1201 lines
46 KiB
Python
1201 lines
46 KiB
Python
# coding=utf-8
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# Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
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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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""" PyTorch T5 model."""
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import copy
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import math
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import warnings
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from typing import Optional, Tuple, Union
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import torch
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import torch.distributed
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from transformers.activations import ACT2FN
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from transformers.modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPastAndCrossAttentions,
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Seq2SeqLMOutput,
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)
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from transformers.modeling_utils import PreTrainedModel
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from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
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from transformers.utils import (
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is_torch_fx_proxy,
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)
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from transformers import T5Config
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from text_generation_server.utils.layers import (
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TensorParallelColumnLinear,
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TensorParallelEmbedding,
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TensorParallelRowLinear,
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TensorParallelHead,
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)
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class PartialTPEmbedding(nn.Module):
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def __init__(self, prefix: str, weights):
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super().__init__()
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weight = weights.get_sharded(f"{prefix}.weight", dim=1)
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self.weight = nn.Parameter(weight)
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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return torch.nn.functional.embedding(input, self.weight)
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@torch.jit.script
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def layer_norm(hidden_states, weight, epsilon):
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# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
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# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
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# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
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# half-precision inputs is done in fp32
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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 + epsilon)
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# convert into half-precision if necessary
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if weight.dtype in [torch.float16, torch.bfloat16]:
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hidden_states = hidden_states.to(weight.dtype)
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return weight * hidden_states
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class T5LayerNorm(nn.Module):
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def __init__(self, prefix, weights, eps=1e-6):
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"""
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Construct a layernorm module in the T5 style. No bias and no subtraction of mean.
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"""
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super().__init__()
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weight = weights.get_tensor(f"{prefix}.weight")
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self.weight = nn.Parameter(weight)
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self.variance_epsilon = torch.tensor(eps)
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def forward(self, hidden_states):
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return layer_norm(hidden_states, self.weight, self.variance_epsilon)
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try:
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from apex.normalization import FusedRMSNorm
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T5LayerNorm = FusedRMSNorm # noqa
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logger.info(
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"Discovered apex.normalization.FusedRMSNorm - will use it instead of T5LayerNorm"
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)
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except ImportError:
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# using the normal T5LayerNorm
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pass
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except Exception:
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logger.warning("discovered apex but it failed to load, falling back to T5LayerNorm")
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pass
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ALL_LAYERNORM_LAYERS.append(T5LayerNorm)
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class T5DenseActDense(nn.Module):
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def __init__(self, config: T5Config, prefix, weights):
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super().__init__()
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self.wi = TensorParallelColumnLinear.load(
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config, prefix=f"{prefix}.wi", weights=weights, bias=False
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)
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### XXX: T5 models do not handle well both f16 and quantization.
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### Overidding specifically this layer for that reason.
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### https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py#L316
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### https://github.com/huggingface/transformers/issues/20287
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_q = config.quantize
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_dtype = weights.dtype
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weights.dtype = torch.float32
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config.quantize = None
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self.wo_cast = (torch.float32, _dtype)
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self.wo = TensorParallelRowLinear.load(
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config, prefix=f"{prefix}.wo", weights=weights, bias=False
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)
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weights.dtype = _dtype
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config.quantize = _q
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self.dropout = nn.Dropout(config.dropout_rate)
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self.act = (
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ACT2FN[config.dense_act_fn]
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if "gelu" not in config.dense_act_fn
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else lambda x: torch.nn.functional.gelu(x, approximate="tanh")
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)
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def forward(self, hidden_states):
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hidden_states = self.wi(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = hidden_states.to(dtype=self.wo_cast[0])
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hidden_states = self.wo(hidden_states)
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# XXX: Recasting is already done within the layer norm.
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# Casting back to float16 here modifies results
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# hidden_states = hidden_states.to(dtype=self.wo_cast[1])
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return hidden_states
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class T5DenseGatedActDense(nn.Module):
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def __init__(self, config: T5Config, prefix, weights):
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super().__init__()
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self.wi_0 = TensorParallelColumnLinear.load(
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config, prefix=f"{prefix}.wi_0", weights=weights, bias=False
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)
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self.wi_1 = TensorParallelColumnLinear.load(
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config, prefix=f"{prefix}.wi_1", weights=weights, bias=False
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)
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### XXX: T5 models do not handle well both f16 and quantization.
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### Overidding specifically this layer for that reason.
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### https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py#L316
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### https://github.com/huggingface/transformers/issues/20287
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_q = config.quantize
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_dtype = weights.dtype
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weights.dtype = torch.float32
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config.quantize = None
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self.wo_cast = (torch.float32, _dtype)
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self.wo = TensorParallelRowLinear.load(
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config, prefix=f"{prefix}.wo", weights=weights, bias=False
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)
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weights.dtype = _dtype
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config.quantize = _q
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self.dropout = nn.Dropout(config.dropout_rate)
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self.act = (
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ACT2FN[config.dense_act_fn]
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if "gelu" not in config.dense_act_fn
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else lambda x: torch.nn.functional.gelu(x, approximate="tanh")
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)
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def forward(self, hidden_states):
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hidden_gelu = self.act(self.wi_0(hidden_states))
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hidden_linear = self.wi_1(hidden_states)
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hidden_states = hidden_gelu * hidden_linear
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hidden_states = self.dropout(hidden_states)
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hidden_states = hidden_states.to(dtype=self.wo_cast[0])
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hidden_states = self.wo(hidden_states)
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# XXX: Recasting is already done within the layer norm.
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# Casting back to float16 here modifies results
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# hidden_states = hidden_states.to(dtype=self.wo_cast[1])
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return hidden_states
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class T5LayerFF(nn.Module):
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def __init__(self, config: T5Config, prefix, weights):
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super().__init__()
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if config.is_gated_act:
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self.DenseReluDense = T5DenseGatedActDense(
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config, prefix=f"{prefix}.DenseReluDense", weights=weights
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)
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else:
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self.DenseReluDense = T5DenseActDense(
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config, prefix=f"{prefix}.DenseReluDense", weights=weights
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)
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self.layer_norm = T5LayerNorm(
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prefix=f"{prefix}.layer_norm",
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weights=weights,
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eps=config.layer_norm_epsilon,
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)
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self.dropout = nn.Dropout(config.dropout_rate)
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def forward(self, hidden_states):
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forwarded_states = self.layer_norm(hidden_states)
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forwarded_states = self.DenseReluDense(forwarded_states)
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hidden_states = hidden_states + self.dropout(forwarded_states)
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return hidden_states
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class T5Attention(nn.Module):
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def __init__(
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self, config: T5Config, prefix, weights, has_relative_attention_bias=False
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):
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super().__init__()
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self.is_decoder = config.is_decoder
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self.has_relative_attention_bias = has_relative_attention_bias
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self.relative_attention_num_buckets = config.relative_attention_num_buckets
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self.relative_attention_max_distance = config.relative_attention_max_distance
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self.d_model = config.d_model
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self.key_value_proj_dim = config.d_kv
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self.n_heads = config.num_heads
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self.dropout = config.dropout_rate
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self.inner_dim = self.n_heads * self.key_value_proj_dim
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process_group = weights.process_group
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# Mesh TensorFlow initialization to avoid scaling before softmax
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assert self.n_heads % process_group.size() == 0
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self.q = TensorParallelColumnLinear.load(
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config, prefix=f"{prefix}.q", weights=weights, bias=False
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)
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self.k = TensorParallelColumnLinear.load(
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config, prefix=f"{prefix}.k", weights=weights, bias=False
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)
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self.v = TensorParallelColumnLinear.load(
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config, prefix=f"{prefix}.v", weights=weights, bias=False
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)
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self.o = TensorParallelRowLinear.load(
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config, prefix=f"{prefix}.o", weights=weights, bias=False
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)
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self.n_heads = self.n_heads // process_group.size()
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self.inner_dim = self.inner_dim // process_group.size()
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if self.has_relative_attention_bias:
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self.relative_attention_bias = PartialTPEmbedding(
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prefix=f"{prefix}.relative_attention_bias", weights=weights
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)
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@staticmethod
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def _relative_position_bucket(
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relative_position, bidirectional=True, num_buckets=32, max_distance=128
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):
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"""
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Adapted from Mesh Tensorflow:
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https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
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Translate relative position to a bucket number for relative attention. The relative position is defined as
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memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
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position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
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small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
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positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
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This should allow for more graceful generalization to longer sequences than the model has been trained on
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Args:
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relative_position: an int32 Tensor
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bidirectional: a boolean - whether the attention is bidirectional
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num_buckets: an integer
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max_distance: an integer
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Returns:
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a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
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"""
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relative_buckets = 0
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if bidirectional:
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num_buckets //= 2
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relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
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relative_position = torch.abs(relative_position)
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else:
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relative_position = -torch.min(
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relative_position, torch.zeros_like(relative_position)
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)
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# now relative_position is in the range [0, inf)
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# half of the buckets are for exact increments in positions
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max_exact = num_buckets // 2
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is_small = relative_position < max_exact
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# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
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relative_position_if_large = max_exact + (
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torch.log(relative_position.float() / max_exact)
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/ math.log(max_distance / max_exact)
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* (num_buckets - max_exact)
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).to(torch.long)
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relative_position_if_large = torch.min(
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relative_position_if_large,
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torch.full_like(relative_position_if_large, num_buckets - 1),
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)
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relative_buckets += torch.where(
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is_small, relative_position, relative_position_if_large
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)
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return relative_buckets
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def compute_bias(self, query_length, key_length, device=None):
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"""Compute binned relative position bias"""
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if device is None:
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device = self.relative_attention_bias.weight.device
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context_position = torch.arange(query_length, dtype=torch.long, device=device)[
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:, None
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]
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memory_position = torch.arange(key_length, dtype=torch.long, device=device)[
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None, :
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]
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relative_position = (
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memory_position - context_position
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) # shape (query_length, key_length)
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relative_position_bucket = self._relative_position_bucket(
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relative_position, # shape (query_length, key_length)
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bidirectional=(not self.is_decoder),
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num_buckets=self.relative_attention_num_buckets,
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max_distance=self.relative_attention_max_distance,
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)
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values = self.relative_attention_bias(
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relative_position_bucket
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) # shape (query_length, key_length, num_heads)
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values = values.permute([2, 0, 1]).unsqueeze(
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0
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) # shape (1, num_heads, query_length, key_length)
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return values
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def forward(
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self,
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hidden_states,
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mask=None,
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key_value_states=None,
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position_bias=None,
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past_key_value=None,
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layer_head_mask=None,
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query_length=None,
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use_cache=False,
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output_attentions=False,
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):
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"""
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Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
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"""
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# Input is (batch_size, seq_length, dim)
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# Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
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# past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
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batch_size, seq_length = hidden_states.shape[:2]
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real_seq_length = seq_length
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if past_key_value is not None:
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assert (
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len(past_key_value) == 2
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), f"past_key_value should have 2 past states: keys and values. Got {len(past_key_value)} past states"
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real_seq_length += (
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past_key_value[0].shape[2] if query_length is None else query_length
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)
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key_length = (
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real_seq_length if key_value_states is None else key_value_states.shape[1]
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)
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def shape(states):
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"""projection"""
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return states.view(
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batch_size, -1, self.n_heads, self.key_value_proj_dim
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).transpose(1, 2)
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def unshape(states):
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"""reshape"""
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return (
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states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
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)
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def project(hidden_states, proj_layer, key_value_states, past_key_value):
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"""projects hidden states correctly to key/query states"""
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if key_value_states is None:
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# self-attn
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# (batch_size, n_heads, seq_length, dim_per_head)
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hidden_states = shape(proj_layer(hidden_states))
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elif past_key_value is None:
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# cross-attn
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# (batch_size, n_heads, seq_length, dim_per_head)
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hidden_states = shape(proj_layer(key_value_states))
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if past_key_value is not None:
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if key_value_states is None:
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# self-attn
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# (batch_size, n_heads, key_length, dim_per_head)
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hidden_states = torch.cat([past_key_value, hidden_states], dim=2)
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elif past_key_value.shape[2] != key_value_states.shape[1]:
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# checking that the `sequence_length` of the `past_key_value` is the same as
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# the provided `key_value_states` to support prefix tuning
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# cross-attn
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# (batch_size, n_heads, seq_length, dim_per_head)
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hidden_states = shape(proj_layer(key_value_states))
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else:
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# cross-attn
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hidden_states = past_key_value
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return hidden_states
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# get query states
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query_states = shape(
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self.q(hidden_states)
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) # (batch_size, n_heads, seq_length, dim_per_head)
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# get key/value states
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key_states = project(
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hidden_states,
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self.k,
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key_value_states,
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past_key_value[0] if past_key_value is not None else None,
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)
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value_states = project(
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hidden_states,
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self.v,
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key_value_states,
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past_key_value[1] if past_key_value is not None else None,
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)
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# compute scores
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scores = torch.matmul(
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query_states, key_states.transpose(3, 2)
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) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
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if position_bias is None:
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if not self.has_relative_attention_bias:
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position_bias = torch.zeros(
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(1, self.n_heads, real_seq_length, key_length),
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device=scores.device,
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dtype=scores.dtype,
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)
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else:
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position_bias = self.compute_bias(
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real_seq_length, key_length, device=scores.device
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)
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# if key and values are already calculated
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# we want only the last query position bias
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if past_key_value is not None:
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position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
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if mask is not None:
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position_bias = (
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position_bias + mask
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) # (batch_size, n_heads, seq_length, key_length)
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position_bias_masked = position_bias
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scores += position_bias_masked
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attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
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scores
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) # (batch_size, n_heads, seq_length, key_length)
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attn_weights = nn.functional.dropout(
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attn_weights, p=self.dropout, training=self.training
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) # (batch_size, n_heads, seq_length, key_length)
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# Mask heads if we want to
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if layer_head_mask is not None:
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attn_weights = attn_weights * layer_head_mask
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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,
|
|
)
|
|
|
|
self.lm_head = TensorParallelHead.load(
|
|
config, prefix="lm_head", 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)
|
|
|
|
lm_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=lm_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,
|
|
)
|
|
|
|
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
|