phi-epmc / modeling_mixformer_sequential.py
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# Copyright (c) Microsoft Corporation.
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# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
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from __future__ import annotations
import math
import copy
from typing import Any, Dict, Optional, Tuple, Union
from dataclasses import dataclass, field
import torch
import torch.nn as nn
from einops import rearrange
from transformers.activations import ACT2FN
from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_mixformer_sequential import MixFormerSequentialConfig
@dataclass
class InferenceParams:
"""Inference parameters passed to model to efficiently calculate
and store context during inference.
Reference:
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/utils/generation.py.
Args:
max_sequence_len: Maximum sequence length.
max_batch_size: Maximum batch size.
sequence_len_offset: Sequence length offset.
batch_size_offset: Batch size offset.
key_value_memory_dict: Key value memory dictionary.
fused_ft_kernel: Whether to use fused kernel for fast inference.
lengths_per_sample: Lengths per sample.
"""
max_sequence_len: int = field(metadata={"help": "Maximum sequence length."})
max_batch_size: int = field(metadata={"help": "Maximum batch size."})
sequence_len_offset: int = field(default=0, metadata={"help": "Sequence length offset."})
batch_size_offset: int = field(default=0, metadata={"help": "Batch size offset."})
key_value_memory_dict: Dict[str, Any] = field(
default_factory=dict, metadata={"help": "Key value memory dictionary."}
)
fused_ft_kernel: bool = field(default=False, metadata={"help": "Whether to use fused kernel for fast inference."})
lengths_per_sample: torch.Tensor = field(default=None, metadata={"help": "Lengths per sample."})
class Embedding(nn.Module):
"""Token embedding with dropout."""
def __init__(self, config: PretrainedConfig) -> None:
super().__init__()
self.wte = nn.Embedding(config.vocab_size, config.n_embd)
self.drop = nn.Dropout(config.embd_pdrop)
def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
hidden_states = self.wte(input_ids)
hidden_states = self.drop(hidden_states)
return hidden_states
class RotaryEmbedding(nn.Module):
"""Rotary embeddings.
Reference:
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py.
"""
def __init__(
self,
dim: int,
base: int = 10000,
scale_base: Optional[float] = None,
device: Optional[str] = None,
**kwargs,
) -> None:
super().__init__()
if scale_base is not None:
raise NotImplementedError
# Generate and save the inverse frequency buffer (non-trainable)
self.dim = dim
self.base = base
self.scale_base = scale_base
self.device = device
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))
self.register_buffer("inv_freq", inv_freq)
scale = (
(torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
if scale_base is not None
else None
)
self.register_buffer("scale", scale)
self._seq_len_cached = 0
self._cos_cached = None
self._sin_cached = None
self._cos_k_cached = None
self._sin_k_cached = None
def _update_cos_sin_cache(self, x: torch.FloatTensor, seqlen_offset: int = 0) -> None:
# Reset the tables if the sequence length has changed,
# or if we're on a new device (possibly due to tracing for instance)
seqlen = x.shape[1] + seqlen_offset
# Re-generate the inverse frequency buffer if it's not fp32
# (for instance if model.half() was called)
if self.inv_freq.dtype != "torch.float32":
self.inv_freq = 1.0 / (
self.base ** (torch.arange(0, self.dim, 2, device=self.device, dtype=torch.float32) / self.dim)
)
if seqlen > self._seq_len_cached or self._cos_cached.device != x.device or self._cos_cached.dtype != x.dtype:
self._seq_len_cached = seqlen
t = torch.arange(seqlen, device=x.device, dtype=torch.float32)
# Don't do einsum, it converts fp32 to fp16
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
freqs = torch.outer(t, self.inv_freq.to(device=t.device, dtype=torch.float32))
if self.scale is None:
self._cos_cached = torch.cos(freqs).to(x.dtype)
self._sin_cached = torch.sin(freqs).to(x.dtype)
else:
power = (
torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2
) / self.scale_base
scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1")
# We want the multiplication by scale to happen in fp32
self._cos_cached = (torch.cos(freqs) * scale).to(x.dtype)
self._sin_cached = (torch.sin(freqs) * scale).to(x.dtype)
self._cos_k_cached = (torch.cos(freqs) / scale).to(x.dtype)
self._sin_k_cached = (torch.sin(freqs) / scale).to(x.dtype)
def _apply_rotary_emb_qkv(
self,
qkv: torch.FloatTensor,
sin: torch.FloatTensor,
cos: torch.FloatTensor,
sin_k: Optional[torch.FloatTensor] = None,
cos_k: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
_, seqlen, three, _, headdim = qkv.shape
assert three == 3
rotary_seqlen, rotary_dim = cos.shape
rotary_dim *= 2
assert rotary_dim <= headdim
assert seqlen <= rotary_seqlen
cos_k = cos if cos_k is None else cos_k
sin_k = sin if sin_k is None else sin_k
assert sin.shape == cos_k.shape == sin_k.shape == (rotary_seqlen, rotary_dim // 2)
q_rot = qkv[:, :, 0, :, :rotary_dim]
q_pass = qkv[:, :, 0, :, rotary_dim:]
k_rot = qkv[:, :, 1, :, :rotary_dim]
k_pass = qkv[:, :, 1, :, rotary_dim:]
# Splits the queries and keys in half
q1, q2 = q_rot.chunk(2, dim=-1)
k1, k2 = k_rot.chunk(2, dim=-1)
c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
# Casts to fp32 are necessary to prevent fp16 overflow issues
q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]]
# Computes the new keys and queries, recasting to original dtype
q_rot = torch.cat([q1 * c - q2 * s, q1 * s + q2 * c], axis=-1).to(qkv.dtype)
k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(qkv.dtype)
return torch.cat(
[
torch.cat([q_rot, q_pass], axis=-1).unsqueeze(2),
torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
qkv[:, :, 2:3, :, :],
],
axis=2,
)
def forward(self, qkv: torch.Tensor, seqlen_offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
# `qkv` is of shape (batch, seqlen, 3, nheads, headdim)
self._update_cos_sin_cache(qkv, seqlen_offset)
return self._apply_rotary_emb_qkv(qkv, self._sin_cached[seqlen_offset:], self._cos_cached[seqlen_offset:])
class MLP(nn.Module):
"""Multi-Layer Perceptron.
Reference:
Attention Is All You Need.
https://arxiv.org/pdf/1706.03762.pdf.
"""
def __init__(self, config: PretrainedConfig, n_inner: Optional[int] = None, act_fn: Optional[str] = None) -> None:
super().__init__()
act_fn = config.activation_function if act_fn is None else act_fn
assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
n_inner = n_inner if n_inner is not None else 4 * config.n_embd
self.fc1 = nn.Linear(config.n_embd, n_inner)
self.fc2 = nn.Linear(n_inner, config.n_embd)
self.act = ACT2FN[act_fn]
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class SelfAttention(nn.Module):
"""Self-attention layer (compatible with PyTorch).
Reference:
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
"""
def __init__(
self,
causal: bool = True,
softmax_scale: Optional[float] = None,
attention_dropout: float = 0.0,
) -> None:
super().__init__()
self.causal = causal
self.softmax_scale = softmax_scale
self.drop = nn.Dropout(attention_dropout)
def forward(
self,
qkv: torch.FloatTensor,
causal: bool = None,
attention_mask: Optional[torch.BoolTensor] = None,
**kwargs,
) -> torch.FloatTensor:
causal = self.causal if causal is None else causal
batch_size, seq_len = qkv.shape[0], qkv.shape[1]
q, k, v = qkv.unbind(dim=2)
softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
if attention_mask is not None:
padding_mask = torch.full((batch_size, seq_len), -10000.0, dtype=scores.dtype, device=scores.device)
padding_mask.masked_fill_(attention_mask, 0.0)
scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
if causal:
causal_mask = torch.triu(torch.full((seq_len, seq_len), -10000.0, device=scores.device), 1)
scores = scores + causal_mask.to(dtype=scores.dtype)
attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
attention = self.drop(attention)
output = torch.einsum("bhts,bshd->bthd", attention, v)
return output
class CrossAttention(nn.Module):
"""Cross-attention layer (compatible with PyTorch).
Reference:
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
"""
def __init__(
self,
causal: bool = True,
softmax_scale: Optional[float] = None,
attention_dropout: float = 0.0,
) -> None:
super().__init__()
self.causal = causal
self.softmax_scale = softmax_scale
self.drop = nn.Dropout(attention_dropout)
def forward(
self,
q: torch.FloatTensor,
kv: torch.FloatTensor,
causal: bool = None,
attention_mask: Optional[torch.BoolTensor] = None,
**kwargs,
) -> torch.FloatTensor:
causal = self.causal if causal is None else causal
batch_size, seq_len_q = q.shape[0], q.shape[1]
assert kv.shape[0] == batch_size and kv.shape[3] == q.shape[2] and kv.shape[4] == q.shape[3]
seq_len_k = kv.shape[1]
k, v = kv.unbind(dim=2)
softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
if attention_mask is not None:
padding_mask = torch.full((batch_size, seq_len_k), -10000.0, dtype=scores.dtype, device=scores.device)
padding_mask.masked_fill_(attention_mask, 0.0)
scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
if causal:
causal_mask = torch.triu(torch.full((seq_len_q, seq_len_k), -10000.0, device=scores.device), 1)
scores = scores + causal_mask.to(dtype=scores.dtype)
attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
attention = self.drop(attention)
output = torch.einsum("bhts,bshd->bthd", attention, v)
return output
def find_mha_dims(
config: PretrainedConfig, n_head: Optional[int] = None, head_dim: Optional[int] = None
) -> Tuple[int, int]:
"""Validate and return the number of heads and head dimension for multi-head attention.
Args:
config: Model configuration.
n_head: Number of heads.
head_dim: Head dimension.
Returns:
Number of heads and head dimension.
"""
assert all(
hasattr(config, attr) for attr in ["n_embd", "n_head"]
), "`config` must have `n_embd` and `n_head` attributes."
if head_dim is None:
assert (
config.n_embd % config.n_head == 0
), f"Hidden size ({config.n_embd}) must be divisible by the number of heads ({config.n_head})."
if n_head is None and head_dim is None:
head_dim = config.n_embd // config.n_head
n_head = config.n_head
elif n_head is None or head_dim is None:
raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")
return n_head, head_dim
def update_kv_cache(kv: torch.FloatTensor, inference_params: InferenceParams, layer_idx: int) -> torch.FloatTensor:
"""Update the key-value cache for inference.
Reference:
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
Args:
kv: Key-value tensor.
inference_params: Inference parameters.
layer_idx: Layer index.
Returns:
Updated key-value tensor.
"""
num_heads, head_dim = kv.shape[-2:]
if layer_idx not in inference_params.key_value_memory_dict:
kv_cache = torch.empty(
inference_params.max_batch_size,
inference_params.max_sequence_len,
2,
num_heads,
head_dim,
dtype=kv.dtype,
device=kv.device,
)
inference_params.key_value_memory_dict[layer_idx] = kv_cache
else:
if not inference_params.fused_ft_kernel:
kv_cache = inference_params.key_value_memory_dict[layer_idx]
else:
k_cache, v_cache = inference_params.key_value_memory_dict[layer_idx]
kv_cache = None
batch_start = inference_params.batch_size_offset
batch_end = batch_start + kv.shape[0]
assert batch_end <= (kv_cache.shape[0] if kv_cache is not None else v_cache.shape[0])
sequence_start = inference_params.sequence_len_offset
sequence_end = sequence_start + kv.shape[1]
assert sequence_end <= (kv_cache.shape[1] if kv_cache is not None else v_cache.shape[2])
if not inference_params.fused_ft_kernel:
assert kv_cache is not None
kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
kv = kv_cache[batch_start:batch_end, :sequence_end, ...]
return kv
assert inference_params.sequence_len_offset == 0
assert kv.dtype in [torch.float16, torch.bfloat16, torch.float32]
packsize = 4 if kv.dtype == torch.float32 else 8
if kv_cache is not None:
kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
k_cache = rearrange(kv_cache[:, :, 0], "b s h (d packsize) -> b h d s packsize", packsize=packsize).contiguous()
v_cache = rearrange(kv_cache[:, :, 1], "b s h d -> b h s d").contiguous()
inference_params.key_value_memory_dict[layer_idx] = (k_cache, v_cache)
else:
k_cache[batch_start:batch_end, :, :, :sequence_end, :] = rearrange(
kv[:, :, 0], "b s h (d packsize) -> b h d s packsize", packsize=packsize
)
v_cache[batch_start:batch_end, :, :sequence_end, :] = rearrange(kv[:, :, 1], "b s h d -> b h s d")
return kv
class MHA(nn.Module):
"""Multi-head attention layer."""
def __init__(
self,
config: PretrainedConfig,
dtype: Optional[torch.dtype] = None,
device: Optional[str] = None,
rotary_dim: Optional[int] = None,
rotary_emb_scale_base: Optional[float] = None,
n_head: Optional[int] = None,
head_dim: Optional[int] = None,
bias: bool = True,
causal: bool = True,
softmax_scale: Optional[float] = None,
dropout: float = 0.0,
layer_idx: Optional[int] = None,
return_residual: bool = False,
checkpointing: bool = False,
) -> None:
super().__init__()
# Rotary embedding
self.rotary_emb_dim = rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
if self.rotary_emb_dim > 0:
rotary_kwargs = {"device": device}
if rotary_emb_scale_base is not None and rotary_emb_scale_base > 0.0:
rotary_kwargs["scale_base"] = rotary_emb_scale_base
self.rotary_emb = RotaryEmbedding(self.rotary_emb_dim, **rotary_kwargs)
# MLP
self.n_head, self.head_dim = find_mha_dims(config, n_head, head_dim)
op_size = self.n_head * self.head_dim
hidden_size = config.n_embd
self.Wqkv = nn.Linear(hidden_size, 3 * op_size, bias=bias, device=device, dtype=dtype)
self.out_proj = nn.Linear(op_size, hidden_size, bias=bias, device=device, dtype=dtype)
# Attention
self.inner_attn = SelfAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
self.inner_cross_attn = CrossAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
self.layer_idx = layer_idx
self.return_residual = return_residual
self.checkpointing = checkpointing
def forward(
self,
x: torch.FloatTensor,
past_key_values: Optional[InferenceParams] = None,
attention_mask: Optional[torch.BoolTensor] = None,
cu_seqlens: Optional[torch.LongTensor] = None,
max_seqlen: Optional[int] = None,
**kwargs,
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
qkv = self.Wqkv(x)
qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)
seqlen_offset = past_key_values.sequence_len_offset if past_key_values is not None else 0
if self.rotary_emb_dim > 0:
qkv = self.rotary_emb(qkv, seqlen_offset=seqlen_offset)
if past_key_values is not None:
kv = update_kv_cache(qkv[:, :, 1:], past_key_values, self.layer_idx)
if attention_mask is not None:
attention_mask = attention_mask[0] if isinstance(attention_mask, tuple) else attention_mask
attention_mask = attention_mask.bool().to(qkv.device)
attention_kwargs = {"attention_mask": attention_mask}
if past_key_values is None or seqlen_offset == 0:
if self.checkpointing:
attn_output = torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, **attention_kwargs)
else:
attn_output = self.inner_attn(qkv, **attention_kwargs)
else:
q = qkv[:, :, 0]
causal = None if past_key_values.sequence_len_offset == 0 else False
attn_output = self.inner_cross_attn(q, kv, causal=causal, **attention_kwargs)
output = rearrange(attn_output, "... h d -> ... (h d)")
output = self.out_proj(output)
return output if not self.return_residual else (output, x)
class ParallelBlock(nn.Module):
"""Parallel block.
This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).
"""
def __init__(
self,
config: PretrainedConfig,
block_idx: Optional[int] = None,
) -> None:
super().__init__()
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.block_idx = block_idx
self.mixer = MHA(config, layer_idx=block_idx)
self.mlp = MLP(config)
def forward(
self,
hidden_states: torch.FloatTensor,
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
attention_mask: Optional[torch.BoolTensor] = None,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states
hidden_states = self.ln(hidden_states)
attn_outputs = self.mixer(hidden_states, past_key_values=past_key_values, attention_mask=attention_mask)
if isinstance(attn_outputs, tuple):
attn_outputs = attn_outputs[0]
attn_outputs = self.resid_dropout(attn_outputs)
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
hidden_states = attn_outputs + feed_forward_hidden_states + residual
return hidden_states
class CausalLMHead(nn.Module):
"""Causal Language Modeling head.
Reference:
Improving Language Understanding by Generative Pre-Training.
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
"""
def __init__(self, config: PretrainedConfig) -> None:
super().__init__()
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.linear = nn.Linear(config.n_embd, config.vocab_size)
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
hidden_states = self.ln(hidden_states)
logits = self.linear(hidden_states).to(torch.float32)
return logits
class CausalLMLoss(nn.Module):
"""Causal Language Modeling loss.
Reference:
Improving Language Understanding by Generative Pre-Training.
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
"""
def __init__(self, shift_labels: bool = True) -> None:
super().__init__()
self.shift_labels = shift_labels
self.loss_fct = nn.CrossEntropyLoss()
def forward(self, logits: torch.FloatTensor, labels: torch.LongTensor) -> torch.FloatTensor:
if self.shift_labels:
logits = logits[..., :-1, :].contiguous()
labels = labels[..., 1:].contiguous()
loss = self.loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))
return loss
class MixFormerSequentialPreTrainedModel(PreTrainedModel):
"""MixFormer (sequential for DeepSpeed) pre-trained model."""
config_class = MixFormerSequentialConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
def __init__(self, *inputs, **kwargs) -> None:
super().__init__(*inputs, **kwargs)
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, (nn.Linear,)):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def prepare_inputs_for_generation(
self,
input_ids: torch.LongTensor,
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
attention_mask: Optional[torch.BoolTensor] = None,
**kwargs,
) -> Dict[str, Any]:
if attention_mask is not None and torch.any(~attention_mask.bool()):
total_seq_len = torch.sum(attention_mask, dim=1)
max_seq_len = torch.max(total_seq_len)
total_seq_len = torch.cat((torch.tensor([0], device=attention_mask.device), total_seq_len)).unsqueeze(1)
cumulative_seq_len = torch.cumsum(total_seq_len, dim=0).squeeze(1).to(torch.int32)
attention_mask = (attention_mask.bool(), cumulative_seq_len, max_seq_len.item())
else:
attention_mask = None
if past_key_values is None or not (isinstance(past_key_values, InferenceParams)):
past_key_values = InferenceParams(
max_batch_size=input_ids.shape[0],
max_sequence_len=self.config.n_positions,
sequence_len_offset=0,
batch_size_offset=0,
fused_ft_kernel=False,
key_value_memory_dict={},
)
else:
# Assume that `past_key_values` has cached all tokens up to the last token in `input_ids`
past_key_values.sequence_len_offset = len(input_ids[0]) - 1
input_ids = input_ids[:, -1].unsqueeze(-1)
return {
"input_ids": input_ids,
"past_key_values": past_key_values,
"attention_mask": attention_mask,
}
class MixFormerSequentialForCausalLM(MixFormerSequentialPreTrainedModel):
"""MixFormer (sequential for DeepSpeed) for Causal Language Modeling."""
_keys_to_ignore_on_load_missing = [""]
_keys_to_ignore_on_load_unexpected = [r"layers\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
_no_split_modules = ["ParallelBlock"]
def __init__(self, config: MixFormerSequentialConfig) -> None:
super().__init__(config)
modules = [Embedding(config)]
modules += [ParallelBlock(config, block_idx=i) for i in range(config.n_layer)]
modules.append(CausalLMHead(config))
self.layers = nn.Sequential(*modules)
self.loss = CausalLMLoss()
self.post_init()
def get_input_embeddings(self) -> nn.Embedding:
return self.layers[0].wte
def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
self.layers[0].wte = new_embeddings
def get_output_embeddings(self) -> nn.Linear:
return self.layers[-1].linear
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
self.layers[-1].linear = new_embeddings
def forward(
self,
input_ids: torch.LongTensor,
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
attention_mask: Optional[torch.BoolTensor] = None,
labels: Optional[torch.LongTensor] = None,
**kwargs,
) -> CausalLMOutputWithPast:
if attention_mask is not None and self.training:
print("`attention_mask` is not supported during training. Using it might lead to unexpected results.")
if past_key_values is None and attention_mask is None:
lm_logits = self.layers(input_ids)
else:
hidden_layer = self.layers[0](input_ids)
for module in self.layers[1:-1]:
hidden_layer = module(hidden_layer, past_key_values=past_key_values, attention_mask=attention_mask)
lm_logits = self.layers[-1](hidden_layer)
loss = None
if labels is not None:
loss = self.loss(lm_logits, labels)
return CausalLMOutputWithPast(loss=loss, logits=lm_logits, past_key_values=past_key_values)