# coding=utf-8 # Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch T5 model.""" import copy import math import warnings from typing import Optional, Tuple, Union from loguru import logger import torch import torch.distributed from torch import nn from torch.nn import CrossEntropyLoss from transformers.activations import ACT2FN from transformers.modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, ) from transformers.modeling_utils import PreTrainedModel from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS from transformers.utils import ( is_torch_fx_proxy, ) from transformers import T5Config from text_generation_server.utils.layers import ( TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, SpeculativeHead, ) class PartialTPEmbedding(nn.Module): def __init__(self, prefix: str, weights): super().__init__() weight = weights.get_sharded(f"{prefix}.weight", dim=1) self.weight = nn.Parameter(weight) def forward(self, input: torch.Tensor) -> torch.Tensor: return torch.nn.functional.embedding(input, self.weight) @torch.jit.script def layer_norm(hidden_states, weight, epsilon): # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for # half-precision inputs is done in fp32 variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + epsilon) # convert into half-precision if necessary if weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(weight.dtype) return weight * hidden_states class T5LayerNorm(nn.Module): def __init__(self, prefix, weights, eps=1e-6): """ Construct a layernorm module in the T5 style. No bias and no subtraction of mean. """ super().__init__() weight = weights.get_tensor(f"{prefix}.weight") self.weight = nn.Parameter(weight) self.variance_epsilon = torch.tensor(eps) def forward(self, hidden_states): return layer_norm(hidden_states, self.weight, self.variance_epsilon) try: from apex.normalization import FusedRMSNorm T5LayerNorm = FusedRMSNorm # noqa logger.info( "Discovered apex.normalization.FusedRMSNorm - will use it instead of T5LayerNorm" ) except ImportError: # using the normal T5LayerNorm pass except Exception: logger.warning("discovered apex but it failed to load, falling back to T5LayerNorm") pass ALL_LAYERNORM_LAYERS.append(T5LayerNorm) class T5DenseActDense(nn.Module): def __init__(self, config: T5Config, prefix, weights): super().__init__() self.wi = TensorParallelColumnLinear.load( config, prefix=f"{prefix}.wi", weights=weights, bias=False ) ### XXX: T5 models do not handle well both f16 and quantization. ### Overidding specifically this layer for that reason. ### https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py#L316 ### https://github.com/huggingface/transformers/issues/20287 _q = config.quantize _dtype = weights.dtype weights.dtype = torch.float32 config.quantize = None self.wo_cast = (torch.float32, _dtype) self.wo = TensorParallelRowLinear.load( config, prefix=f"{prefix}.wo", weights=weights, bias=False ) weights.dtype = _dtype config.quantize = _q self.dropout = nn.Dropout(config.dropout_rate) self.act = ( ACT2FN[config.dense_act_fn] if "gelu" not in config.dense_act_fn else lambda x: torch.nn.functional.gelu(x, approximate="tanh") ) def forward(self, hidden_states): hidden_states = self.wi(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states.to(dtype=self.wo_cast[0]) hidden_states = self.wo(hidden_states) # XXX: Recasting is already done within the layer norm. # Casting back to float16 here modifies results # hidden_states = hidden_states.to(dtype=self.wo_cast[1]) return hidden_states class T5DenseGatedActDense(nn.Module): def __init__(self, config: T5Config, prefix, weights): super().__init__() self.wi_0 = TensorParallelColumnLinear.load( config, prefix=f"{prefix}.wi_0", weights=weights, bias=False ) self.wi_1 = TensorParallelColumnLinear.load( config, prefix=f"{prefix}.wi_1", weights=weights, bias=False ) ### XXX: T5 models do not handle well both f16 and quantization. ### Overidding specifically this layer for that reason. ### https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py#L316 ### https://github.com/huggingface/transformers/issues/20287 _q = config.quantize _dtype = weights.dtype weights.dtype = torch.float32 config.quantize = None self.wo_cast = (torch.float32, _dtype) self.wo = TensorParallelRowLinear.load( config, prefix=f"{prefix}.wo", weights=weights, bias=False ) weights.dtype = _dtype config.quantize = _q self.dropout = nn.Dropout(config.dropout_rate) self.act = ( ACT2FN[config.dense_act_fn] if "gelu" not in config.dense_act_fn else lambda x: torch.nn.functional.gelu(x, approximate="tanh") ) def forward(self, hidden_states): hidden_gelu = self.act(self.wi_0(hidden_states)) hidden_linear = self.wi_1(hidden_states) hidden_states = hidden_gelu * hidden_linear hidden_states = self.dropout(hidden_states) hidden_states = hidden_states.to(dtype=self.wo_cast[0]) hidden_states = self.wo(hidden_states) # XXX: Recasting is already done within the layer norm. # Casting back to float16 here modifies results # hidden_states = hidden_states.to(dtype=self.wo_cast[1]) return hidden_states class T5LayerFF(nn.Module): def __init__(self, config: T5Config, prefix, weights): super().__init__() if config.is_gated_act: self.DenseReluDense = T5DenseGatedActDense( config, prefix=f"{prefix}.DenseReluDense", weights=weights ) else: self.DenseReluDense = T5DenseActDense( config, prefix=f"{prefix}.DenseReluDense", weights=weights ) self.layer_norm = T5LayerNorm( prefix=f"{prefix}.layer_norm", weights=weights, eps=config.layer_norm_epsilon, ) self.dropout = nn.Dropout(config.dropout_rate) def forward(self, hidden_states): forwarded_states = self.layer_norm(hidden_states) forwarded_states = self.DenseReluDense(forwarded_states) hidden_states = hidden_states + self.dropout(forwarded_states) return hidden_states class T5Attention(nn.Module): def __init__( self, config: T5Config, prefix, weights, has_relative_attention_bias=False ): super().__init__() self.is_decoder = config.is_decoder self.has_relative_attention_bias = has_relative_attention_bias self.relative_attention_num_buckets = config.relative_attention_num_buckets self.relative_attention_max_distance = config.relative_attention_max_distance self.d_model = config.d_model self.key_value_proj_dim = config.d_kv self.n_heads = config.num_heads self.dropout = config.dropout_rate self.inner_dim = self.n_heads * self.key_value_proj_dim process_group = weights.process_group # Mesh TensorFlow initialization to avoid scaling before softmax assert self.n_heads % process_group.size() == 0 self.q = TensorParallelColumnLinear.load( config, prefix=f"{prefix}.q", weights=weights, bias=False ) self.k = TensorParallelColumnLinear.load( config, prefix=f"{prefix}.k", weights=weights, bias=False ) self.v = TensorParallelColumnLinear.load( config, prefix=f"{prefix}.v", weights=weights, bias=False ) self.o = TensorParallelRowLinear.load( config, prefix=f"{prefix}.o", weights=weights, bias=False ) if self.n_heads % weights.process_group.size() != 0: raise ValueError( f"`n_heads` must be divisible by `num_shards` (got `n_heads`: {self.n_heads} " f"and `num_shards`: {weights.process_group.size()}" ) self.n_heads = self.n_heads // process_group.size() self.inner_dim = self.inner_dim // process_group.size() if self.has_relative_attention_bias: self.relative_attention_bias = PartialTPEmbedding( prefix=f"{prefix}.relative_attention_bias", weights=weights ) @staticmethod def _relative_position_bucket( relative_position, bidirectional=True, num_buckets=32, max_distance=128 ): """ Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) """ relative_buckets = 0 if bidirectional: num_buckets //= 2 relative_buckets += (relative_position > 0).to(torch.long) * num_buckets relative_position = torch.abs(relative_position) else: relative_position = -torch.min( relative_position, torch.zeros_like(relative_position) ) # now relative_position is in the range [0, inf) # half of the buckets are for exact increments in positions max_exact = num_buckets // 2 is_small = relative_position < max_exact # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance relative_position_if_large = max_exact + ( torch.log(relative_position.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).to(torch.long) relative_position_if_large = torch.min( relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1), ) relative_buckets += torch.where( is_small, relative_position, relative_position_if_large ) return relative_buckets def compute_bias(self, query_length, key_length, device=None): """Compute binned relative position bias""" if device is None: device = self.relative_attention_bias.weight.device context_position = torch.arange(query_length, dtype=torch.long, device=device)[ :, None ] memory_position = torch.arange(key_length, dtype=torch.long, device=device)[ None, : ] relative_position = ( memory_position - context_position ) # shape (query_length, key_length) relative_position_bucket = self._relative_position_bucket( relative_position, # shape (query_length, key_length) bidirectional=(not self.is_decoder), num_buckets=self.relative_attention_num_buckets, max_distance=self.relative_attention_max_distance, ) values = self.relative_attention_bias( relative_position_bucket ) # shape (query_length, key_length, num_heads) values = values.permute([2, 0, 1]).unsqueeze( 0 ) # shape (1, num_heads, query_length, key_length) return values def forward( self, hidden_states, mask=None, key_value_states=None, position_bias=None, past_key_value=None, layer_head_mask=None, query_length=None, use_cache=False, output_attentions=False, ): """ Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). """ # Input is (batch_size, seq_length, dim) # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length) # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head) batch_size, seq_length = hidden_states.shape[:2] real_seq_length = seq_length if past_key_value is not None: assert ( len(past_key_value) == 2 ), f"past_key_value should have 2 past states: keys and values. Got {len(past_key_value)} past states" real_seq_length += ( past_key_value[0].shape[2] if query_length is None else query_length ) key_length = ( real_seq_length if key_value_states is None else key_value_states.shape[1] ) def shape(states): """projection""" return states.view( batch_size, -1, self.n_heads, self.key_value_proj_dim ).transpose(1, 2) def unshape(states): """reshape""" return ( states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim) ) def project(hidden_states, proj_layer, key_value_states, past_key_value): """projects hidden states correctly to key/query states""" if key_value_states is None: # self-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(hidden_states)) elif past_key_value is None: # cross-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(key_value_states)) if past_key_value is not None: if key_value_states is None: # self-attn # (batch_size, n_heads, key_length, dim_per_head) hidden_states = torch.cat([past_key_value, hidden_states], dim=2) elif past_key_value.shape[2] != key_value_states.shape[1]: # checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning # cross-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(key_value_states)) else: # cross-attn hidden_states = past_key_value return hidden_states # get query states query_states = shape( self.q(hidden_states) ) # (batch_size, n_heads, seq_length, dim_per_head) # get key/value states key_states = project( hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None, ) value_states = project( hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None, ) # compute scores scores = torch.matmul( query_states, key_states.transpose(3, 2) ) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9 if position_bias is None: if not self.has_relative_attention_bias: position_bias = torch.zeros( (1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype, ) else: position_bias = self.compute_bias( real_seq_length, key_length, device=scores.device ) # if key and values are already calculated # we want only the last query position bias if past_key_value is not None: position_bias = position_bias[:, :, -hidden_states.size(1) :, :] if mask is not None: position_bias = ( position_bias + mask ) # (batch_size, n_heads, seq_length, key_length) position_bias_masked = position_bias scores += position_bias_masked attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as( scores ) # (batch_size, n_heads, seq_length, key_length) attn_weights = nn.functional.dropout( attn_weights, p=self.dropout, training=self.training ) # (batch_size, n_heads, seq_length, key_length) # Mask heads if we want to if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask attn_output = unshape( torch.matmul(attn_weights, value_states) ) # (batch_size, seq_length, dim) attn_output = self.o(attn_output) present_key_value_state = ( (key_states, value_states) if (self.is_decoder and use_cache) else None ) outputs = (attn_output,) + (present_key_value_state,) + (position_bias,) if output_attentions: outputs = outputs + (attn_weights,) return outputs class T5LayerSelfAttention(nn.Module): def __init__(self, config, prefix, weights, has_relative_attention_bias=False): super().__init__() self.SelfAttention = T5Attention( config, prefix=f"{prefix}.SelfAttention", weights=weights, has_relative_attention_bias=has_relative_attention_bias, ) self.layer_norm = T5LayerNorm( prefix=f"{prefix}.layer_norm", weights=weights, eps=config.layer_norm_epsilon, ) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.SelfAttention( normed_hidden_states, mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = hidden_states + self.dropout(attention_output[0]) outputs = (hidden_states,) + attention_output[ 1: ] # add attentions if we output them return outputs class T5LayerCrossAttention(nn.Module): def __init__(self, config, prefix, weights): super().__init__() self.EncDecAttention = T5Attention( config, prefix=f"{prefix}.EncDecAttention", weights=weights, has_relative_attention_bias=False, ) self.layer_norm = T5LayerNorm( prefix=f"{prefix}.layer_norm", weights=weights, eps=config.layer_norm_epsilon, ) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, key_value_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, query_length=None, output_attentions=False, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.EncDecAttention( normed_hidden_states, mask=attention_mask, key_value_states=key_value_states, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, query_length=query_length, output_attentions=output_attentions, ) layer_output = hidden_states + self.dropout(attention_output[0]) outputs = (layer_output,) + attention_output[ 1: ] # add attentions if we output them return outputs class T5Block(nn.Module): def __init__(self, config, prefix, weights, has_relative_attention_bias: bool): super().__init__() self.is_decoder = config.is_decoder self.layer = nn.ModuleList() self.layer.append( T5LayerSelfAttention( config, prefix=f"{prefix}.layer.0", weights=weights, has_relative_attention_bias=has_relative_attention_bias, ) ) if self.is_decoder: i = 2 self.layer.append( T5LayerCrossAttention( config, prefix=f"{prefix}.layer.1", weights=weights ) ) else: i = 1 self.layer.append( T5LayerFF(config, prefix=f"{prefix}.layer.{i}", weights=weights) ) def forward( self, hidden_states, attention_mask=None, position_bias=None, encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None, layer_head_mask=None, cross_attn_layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, return_dict=True, ): if past_key_value is not None: if not self.is_decoder: logger.warning( "`past_key_values` is passed to the encoder. Please make sure this is intended." ) expected_num_past_key_values = 2 if encoder_hidden_states is None else 4 if len(past_key_value) != expected_num_past_key_values: raise ValueError( f"There should be {expected_num_past_key_values} past states. " f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}" f"Got {len(past_key_value)} past key / value states" ) self_attn_past_key_value = past_key_value[:2] cross_attn_past_key_value = past_key_value[2:] else: self_attn_past_key_value, cross_attn_past_key_value = None, None self_attention_outputs = self.layer[0]( hidden_states, attention_mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=self_attn_past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states, present_key_value_state = self_attention_outputs[:2] attention_outputs = self_attention_outputs[ 2: ] # Keep self-attention outputs and relative position weights # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16: clamp_value = torch.where( torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max, ) hidden_states = torch.clamp( hidden_states, min=-clamp_value, max=clamp_value ) do_cross_attention = self.is_decoder and encoder_hidden_states is not None if do_cross_attention: # the actual query length is unknown for cross attention # if using past key value states. Need to inject it here if present_key_value_state is not None: query_length = present_key_value_state[0].shape[2] else: query_length = None cross_attention_outputs = self.layer[1]( hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, position_bias=encoder_decoder_position_bias, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, query_length=query_length, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = cross_attention_outputs[0] # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16: clamp_value = torch.where( torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max, ) hidden_states = torch.clamp( hidden_states, min=-clamp_value, max=clamp_value ) # Combine self attn and cross attn key value states if present_key_value_state is not None: present_key_value_state = ( present_key_value_state + cross_attention_outputs[1] ) # Keep cross-attention outputs and relative position weights attention_outputs = attention_outputs + cross_attention_outputs[2:] # Apply Feed Forward layer hidden_states = self.layer[-1](hidden_states) # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16: clamp_value = torch.where( torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max, ) hidden_states = torch.clamp( hidden_states, min=-clamp_value, max=clamp_value ) outputs = (hidden_states,) if use_cache: outputs = outputs + (present_key_value_state,) + attention_outputs else: outputs = outputs + attention_outputs return outputs # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) class T5PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = T5Config def _shift_right(self, input_ids): decoder_start_token_id = self.config.decoder_start_token_id pad_token_id = self.config.pad_token_id assert decoder_start_token_id is not None, ( "self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id." " See T5 docs for more information" ) # shift inputs to the right if is_torch_fx_proxy(input_ids): # Item assignment is not supported natively for proxies. shifted_input_ids = torch.full( input_ids.shape[:-1] + (1,), decoder_start_token_id ) shifted_input_ids = torch.cat( [shifted_input_ids, input_ids[..., :-1]], dim=-1 ) else: shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() shifted_input_ids[..., 0] = decoder_start_token_id assert ( pad_token_id is not None ), "self.model.config.pad_token_id has to be defined." # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids class T5Stack(T5PreTrainedModel): def __init__(self, config, prefix, weights, embed_tokens): super().__init__(config) self.is_decoder = config.is_decoder self.embed_tokens = embed_tokens self.block = nn.ModuleList( [ T5Block( config, prefix=f"{prefix}.block.{layer_id}", weights=weights, has_relative_attention_bias=(layer_id == 0), ) for layer_id in range(config.num_layers) ] ) self.final_layer_norm = T5LayerNorm( prefix=f"{prefix}.final_layer_norm", weights=weights, eps=config.layer_norm_epsilon, ) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, inputs_embeds=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): # Model parallel use_cache = use_cache if use_cache is not None else self.config.use_cache output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) if input_ids is not None and inputs_embeds is not None: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError( f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time" ) elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError( f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds" ) if inputs_embeds is None: assert ( self.embed_tokens is not None ), "You have to initialize the model with valid token embeddings" inputs_embeds = self.embed_tokens(input_ids) batch_size, seq_length = input_shape # required mask seq length can be calculated via length of past mask_seq_length = ( past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length ) if use_cache is True: assert ( self.is_decoder ), f"`use_cache` can only be set to `True` if {self} is used as a decoder" if attention_mask is None: attention_mask = torch.ones( batch_size, mask_seq_length, device=inputs_embeds.device ) if ( self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None ): encoder_seq_length = encoder_hidden_states.shape[1] encoder_attention_mask = torch.ones( batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long, ) # initialize past_key_values with `None` if past does not exist if past_key_values is None: past_key_values = [None] * len(self.block) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask = self.get_extended_attention_mask( attention_mask, input_shape ) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.is_decoder and encoder_hidden_states is not None: ( encoder_batch_size, encoder_sequence_length, _, ) = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones( encoder_hidden_shape, device=inputs_embeds.device ) encoder_extended_attention_mask = self.invert_attention_mask( encoder_attention_mask ) else: encoder_extended_attention_mask = None # Prepare head mask if needed head_mask = self.get_head_mask(head_mask, self.config.num_layers) cross_attn_head_mask = self.get_head_mask( cross_attn_head_mask, self.config.num_layers ) present_key_value_states = () if use_cache else None all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if (output_attentions and self.is_decoder) else None position_bias = None encoder_decoder_position_bias = None hidden_states = self.dropout(inputs_embeds) for i, (layer_module, past_key_value) in enumerate( zip(self.block, past_key_values) ): layer_head_mask = head_mask[i] cross_attn_layer_head_mask = cross_attn_head_mask[i] # Model parallel if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_outputs = layer_module( hidden_states, attention_mask=extended_attention_mask, position_bias=position_bias, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, encoder_decoder_position_bias=encoder_decoder_position_bias, layer_head_mask=layer_head_mask, cross_attn_layer_head_mask=cross_attn_layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) # layer_outputs is a tuple with: # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) if use_cache is False: layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:] hidden_states, present_key_value_state = layer_outputs[:2] # We share the position biases between the layers - the first layer store them # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights), # (cross-attention position bias), (cross-attention weights) position_bias = layer_outputs[2] if self.is_decoder and encoder_hidden_states is not None: encoder_decoder_position_bias = layer_outputs[ 4 if output_attentions else 3 ] # append next layer key value states if use_cache: present_key_value_states = present_key_value_states + ( present_key_value_state, ) if output_attentions: all_attentions = all_attentions + (layer_outputs[3],) if self.is_decoder: all_cross_attentions = all_cross_attentions + (layer_outputs[5],) hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.dropout(hidden_states) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, present_key_value_states, all_hidden_states, all_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=present_key_value_states, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) class T5ForConditionalGeneration(T5PreTrainedModel): def __init__(self, config: T5Config, weights): super().__init__(config) self.model_dim = config.d_model self.shared = TensorParallelEmbedding(prefix="shared", weights=weights) encoder_config = copy.deepcopy(config) encoder_config.is_decoder = False encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = T5Stack( config=encoder_config, prefix="encoder", weights=weights, embed_tokens=self.shared, ) decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True decoder_config.is_encoder_decoder = False decoder_config.num_layers = config.num_decoder_layers self.decoder = T5Stack( config=decoder_config, prefix="decoder", weights=weights, embed_tokens=self.shared, ) try: self.lm_head = SpeculativeHead.load( config, prefix="lm_head", weights=weights ) except RuntimeError: # Some models like t5-small were saved with shared weights unlike flan # Since they are declared as the same arch we have no choice but hope # that this is OK instead of using a proper flag. self.lm_head = SpeculativeHead.load( config, prefix="shared", weights=weights ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]: 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 ) # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask if head_mask is not None and decoder_head_mask is None: if self.config.num_layers == self.config.num_decoder_layers: warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning) decoder_head_mask = head_mask # Encode if needed (training, first prediction pass) if encoder_outputs is None: # Convert encoder inputs in embeddings if needed encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) hidden_states = encoder_outputs[0] if ( labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None ): # get decoder inputs from shifting lm labels to the right decoder_input_ids = self._shift_right(labels) # Decode decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values, encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = decoder_outputs[0] if self.config.tie_word_embeddings: # Rescale output before projecting on vocab # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586 sequence_output = sequence_output * (self.model_dim**-0.5) logits, speculative_logits = self.lm_head(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss(ignore_index=-100) # move labels to correct device to enable PP labels = labels.to(lm_logits.device) loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666 if not return_dict: output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs return ((loss,) + output) if loss is not None else output return ( Seq2SeqLMOutput( loss=loss, logits=logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ), speculative_logits, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, head_mask=None, decoder_head_mask=None, decoder_attention_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs, ): # cut decoder_input_ids if past is used if past_key_values is not None: input_ids = input_ids[:, -1:] return { "decoder_input_ids": input_ids, "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "decoder_attention_mask": decoder_attention_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, } def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return self._shift_right(labels) def _reorder_cache(self, past_key_values, beam_idx): # if decoder past is not included in output # speedy decoding is disabled and no need to reorder if past_key_values is None: logger.warning( "You might want to consider setting `use_cache=True` to speed up decoding" ) return past_key_values reordered_decoder_past = () for layer_past_states in past_key_values: # get the correct batch idx from layer past batch dim # batch dim of `past` is at 2nd position reordered_layer_past_states = () for layer_past_state in layer_past_states: # need to set correct `past` for each of the four key / value states reordered_layer_past_states = reordered_layer_past_states + ( layer_past_state.index_select( 0, beam_idx.to(layer_past_state.device) ), ) assert reordered_layer_past_states[0].shape == layer_past_states[0].shape assert len(reordered_layer_past_states) == len(layer_past_states) reordered_decoder_past = reordered_decoder_past + ( reordered_layer_past_states, ) return reordered_decoder_past