431 lines
16 KiB
Python
431 lines
16 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright 2021-2022 The Alibaba DAMO NLP Team Authors.
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import os
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from typing import Optional, Union
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import addict
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import torch
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from torch import nn
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from torch.nn import functional as F
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from transformers.modeling_utils import PreTrainedModel
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from modelscope.outputs import TokenGeneratorOutput
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from modelscope.utils.constant import ModelFile
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from .configuration import GPT3Config
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from .distributed_gpt3 import sample
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class GPT3SelfAttention(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):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.num_attention_heads = config.num_attention_heads
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# Per attention head
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self.hidden_size_per_attention_head = \
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self.hidden_size // self.num_attention_heads
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self.query_key_value = nn.Linear(self.hidden_size,
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3 * self.hidden_size)
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self.softmax = nn.Softmax(dim=-1)
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self.attention_dropout = nn.Dropout(
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config.attention_probs_dropout_prob)
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# Output.
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self.dense = nn.Linear(self.hidden_size, self.hidden_size)
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self.output_dropout = nn.Dropout(config.hidden_dropout_prob)
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def _transpose_for_scores(self, tensor):
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"""Transpose a 3D tensor [b, s, np*hn] into a 4D tensor with
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size [b, np, s, hn].
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"""
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new_tensor_shape = tensor.size()[:-1] + (
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self.num_attention_heads, self.hidden_size_per_attention_head)
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tensor = tensor.view(*new_tensor_shape)
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return tensor.permute(0, 2, 1, 3)
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def _split_tensor_along_last_dim(self,
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tensor,
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num_partitions,
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contiguous_split_chunks=False):
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# Get the size and dimension.
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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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# Split.
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tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
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# Note: torch.split does not create contiguous tensors by default.
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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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def forward(self, hidden_states, ltor_mask, is_infer=False):
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# hidden_states: [b, s, h]
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# ltor_mask: [1, 1, s, s]
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# Attention heads. [b, s, hp]
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tgt_len = hidden_states.size(1)
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ltor_mask = torch.reshape(ltor_mask, [1, 1, tgt_len, tgt_len])
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mixed_x_layer = self.query_key_value(hidden_states)
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(mixed_query_layer, mixed_key_layer, mixed_value_layer) = \
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self._split_tensor_along_last_dim(mixed_x_layer, 3)
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# Reshape and transpose [b, np, s, hn]
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query_layer = self._transpose_for_scores(mixed_query_layer)
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key_layer = self._transpose_for_scores(mixed_key_layer)
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value_layer = self._transpose_for_scores(mixed_value_layer)
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previous_type = value_layer.type()
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# Raw attention scores. [b, np, s, s]
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attention_scores = torch.matmul(query_layer,
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key_layer.transpose(-1, -2))
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attention_scores = attention_scores / math.sqrt(
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self.hidden_size_per_attention_head)
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# Apply the left to right attention mask.
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if is_infer:
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src_len = key_layer.size(2)
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ltor_mask = torch.tril(
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torch.ones((1, tgt_len, src_len),
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device=hidden_states.device)).view(
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1, 1, tgt_len, src_len).type(previous_type)
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converted_mask = 10000.0 * (1.0 - ltor_mask)
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attention_scores = (torch.mul(attention_scores, ltor_mask)
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- converted_mask).type(previous_type)
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# Attention probabilities. [b, np, s, s]
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attention_probs = self.softmax(attention_scores)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.attention_dropout(attention_probs)
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# Context layer.
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# [b, np, s, hn]
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context_layer = torch.matmul(attention_probs, value_layer)
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# [b, s, np, hn]
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (
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self.hidden_size, )
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# [b, s, hp]
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context_layer = context_layer.view(*new_context_layer_shape)
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# Output. [b, s, h]
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output = self.dense(context_layer)
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output = self.output_dropout(output)
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return output
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class GPT3MLP(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):
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super().__init__()
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hidden_size = config.hidden_size
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# Project to 4h.
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self.dense_h_to_4h = nn.Linear(hidden_size, 4 * hidden_size)
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self.activation_func = F.gelu
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# Project back to h.
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self.dense_4h_to_h = nn.Linear(4 * hidden_size, hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states):
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# [s, b, 4hp]
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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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# [s, b, h]
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output = self.dense_4h_to_h(intermediate_parallel)
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output = self.dropout(output)
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return output
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class GPT3TransformerLayer(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):
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super().__init__()
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# Layernorm on the input data.
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self.input_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.layernorm_epsilon)
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# Self attention.
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self.attention = GPT3SelfAttention(config)
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# Layernorm on the attention output
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self.post_attention_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.layernorm_epsilon)
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# MLP
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self.mlp = GPT3MLP(config)
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def forward(self, hidden_states, ltor_mask):
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# hidden_states: [b, s, h]
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# ltor_mask: [1, 1, s, s]
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# Layer norm at the begining of the transformer layer.
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layernorm_output = self.input_layernorm(hidden_states)
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# Self attention.
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attention_output = self.attention(layernorm_output, ltor_mask)
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# Residual connection.
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layernorm_input = hidden_states + attention_output
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# Layer norm post the self attention.
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layernorm_output = self.post_attention_layernorm(layernorm_input)
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# MLP.
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mlp_output = self.mlp(layernorm_output)
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# Second residual connection.
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output = layernorm_input + mlp_output
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return output
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class GPT3Transformer(nn.Module):
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"""Transformer class."""
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def __init__(self, config):
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super().__init__()
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self.input_tensor = None
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# Number of layers.
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self.num_layers = config.num_hidden_layers
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self.layers = torch.nn.ModuleList(
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[GPT3TransformerLayer(config) for _ in range(self.num_layers)])
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# Final layer norm before output.
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self.final_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.layernorm_epsilon)
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def _get_layer(self, layer_number):
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return self.layers[layer_number]
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def forward(self, hidden_states, attention_mask):
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# hidden_states: [s, b, h]
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for index in range(self.num_layers):
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layer = self._get_layer(index)
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hidden_states = layer(hidden_states, attention_mask)
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# Final layer norm.
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states
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class GPT3TransformerLanguageModel(nn.Module):
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"""Transformer language model.
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Arguments:
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transformer_hparams: transformer hyperparameters
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vocab_size: vocabulary size
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max_sequence_length: maximum size of sequence. This
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is used for positional embedding
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embedding_dropout_prob: dropout probability for embeddings
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num_tokentypes: size of the token-type embeddings. 0 value
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will ignore this embedding
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"""
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def __init__(self, config):
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super().__init__()
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# Embeddings.
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self.word_embeddings = nn.Embedding(config.vocab_size,
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config.hidden_size)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings,
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config.hidden_size)
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self.embedding_dropout = nn.Dropout(config.hidden_dropout_prob)
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# Transformer.
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self.transformer = GPT3Transformer(config)
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def forward(self, input_ids, attention_mask, position_ids):
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words_embeddings = self.word_embeddings(input_ids)
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position_embeddings = self.position_embeddings(position_ids)
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embeddings = words_embeddings + position_embeddings
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transformer_input = self.embedding_dropout(embeddings)
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transformer_output = self.transformer(transformer_input,
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attention_mask)
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logits = F.linear(transformer_output, self.word_embeddings.weight)
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return logits
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class GPT3Model(PreTrainedModel):
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config_class = GPT3Config
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def _init_weights(self, module):
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"""Initialize the weights"""
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if isinstance(module, nn.Linear):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(
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mean=0.0, std=self.config.initializer_range)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(
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mean=0.0, std=self.config.initializer_range)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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def __init__(self, config):
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super().__init__(config)
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self.language_model = GPT3TransformerLanguageModel(config)
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def forward(self,
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input_ids,
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attention_mask=None,
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position_ids=None,
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labels=None,
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**kwargs):
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seq_length = input_ids.size(1)
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attention_mask = torch.tril(
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torch.ones((1, 1, seq_length, seq_length),
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dtype=torch.long,
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device=input_ids.device))
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if position_ids is None:
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position_ids = torch.arange(
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seq_length, dtype=torch.long, device=input_ids.device)
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position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
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logits = self.language_model(input_ids, attention_mask, position_ids)
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loss = None
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if labels is not None:
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loss_fct = nn.CrossEntropyLoss()
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loss = loss_fct(
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logits.view(-1, self.config.vocab_size), labels.view(-1))
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return addict.Dict(loss=loss, logits=logits)
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@classmethod
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def from_pretrained(
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cls, pretrained_model_name_or_path: Optional[Union[str,
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os.PathLike]]):
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config = cls.config_class.from_pretrained(
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pretrained_model_name_or_path)
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model = cls(config)
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state_dict_file = os.path.join(pretrained_model_name_or_path,
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ModelFile.TORCH_MODEL_BIN_FILE)
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state_dict = torch.load(state_dict_file)
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if 'state_dict' in state_dict:
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state_dict = state_dict['state_dict']
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state_dict = {
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k.replace('model.language_model', 'language_model'): v
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for k, v in state_dict.items()
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}
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model.load_state_dict(state_dict)
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return model
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def streaming_generate(self, tokens, temperature=1.0, **kwargs):
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top_k = kwargs.pop('top_k', self.config.top_k)
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top_p = kwargs.pop('top_p', self.config.top_p)
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max_length = kwargs.pop('max_length', tokens.size(1) + 100)
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batch_size = tokens.size(0)
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lengths = kwargs.pop(
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'prompt_length',
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torch.tensor([tokens.size(1)], device=tokens.device))
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min_prompt_length = lengths.min().item()
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max_sequence_length = min(max_length,
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self.config.max_position_embeddings)
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# If the context is too big, this happens
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if min_prompt_length >= max_sequence_length:
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raise ValueError('context length too large')
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pad_length = max_sequence_length - tokens.size(1)
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if pad_length > 0:
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pads = torch.zeros(
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batch_size, pad_length, device=tokens.device).long()
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tokens = torch.cat((tokens, pads), dim=-1)
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# Added termination_id to support the case that we want to terminate the
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# generation once that id is generated.
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termination_id = self.config.eod_id
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# Whether we have reached a termination id.
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is_generation_done = torch.zeros(
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batch_size, dtype=torch.uint8, device=tokens.device)
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with torch.no_grad():
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for context_length in range(min_prompt_length,
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max_sequence_length):
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# Pick the slice that we need to pass through the network.
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tokens2use = tokens[:, :context_length]
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# logits will be meanigful only in the last pipeline stage.
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logits = self(tokens2use).logits
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# Sample.
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last_token_logits = logits[:, -1, :]
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new_sample = sample(
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last_token_logits,
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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vocab_size=self.config.vocab_size)
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# If a prompt length is smaller or equal th current context
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# length, it means we have started generating tokens
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started = lengths <= context_length
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# Update the tokens.
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tokens[started, context_length] = new_sample[started]
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yield TokenGeneratorOutput(sequences=tokens[:, :(context_length
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+ 1)])
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done_token = (new_sample == termination_id).byte() & \
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started.byte()
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is_generation_done = is_generation_done | done_token
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done = torch.all(is_generation_done)
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if done:
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break
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def generate(self, tokens, temperature=1.0, **kwargs):
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last_output = None
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for output in self.streaming_generate(tokens, temperature, **kwargs):
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last_output = output
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return last_output
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