Update modeling_mpt.py

modeling_mpt.py

@@ -1,16 +1,31 @@
 """A simple, flexible implementation of a GPT model.
-
 Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
 """
+from __future__ import annotations
 import math
 import warnings
 from typing import Any, Dict, List, Mapping, MutableMapping, Optional, Tuple, Union
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from .attention import is_flash_v1_installed, is_flash_v2_installed
+if is_flash_v2_installed():
+    try:
+        from flash_attn import bert_padding
+        from flash_attn.layers.rotary import RotaryEmbedding as DAILRotaryEmbedding
+    except Exception as e:
+        raise e
+if is_flash_v1_installed():
+    try:
+        from flash_attn import bert_padding
+    except Exception as e:
+        raise e
 from transformers import PreTrainedModel, PreTrainedTokenizerBase
 from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from .attention import attn_bias_shape, build_attn_bias
+from transformers.models.llama.modeling_llama import LlamaDynamicNTKScalingRotaryEmbedding as HFDynamicNTKScalingRotaryEmbedding
+from transformers.models.llama.modeling_llama import LlamaLinearScalingRotaryEmbedding as HFLinearScalingRotaryEmbedding
+from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding as HFRotaryEmbedding
+from .attention import ATTN_CLASS_REGISTRY, attn_bias_shape, build_attn_bias, gen_slopes
 from .blocks import MPTBlock
 from .custom_embedding import SharedEmbedding
 from .fc import FC_CLASS_REGISTRY as FC_CLASS_REGISTRY
@@ -30,11 +45,127 @@ except:
 import logging
 log = logging.getLogger(__name__)
 
+def gen_rotary_embedding(rope_head_dim: int, rope_impl: str, rope_theta: int, rope_dail_config: dict, rope_hf_config: dict, max_seq_len: int):
+    if rope_impl == 'dail':
+        return DAILRotaryEmbedding(dim=rope_head_dim, base=rope_theta, interleaved=False, scale_base=rope_dail_config['xpos_scale_base'] if rope_dail_config['type'] == 'xpos' else None, pos_idx_in_fp32=rope_dail_config['pos_idx_in_fp32'], device='cpu')
+    elif rope_impl == 'hf':
+        if rope_hf_config['type'] == 'no_scaling':
+            return HFRotaryEmbedding(rope_head_dim, max_position_embeddings=max_seq_len, base=rope_theta, device='cpu')
+        elif rope_hf_config['type'] == 'linear':
+            return HFLinearScalingRotaryEmbedding(rope_head_dim, max_position_embeddings=max_seq_len, base=rope_theta, scaling_factor=rope_hf_config['factor'], device='cpu')
+        elif rope_hf_config['type'] == 'dynamic':
+            return HFDynamicNTKScalingRotaryEmbedding(rope_head_dim, max_position_embeddings=max_seq_len, base=rope_theta, scaling_factor=rope_hf_config['factor'], device='cpu')
+    raise ValueError('rope_impl needs to be either dail or hf')
+
+def gen_attention_mask_in_length(sequence_id: Union[None, torch.Tensor], S: int, attn_uses_sequence_id: bool, attn_impl: str, attention_mask: Union[torch.Tensor, None]):
+    """Generates the attention mask used for sequence masking in FA v2.
+    Only supports sequence id based sparse attention for no attention masking or attention masking with right padding.
+    In case of left padding:
+        1. Training with left padding is not supported in MPT (see https://github.com/mosaicml/llm-foundry/blob/1eecd4cb8e734499f77f6a35f657b8b20c0adfcb/llmfoundry/models/mpt/modeling_mpt.py#L407).
+        2. For generation with left padding, we only have a single sequence id per sample, so we don't need sequence id based sparse attention.
+    Args:
+        sequence_id (Union[None, torch.Tensor]): Tensor containing the sequence id for each token. Shape (batch_size, seq_len).
+        S (int): Sequence length
+        attn_uses_sequence_id (bool): Whether the attention uses sequence id based masking.
+        attn_impl (str): Attention implementation. This function is only creates attention_mask_in_length for flash attention.
+        attention_mask (Union[torch.Tensor, None]): Attention mask tensor of shape (batch_size, seq_len)
+    Returns:
+        attention_mask_in_length: (batch, seqlen), int, a nonzero number (e.g., 1, 2, 3, etc.) means length of concatenated sequence in b-th batch, and 0 means none. For example, if batch = 3 and seqlen = 6, the attention_mask_in_length is:
+            ```
+            [
+            [2, 3, 0, 0, 0, 0],
+            [3, 2, 0, 0, 0, 0],
+            [6, 0, 0, 0, 0, 0]
+            ]
+            ```
+        , which refers to the 3D-attention mask:
+            ```
+            [
+            [
+                [1, 0, 0, 0, 0, 0],
+                [1, 1, 0, 0, 0, 0],
+                [0, 0, 1, 0, 0, 0],
+                [0, 0, 1, 1, 0, 0],
+                [0, 0, 1, 1, 1, 0],
+                [0, 0, 0, 0, 0, 1]
+            ],
+            [
+                [1, 0, 0, 0, 0, 0],
+                [1, 1, 0, 0, 0, 0],
+                [1, 1, 1, 0, 0, 0],
+                [0, 0, 0, 1, 0, 0],
+                [0, 0, 0, 1, 1, 0],
+                [0, 0, 0, 0, 0, 1]
+            ],
+            [
+                [1, 0, 0, 0, 0, 0],
+                [1, 1, 0, 0, 0, 0],
+                [1, 1, 1, 0, 0, 0],
+                [1, 1, 1, 1, 0, 0],
+                [1, 1, 1, 1, 1, 0],
+                [1, 1, 1, 1, 1, 1]
+            ]
+            ]
+            ```.
+            (The description above is taken verbatim from https://github.com/Dao-AILab/flash-attention/blob/9356a1c0389660d7e231ff3163c1ac17d9e3824a/flash_attn/bert_padding.py#L125 .)
+    """
+    attention_mask_in_length = None
+    if sequence_id is not None and attn_uses_sequence_id and (attn_impl == 'flash'):
+        if attention_mask is not None and attention_mask[:, 0].sum() != attention_mask.shape[0]:
+            raise NotImplementedError('Left padding is not supported with flash attention when attn_uses_sequence_id is set to True.')
+        if S != sequence_id.shape[-1]:
+            raise ValueError(f'Sequence length ({S}) does not match length of sequences in sequence_id ({sequence_id.shape[-1]}).')
+        if attention_mask is not None:
+            sequence_id = sequence_id.masked_fill(~attention_mask, 0)
+        attention_mask_in_length = torch.nn.functional.one_hot(sequence_id)
+        if attention_mask is not None:
+            attention_mask_in_length = attention_mask_in_length.masked_fill(~attention_mask.unsqueeze(-1), 0)
+        attention_mask_in_length = attention_mask_in_length.sum(dim=1)
+        attention_mask_in_length = torch.nn.functional.pad(attention_mask_in_length, (0, S - attention_mask_in_length.shape[-1]), mode='constant', value=0)
+    return attention_mask_in_length
+
+def gen_flash_attn_padding_info(bsz: int, S: int, past_key_len: int, device: torch.device, attention_mask_in_length: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None):
+    flash_attn_padding_info = {}
+    if attention_mask_in_length is None:
+        key_padding_mask = attention_mask
+        if key_padding_mask is None:
+            key_padding_mask = torch.ones((bsz, past_key_len + S), dtype=torch.bool, device=device)
+        query_padding_mask = key_padding_mask[:, -S:]
+        unpadding_function = bert_padding.unpad_input
+    else:
+        key_padding_mask = attention_mask_in_length
+        query_padding_mask = attention_mask_in_length
+        unpadding_function = bert_padding.unpad_input_for_concatenated_sequences
+    (_, indices_q, cu_seqlens_q, max_seqlen_q) = unpadding_function(torch.empty(bsz, S, 1, device=device), query_padding_mask)
+    (_, indices_k, cu_seqlens_k, max_seqlen_k) = unpadding_function(torch.empty(bsz, past_key_len + S, 1, device=device), key_padding_mask)
+    (_, indices_v, _, _) = unpadding_function(torch.empty(bsz, past_key_len + S, 1, device=device), key_padding_mask)
+    flash_attn_padding_info['indices_q'] = indices_q
+    flash_attn_padding_info['indices_k'] = indices_k
+    flash_attn_padding_info['indices_v'] = indices_v
+    flash_attn_padding_info['cu_seqlens_q'] = cu_seqlens_q
+    flash_attn_padding_info['cu_seqlens_k'] = cu_seqlens_k
+    flash_attn_padding_info['max_seqlen_q'] = max_seqlen_q
+    flash_attn_padding_info['max_seqlen_k'] = max_seqlen_k
+    return flash_attn_padding_info
+
+def apply_sequence_id(attn_bias: torch.Tensor, sequence_id: torch.LongTensor, max_seq_len: int) -> torch.Tensor:
+    seq_len = sequence_id.shape[-1]
+    if seq_len > max_seq_len:
+        raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={max_seq_len}')
+    attn_bias = attn_bias[..., :seq_len, :seq_len]
+    cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1)
+    min_val = torch.finfo(attn_bias.dtype).min
+    attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
+    return attn_bias
+
 class MPTPreTrainedModel(PreTrainedModel):
     config_class = MPTConfig
     base_model_prefix = 'model'
     _no_split_modules = ['MPTBlock']
 
+def _fsdp_wrap_fn(self: Union[MPTModel, MPTForCausalLM], module: nn.Module) -> bool:
+    return isinstance(module, MPTBlock)
+
 class MPTModel(MPTPreTrainedModel):
 
     def __init__(self, config: MPTConfig):
@@ -62,6 +193,11 @@ class MPTModel(MPTPreTrainedModel):
         self.emb_drop = nn.Dropout(config.emb_pdrop)
         self.blocks = nn.ModuleList([MPTBlock(device=config.init_device, **config.to_dict()) for _ in range(config.n_layers)])
         self.norm_f = norm_class(config.d_model, device=config.init_device)
+        self.rope = config.attn_config['rope']
+        self.rope_impl = None
+        if self.rope:
+            self.rope_impl = config.attn_config['rope_impl']
+            self.rotary_embedding = gen_rotary_embedding(rope_head_dim=config.d_model // config.n_heads, rope_impl=self.rope_impl, rope_theta=config.attn_config['rope_theta'], rope_dail_config=config.attn_config['rope_dail_config'], rope_hf_config=config.attn_config['rope_hf_config'], max_seq_len=self.config.max_seq_len)
         if config.init_device != 'meta':
             log.info(f'We recommend using config.init_device="meta" with Composer + FSDP for faster initialization.')
             self.apply(self.param_init_fn)
@@ -72,15 +208,18 @@ class MPTModel(MPTPreTrainedModel):
         if config.no_bias:
             for module in self.modules():
                 if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter):
-                    log.info(f'Removing bias ({module.bias}) from {module}.')
+                    log.info(f'Removing bias from module={module!r}.')
                     module.register_parameter('bias', None)
+                if hasattr(module, 'use_bias'):
+                    log.info(f'Setting use_bias=False for module={module!r}.')
+                    module.use_bias = False
         log.debug(self)
         log.debug(f"Using {self.config.init_config['name']} initialization.")
 
-    def get_input_embeddings(self) -> nn.Embedding:
+    def get_input_embeddings(self) -> Union[SharedEmbedding, nn.Embedding]:
         return self.wte
 
-    def set_input_embeddings(self, value: nn.Embedding) -> None:
+    def set_input_embeddings(self, value: Union[SharedEmbedding, nn.Embedding]) -> None:
         self.wte = value
 
     @torch.no_grad()
@@ -101,7 +240,7 @@ class MPTModel(MPTPreTrainedModel):
             attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)
         if self.attn_uses_sequence_id and sequence_id is not None:
             assert isinstance(attn_bias, torch.Tensor)
-            attn_bias = self._apply_sequence_id(attn_bias, sequence_id)
+            attn_bias = apply_sequence_id(attn_bias, sequence_id, self.config.max_seq_len)
         if attention_mask is not None:
             s_k = attention_mask.shape[-1]
             if attn_bias is None:
@@ -113,7 +252,7 @@ class MPTModel(MPTPreTrainedModel):
                 raise ValueError(f'attention_mask shape={attention_mask.shape} ' + f'and prefix_mask shape={prefix_mask.shape} are not equal.')
             min_val = torch.finfo(attn_bias.dtype).min
             attn_bias = attn_bias.masked_fill(~attention_mask.view(-1, 1, 1, s_k), min_val)
-        return (attn_bias, None)
+        return (attn_bias, attention_mask)
 
     def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor) -> torch.Tensor:
         (s_k, s_q) = attn_bias.shape[-2:]
@@ -130,17 +269,7 @@ class MPTModel(MPTPreTrainedModel):
         attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
         return attn_bias
 
-    def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor) -> torch.Tensor:
-        seq_len = sequence_id.shape[-1]
-        if seq_len > self.config.max_seq_len:
-            raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
-        attn_bias = attn_bias[..., :seq_len, :seq_len]
-        cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1)
-        min_val = torch.finfo(attn_bias.dtype).min
-        attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
-        return attn_bias
-
-    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.Tensor]=None) -> BaseModelOutputWithPast:
+    def forward(self, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.Tensor]=None) -> BaseModelOutputWithPast:
         return_dict = return_dict if return_dict is not None else self.config.return_dict
         use_cache = use_cache if use_cache is not None else self.config.use_cache
         if attention_mask is not None:
@@ -156,33 +285,47 @@ class MPTModel(MPTPreTrainedModel):
             raise NotImplementedError('MPT does not support training with left padding.')
         if self.prefix_lm and prefix_mask is None:
             raise ValueError('prefix_mask is a required argument when MPT is configured with prefix_lm=True.')
-        if inputs_embeds is not None:
-            raise NotImplementedError('inputs_embeds is not implemented for MPT.')
         if self.training:
             if self.attn_uses_sequence_id and sequence_id is None:
                 raise ValueError('sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True ' + 'and the model is in train mode.')
             elif self.attn_uses_sequence_id is False and sequence_id is not None:
                 warnings.warn('MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. ' + 'This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True.')
-        S = input_ids.size(1)
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError('You cannot specify both input_ids and inputs_embeds.')
+        elif input_ids is not None:
+            bsz = input_ids.size(0)
+            S = input_ids.size(1)
+            x = self.wte(input_ids)
+            input_device = input_ids.device
+        elif inputs_embeds is not None:
+            bsz = inputs_embeds.size(0)
+            S = inputs_embeds.size(1)
+            x = inputs_embeds
+            input_device = inputs_embeds.device
+        else:
+            raise ValueError('You must specify input_ids or inputs_embeds')
         assert S <= self.config.max_seq_len, f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}'
-        tok_emb = self.wte(input_ids)
-        if self.learned_pos_emb:
-            past_position = 0
-            if past_key_values is not None:
-                if len(past_key_values) != self.config.n_layers:
-                    raise ValueError(f'past_key_values must provide a past_key_value for each attention ' + f'layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r}).')
-                past_position = past_key_values[0][0].size(1)
-                if self.attn_impl == 'torch':
-                    past_position = past_key_values[0][0].size(3)
-            if S + past_position > self.config.max_seq_len:
+        rotary_emb_w_meta_info = None
+        past_position = 0
+        if past_key_values is not None:
+            if len(past_key_values) != self.config.n_layers:
+                raise ValueError(f'past_key_values must provide a past_key_value for each attention ' + f'layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r}).')
+            past_position = past_key_values[0][0].size(1)
+            if self.attn_impl == 'torch':
+                past_position = past_key_values[0][0].size(3)
+        if self.learned_pos_emb or self.rope:
+            if self.learned_pos_emb and S + past_position > self.config.max_seq_len:
                 raise ValueError(f'Cannot forward input with past sequence length {past_position} and current sequence length ' + f'{S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.')
-            pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_ids.device).unsqueeze(0)
-            if attention_mask is not None:
-                pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0)
-            pos_emb = self.wpe(pos)
-            x = tok_emb + pos_emb
-        else:
-            x = tok_emb
+            if self.learned_pos_emb or (self.rope and self.rope_impl == 'hf'):
+                pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_device).unsqueeze(0)
+                if attention_mask is not None:
+                    pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0)
+                if self.learned_pos_emb:
+                    x = x + self.wpe(pos)
+                elif self.rope and self.rope_impl == 'hf':
+                    rotary_emb_w_meta_info = {'impl': self.rope_impl, 'rotary_emb': self.rotary_embedding, 'offset_info': pos, 'seq_len': S + past_position}
+            elif self.rope and self.rope_impl == 'dail':
+                rotary_emb_w_meta_info = {'impl': self.rope_impl, 'rotary_emb': self.rotary_embedding, 'offset_info': past_position, 'seq_len': S + past_position}
         if self.embedding_fraction == 1:
             x = self.emb_drop(x)
         else:
@@ -190,18 +333,26 @@ class MPTModel(MPTPreTrainedModel):
             assert isinstance(self.emb_drop, nn.Module)
             x = self.emb_drop(x_shrunk)
         (attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=torch.float32, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id)
+        attention_mask_in_length = gen_attention_mask_in_length(sequence_id=sequence_id, S=S, attn_uses_sequence_id=self.attn_uses_sequence_id, attn_impl=self.attn_impl, attention_mask=attention_mask)
+        alibi_slopes = None
+        if self.alibi and self.attn_impl == 'flash':
+            alibi_slopes = gen_slopes(n_heads=self.config.n_heads, alibi_bias_max=self.alibi_bias_max, device=x.device, return_1d=True)
+        presents = () if use_cache else None
         if use_cache and past_key_values is None:
             past_key_values = [() for _ in range(self.config.n_layers)]
         all_hidden_states = () if output_hidden_states else None
         all_self_attns = () if output_attentions else None
+        flash_attn_padding_info = {}
+        if self.attn_impl == 'flash':
+            flash_attn_padding_info = gen_flash_attn_padding_info(bsz, S, past_position, x.device, attention_mask_in_length, attention_mask)
         for (b_idx, block) in enumerate(self.blocks):
             if output_hidden_states:
                 assert all_hidden_states is not None
                 all_hidden_states = all_hidden_states + (x,)
             past_key_value = past_key_values[b_idx] if past_key_values is not None else None
-            (x, attn_weights, past_key_value) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal, output_attentions=bool(output_attentions))
-            if past_key_values is not None:
-                past_key_values[b_idx] = past_key_value
+            (x, attn_weights, present) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, rotary_emb_w_meta_info=rotary_emb_w_meta_info, attention_mask=attention_mask, is_causal=self.is_causal, output_attentions=bool(output_attentions), alibi_slopes=alibi_slopes, flash_attn_padding_info=flash_attn_padding_info)
+            if presents is not None:
+                presents += (present,)
             if output_attentions:
                 assert all_self_attns is not None
                 all_self_attns = all_self_attns + (attn_weights,)
@@ -209,14 +360,14 @@ class MPTModel(MPTPreTrainedModel):
         if output_hidden_states:
             assert all_hidden_states is not None
             all_hidden_states = all_hidden_states + (x,)
-        return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns)
+        return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attns)
 
     def param_init_fn(self, module: nn.Module) -> None:
         init_fn_name = self.config.init_config['name']
         MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
 
     def fsdp_wrap_fn(self, module: nn.Module) -> bool:
-        return isinstance(module, MPTBlock)
+        return _fsdp_wrap_fn(self, module)
 
     def activation_checkpointing_fn(self, module: nn.Module) -> bool:
         return isinstance(module, MPTBlock)
@@ -225,10 +376,12 @@ class MPTForCausalLM(MPTPreTrainedModel):
 
     def __init__(self, config: MPTConfig):
         super().__init__(config)
-        if not config.tie_word_embeddings:
-            raise ValueError('MPTForCausalLM only supports tied word embeddings')
         log.info(f'Instantiating an MPTForCausalLM model from {__file__}')
         self.transformer: MPTModel = MPTModel(config)
+        self.lm_head = None
+        if not config.tie_word_embeddings:
+            self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False, device=config.init_device)
+            self.lm_head._fsdp_wrap = True
         for child in self.transformer.children():
             if isinstance(child, torch.nn.ModuleList):
                 continue
@@ -244,17 +397,28 @@ class MPTForCausalLM(MPTPreTrainedModel):
                     raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
             self.logit_scale = logit_scale
 
-    def get_input_embeddings(self) -> nn.Embedding:
-        return self.transformer.wte
+    def get_input_embeddings(self) -> Union[SharedEmbedding, nn.Embedding]:
+        return self.transformer.get_input_embeddings()
 
     def set_input_embeddings(self, value: Union[SharedEmbedding, nn.Embedding]) -> None:
-        self.transformer.wte = value
+        self.transformer.set_input_embeddings(value)
 
-    def get_output_embeddings(self) -> nn.Embedding:
-        return self.transformer.wte
+    def get_output_embeddings(self) -> Union[SharedEmbedding, nn.Embedding, nn.Linear]:
+        if self.lm_head is not None:
+            return self.lm_head
+        return self.transformer.get_input_embeddings()
 
-    def set_output_embeddings(self, new_embeddings: Union[SharedEmbedding, nn.Embedding]) -> None:
-        self.transformer.wte = new_embeddings
+    def set_output_embeddings(self, new_embeddings: Union[SharedEmbedding, nn.Embedding, nn.Linear]) -> None:
+        if self.lm_head is not None:
+            self.lm_head = new_embeddings
+        else:
+            if not isinstance(new_embeddings, (SharedEmbedding, nn.Embedding)):
+                raise ValueError('new_embeddings must be an instance of SharedEmbedding ' + f'or nn.Embedding, but got {type(new_embeddings)}.')
+            warnings.warn('Using `set_output_embeddings` to set the embedding layer of ' + 'MPTForCausalLM with tied weights. Given weights are tied, ' + 'using `set_input_embeddings` is recommended over using ' + '`set_output_embeddings`.')
+            self.transformer.set_input_embeddings(new_embeddings)
+
+    def tie_weights(self) -> None:
+        self.lm_head = None
 
     def set_decoder(self, decoder: MPTModel) -> None:
         self.transformer = decoder
@@ -262,13 +426,16 @@ class MPTForCausalLM(MPTPreTrainedModel):
     def get_decoder(self) -> MPTModel:
         return self.transformer
 
-    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.FloatTensor]=None) -> CausalLMOutputWithPast:
+    def forward(self, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.FloatTensor]=None) -> CausalLMOutputWithPast:
         return_dict = return_dict if return_dict is not None else self.config.return_dict
         use_cache = use_cache if use_cache is not None else self.config.use_cache
-        if inputs_embeds is not None:
-            raise NotImplementedError('inputs_embeds has to be None (for hf/peft support).')
-        outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache)
-        logits = self.transformer.wte(outputs.last_hidden_state.to(self.transformer.wte.weight.device), True)
+        outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache, inputs_embeds=inputs_embeds)
+        if self.lm_head is not None:
+            logits = self.lm_head(outputs.last_hidden_state)
+        else:
+            out = outputs.last_hidden_state
+            out = out.to(self.transformer.wte.weight.device)
+            logits = self.transformer.wte(out, True)
         if self.logit_scale is not None:
             if self.logit_scale == 0:
                 warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.')
@@ -285,14 +452,34 @@ class MPTForCausalLM(MPTPreTrainedModel):
         MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
 
     def fsdp_wrap_fn(self, module: nn.Module) -> bool:
-        return isinstance(module, MPTBlock)
+        return _fsdp_wrap_fn(self, module)
 
     def activation_checkpointing_fn(self, module: nn.Module) -> bool:
-        return isinstance(module, MPTBlock)
+        act_ckpt_list = getattr(self.config, 'activation_checkpointing_target', None) or ['MPTBlock']
+        if isinstance(act_ckpt_list, str):
+            act_ckpt_list = [act_ckpt_list]
+        elif not isinstance(act_ckpt_list, list):
+            raise ValueError(f'activation_checkpointing_target must be either a single string or a list, but got {type(act_ckpt_list)}')
+        if 'MPTBlock' in act_ckpt_list or 'mptblock' in act_ckpt_list:
+            if len(act_ckpt_list) > 1:
+                log.info('Activation checkpointing MPTBlock only (ignoring other sub-block modules specified in activation_checkpointing_target).')
+            return isinstance(module, MPTBlock)
+        mod_types = ()
+        for mod_name in act_ckpt_list:
+            if mod_name.lower() == 'mptblock':
+                mod_types += (MPTBlock,)
+            elif mod_name in ATTN_CLASS_REGISTRY:
+                mod_types += (ATTN_CLASS_REGISTRY[mod_name],)
+            elif mod_name in FFN_CLASS_REGISTRY:
+                mod_types += (FFN_CLASS_REGISTRY[mod_name],)
+            elif mod_name in NORM_CLASS_REGISTRY:
+                mod_types += (NORM_CLASS_REGISTRY[mod_name],)
+            else:
+                msg = ', '.join(list(ATTN_CLASS_REGISTRY.keys()) + list(FFN_CLASS_REGISTRY.keys()) + list(NORM_CLASS_REGISTRY.keys()) + ['MPTBlock'])
+                raise ValueError(f'{mod_name} (specified in activation_checkpointing_target) is not a recognized option out of available options {msg}.')
+        return isinstance(module, mod_types)
 
     def prepare_inputs_for_generation(self, input_ids: torch.Tensor, past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]]=None, inputs_embeds: Optional[torch.Tensor]=None, **kwargs: Any) -> Dict[str, Any]:
-        if inputs_embeds is not None:
-            raise NotImplementedError('inputs_embeds is not implemented for MPT yet')
         attention_mask = kwargs['attention_mask'].bool()
         if attention_mask[:, -1].sum() != attention_mask.shape[0]:
             raise NotImplementedError('MPT does not support generation with right padding.')
@@ -308,12 +495,16 @@ class MPTForCausalLM(MPTPreTrainedModel):
                 raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.')
         else:
             prefix_mask = None
-        return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True)}
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {'inputs_embeds': inputs_embeds}
+        else:
+            model_inputs = {'input_ids': input_ids}
+        model_inputs.update({'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True)})
+        return model_inputs
 
     @staticmethod
     def _reorder_cache(past_key_values: List[Tuple[torch.Tensor, torch.Tensor]], beam_idx: torch.LongTensor) -> List[Tuple[torch.Tensor, ...]]:
         """Used by HuggingFace generate when using beam search with kv-caching.
-
         See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133
         for an example in transformers.
         """