|
inputs_embeds=inputs_embeds, |
|
use_cache=use_cache, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
hidden_states = transformer_outputs[0] |
|
pooled_hidden_states = hidden_states[-1] |
|
if self.dropout is not None: |
|
pooled_hidden_states = self.dropout(pooled_hidden_states) |
|
logits = self.classifier_head(pooled_hidden_states) |
|
|
|
loss = None |
|
if labels is not None: |
|
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(logits.squeeze().float(), labels.squeeze()) |
|
else: |
|
loss = loss_fct(logits.float(), labels) |
|
elif self.config.problem_type == "single_label_classification": |
|
loss_fct = CrossEntropyLoss() |
|
loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1)) |
|
elif self.config.problem_type == "multi_label_classification": |
|
loss_fct = BCEWithLogitsLoss() |
|
loss = loss_fct(logits.float(), labels.view(-1, self.num_labels)) |
|
|
|
if not return_dict: |
|
output = (logits,) + transformer_outputs[1:] |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return SequenceClassifierOutputWithPast( |
|
loss=loss, |
|
logits=logits, |
|
past_key_values=transformer_outputs.past_key_values, |
|
hidden_states=transformer_outputs.hidden_states, |
|
attentions=transformer_outputs.attentions, |
|
) |
|
|