Create fid.py

fid.py

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+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import types
+import torch
+import transformers
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import CrossEntropyLoss
+import numpy as np
+
+class FiDT5(transformers.T5ForConditionalGeneration):
+    def __init__(self, config):
+        super().__init__(config)
+        self.wrap_encoder()
+
+    def forward_(self, **kwargs):
+        if 'input_ids' in kwargs:
+            kwargs['input_ids'] = kwargs['input_ids'].view(kwargs['input_ids'].size(0), -1)
+        if 'attention_mask' in kwargs:
+            kwargs['attention_mask'] = kwargs['attention_mask'].view(kwargs['attention_mask'].size(0), -1)
+
+        return super(FiDT5, self).forward(
+            **kwargs
+        )
+
+    # We need to resize as B x (N * L) instead of (B * N) x L here
+    # because the T5 forward method uses the input tensors to infer
+    # dimensions used in the decoder.
+    # EncoderWrapper resizes the inputs as (B * N) x L.
+    def forward(self, input_ids=None, attention_mask=None, **kwargs):
+        if input_ids != None:
+            # inputs might have already be resized in the generate method
+            if input_ids.dim() == 3:
+                self.encoder.n_passages = input_ids.size(1)
+            input_ids = input_ids.view(input_ids.size(0), -1)
+        if attention_mask != None:
+            attention_mask = attention_mask.view(attention_mask.size(0), -1)
+        return super().forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            **kwargs
+        )
+
+    # We need to resize the inputs here, as the generate method expect 2D tensors
+    def generate(self, input_ids, attention_mask, max_length):
+        self.encoder.n_passages = input_ids.size(1)
+        return super().generate(
+            input_ids=input_ids.view(input_ids.size(0), -1),
+            attention_mask=attention_mask.view(attention_mask.size(0), -1),
+            max_length=max_length
+        )
+
+    def wrap_encoder(self, use_checkpoint=False):
+        """
+        Wrap T5 encoder to obtain a Fusion-in-Decoder model.
+        """
+        self.encoder = EncoderWrapper(self.encoder, use_checkpoint=use_checkpoint)
+
+    def unwrap_encoder(self):
+        """
+        Unwrap Fusion-in-Decoder encoder, useful to load T5 weights.
+        """
+        self.encoder = self.encoder.encoder
+        block = []
+        for mod in self.encoder.block:
+            block.append(mod.module)
+        block = nn.ModuleList(block)
+        self.encoder.block = block
+
+    def load_t5(self, state_dict):
+        self.unwrap_encoder()
+        self.load_state_dict(state_dict)
+        self.wrap_encoder()
+
+    def set_checkpoint(self, use_checkpoint):
+        """
+        Enable or disable checkpointing in the encoder.
+        See https://pytorch.org/docs/stable/checkpoint.html
+        """
+        for mod in self.encoder.encoder.block:
+            mod.use_checkpoint = use_checkpoint
+
+    def reset_score_storage(self):
+        """
+        Reset score storage, only used when cross-attention scores are saved
+        to train a retriever.
+        """
+        for mod in self.decoder.block:
+            mod.layer[1].EncDecAttention.score_storage = None
+
+    def get_crossattention_scores(self, context_mask):
+        """
+        Cross-attention scores are aggregated to obtain a single scalar per
+        passage. This scalar can be seen as a similarity score between the
+        question and the input passage. It is obtained by averaging the
+        cross-attention scores obtained on the first decoded token over heads,
+        layers, and tokens of the input passage.
+        More details in Distilling Knowledge from Reader to Retriever:
+        https://arxiv.org/abs/2012.04584.
+        """
+        scores = []
+        n_passages = context_mask.size(1)
+        for mod in self.decoder.block:
+            scores.append(mod.layer[1].EncDecAttention.score_storage)
+        scores = torch.cat(scores, dim=2)
+        bsz, n_heads, n_layers, _ = scores.size()
+        # batch_size, n_head, n_layers, n_passages, text_maxlength
+        scores = scores.view(bsz, n_heads, n_layers, n_passages, -1)
+        scores = scores.masked_fill(~context_mask[:, None, None], 0.)
+        scores = scores.sum(dim=[1, 2, 4])
+        ntokens = context_mask.sum(dim=[2]) * n_layers * n_heads
+        scores = scores/ntokens
+        return scores
+
+    def overwrite_forward_crossattention(self):
+        """
+        Replace cross-attention forward function, only used to save
+        cross-attention scores.
+        """
+        for mod in self.decoder.block:
+            attn = mod.layer[1].EncDecAttention
+            attn.forward = types.MethodType(cross_attention_forward, attn)
+
+class EncoderWrapper(torch.nn.Module):
+    """
+    Encoder Wrapper for T5 Wrapper to obtain a Fusion-in-Decoder model.
+    """
+    def __init__(self, encoder, use_checkpoint=False):
+        super().__init__()
+
+        self.encoder = encoder
+        apply_checkpoint_wrapper(self.encoder, use_checkpoint)
+
+    def forward(self, input_ids=None, attention_mask=None, **kwargs,):
+        # total_length = n_passages * passage_length
+        bsz, total_length = input_ids.shape
+        passage_length = total_length // self.n_passages
+        input_ids = input_ids.view(bsz*self.n_passages, passage_length)
+        attention_mask = attention_mask.view(bsz*self.n_passages, passage_length)
+        outputs = self.encoder(input_ids, attention_mask, **kwargs)
+        outputs = (outputs[0].view(bsz, self.n_passages*passage_length, -1), ) + outputs[1:]
+        return outputs
+
+class CheckpointWrapper(torch.nn.Module):
+    """
+    Wrapper replacing None outputs by empty tensors, which allows the use of
+    checkpointing.
+    """
+    def __init__(self, module, use_checkpoint=False):
+        super().__init__()
+        self.module = module
+        self.use_checkpoint = use_checkpoint
+
+    def forward(self, hidden_states, attention_mask, position_bias, **kwargs):
+        if self.use_checkpoint and self.training:
+            kwargs = {k: v for k, v in kwargs.items() if v is not None}
+            def custom_forward(*inputs):
+                output = self.module(*inputs, **kwargs)
+                empty = torch.tensor(
+                    [],
+                    dtype=torch.float,
+                    device=output[0].device,
+                    requires_grad=True)
+                output = tuple(x if x is not None else empty for x in output)
+                return output
+
+            output = torch.utils.checkpoint.checkpoint(
+                custom_forward,
+                hidden_states,
+                attention_mask,
+                position_bias
+            )
+            output = tuple(x if x.size() != 0 else None for x in output)
+        else:
+            output = self.module(hidden_states, attention_mask, position_bias, **kwargs)
+        return output
+
+def apply_checkpoint_wrapper(t5stack, use_checkpoint):
+    """
+    Wrap each block of the encoder to enable checkpointing.
+    """
+    block = []
+    for mod in t5stack.block:
+        wrapped_mod = CheckpointWrapper(mod, use_checkpoint)
+        block.append(wrapped_mod)
+    block = nn.ModuleList(block)
+    t5stack.block = block
+
+def cross_attention_forward(
+        self,
+        input,
+        mask=None,
+        kv=None,
+        position_bias=None,
+        past_key_value_state=None,
+        head_mask=None,
+        query_length=None,
+        use_cache=False,
+        output_attentions=False,
+    ):
+    """
+    This only works for computing cross attention over the input
+    """
+    assert(kv != None)
+    assert(head_mask == None)
+    assert(position_bias != None or self.has_relative_attention_bias)
+
+    bsz, qlen, dim = input.size()
+    n_heads, d_heads = self.n_heads, self.d_kv
+    klen = kv.size(1)
+
+    q = self.q(input).view(bsz, -1, n_heads, d_heads).transpose(1, 2)
+    if past_key_value_state == None:
+        k = self.k(kv).view(bsz, -1, n_heads, d_heads).transpose(1, 2)
+        v = self.v(kv).view(bsz, -1, n_heads, d_heads).transpose(1, 2)
+    else:
+        k, v = past_key_value_state
+
+    scores = torch.einsum("bnqd,bnkd->bnqk", q, k)
+
+    if mask is not None:
+       scores += mask
+
+    if position_bias is None:
+        position_bias = self.compute_bias(qlen, klen)
+    scores += position_bias
+
+    if self.score_storage is None:
+        self.score_storage = scores
+
+    attn = F.softmax(scores.float(), dim=-1).type_as(scores)
+    attn = F.dropout(attn, p=self.dropout, training=self.training)
+
+    output = torch.matmul(attn, v)
+    output = output.transpose(1, 2).contiguous().view(bsz, -1, self.inner_dim)
+    output = self.o(output)
+
+    if use_cache:
+        output = (output,) + ((k, v),)
+    else:
+        output = (output,) + (None,)
+
+    if output_attentions:
+        output = output + (attn,)
+
+    if self.has_relative_attention_bias:
+        output = output + (position_bias,)
+
+    return output
+
+class RetrieverConfig(transformers.BertConfig):
+
+    def __init__(self,
+                 indexing_dimension=768,
+                 apply_question_mask=False,
+                 apply_passage_mask=False,
+                 extract_cls=False,
+                 passage_maxlength=200,
+                 question_maxlength=40,
+                 projection=True,
+                 **kwargs):
+        super().__init__(**kwargs)
+        self.indexing_dimension = indexing_dimension
+        self.apply_question_mask = apply_question_mask
+        self.apply_passage_mask = apply_passage_mask
+        self.extract_cls=extract_cls
+        self.passage_maxlength = passage_maxlength
+        self.question_maxlength = question_maxlength
+        self.projection = projection
+
+class Retriever(transformers.PreTrainedModel):
+
+    config_class = RetrieverConfig
+    base_model_prefix = "retriever"
+
+    def __init__(self, config, initialize_wBERT=False):
+        super().__init__(config)
+        assert config.projection or config.indexing_dimension == 768, \
+            'If no projection then indexing dimension must be equal to 768'
+        self.config = config
+        if initialize_wBERT:
+            self.model = transformers.BertModel.from_pretrained('bert-base-uncased')
+        else:
+            self.model = transformers.BertModel(config)
+        if self.config.projection:
+            self.proj = nn.Linear(
+                self.model.config.hidden_size,
+                self.config.indexing_dimension
+            )
+            self.norm = nn.LayerNorm(self.config.indexing_dimension)
+        self.loss_fct = torch.nn.KLDivLoss()
+
+    def forward(self,
+                question_ids,
+                question_mask,
+                passage_ids,
+                passage_mask,
+                gold_score=None):
+        question_output = self.embed_text(
+            text_ids=question_ids,
+            text_mask=question_mask,
+            apply_mask=self.config.apply_question_mask,
+            extract_cls=self.config.extract_cls,
+        )
+        bsz, n_passages, plen = passage_ids.size()
+        passage_ids = passage_ids.view(bsz * n_passages, plen)
+        passage_mask = passage_mask.view(bsz * n_passages, plen)
+        passage_output = self.embed_text(
+            text_ids=passage_ids,
+            text_mask=passage_mask,
+            apply_mask=self.config.apply_passage_mask,
+            extract_cls=self.config.extract_cls,
+        )
+
+        score = torch.einsum(
+            'bd,bid->bi',
+            question_output,
+            passage_output.view(bsz, n_passages, -1)
+        )
+        score = score / np.sqrt(question_output.size(-1))
+        if gold_score is not None:
+            loss = self.kldivloss(score, gold_score)
+        else:
+            loss = None
+
+        return question_output, passage_output, score, loss
+
+    def embed_text(self, text_ids, text_mask, apply_mask=False, extract_cls=False):
+        text_output = self.model(
+            input_ids=text_ids,
+            attention_mask=text_mask if apply_mask else None
+        )
+        if type(text_output) is not tuple:
+            text_output.to_tuple()
+        text_output = text_output[0]
+        if self.config.projection:
+            text_output = self.proj(text_output)
+            text_output = self.norm(text_output)
+
+        if extract_cls:
+            text_output = text_output[:, 0]
+        else:
+            if apply_mask:
+                text_output = text_output.masked_fill(~text_mask[:, :, None], 0.)
+                text_output = torch.sum(text_output, dim=1) / torch.sum(text_mask, dim=1)[:, None]
+            else:
+                text_output = torch.mean(text_output, dim=1)
+        return text_output
+
+    def kldivloss(self, score, gold_score):
+        gold_score = torch.softmax(gold_score, dim=-1)
+        score = torch.nn.functional.log_softmax(score, dim=-1)
+        return self.loss_fct(score, gold_score)