Clean up landmark patching

src/axolotl/monkeypatch/llama_landmark_attn.py

@@ -28,15 +28,23 @@ from typing import List, Optional, Tuple, Union
 import torch
 import torch.utils.checkpoint
 from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-from transformers.activations import ACT2FN
+from torch.nn import CrossEntropyLoss
 from transformers.modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
-    SequenceClassifierOutputWithPast,
 )
-from transformers.modeling_utils import PreTrainedModel
 from transformers.models.llama.configuration_llama import LlamaConfig
+from transformers.models.llama.modeling_llama import (
+    LLAMA_INPUTS_DOCSTRING,
+    LLAMA_START_DOCSTRING,
+    LlamaMLP,
+    LlamaPreTrainedModel,
+    LlamaRMSNorm,
+    LlamaRotaryEmbedding,
+    _expand_mask,
+    _make_causal_mask,
+    rotate_half,
+)
 from transformers.utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -51,131 +59,6 @@ _CONFIG_FOR_DOC = "LlamaConfig"
 MEM_TOKEN = "<landmark>"  # nosec
 
 
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size,
-    dtype: torch.dtype,
-    device: torch.device,
-    past_key_values_length: int = 0,
-):
-    """
-    Make causal mask used for bi-directional self-attention.
-    """
-    bsz, tgt_len = input_ids_shape
-    mask = torch.full(
-        (tgt_len, tgt_len),
-        torch.tensor(torch.finfo(dtype).min, device=device),
-        device=device,
-    )
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-
-    if past_key_values_length > 0:
-        mask = torch.cat(
-            [
-                torch.zeros(
-                    tgt_len, past_key_values_length, dtype=dtype, device=device
-                ),
-                mask,
-            ],
-            dim=-1,
-        )
-    return mask[None, None, :, :].expand(
-        bsz, 1, tgt_len, tgt_len + past_key_values_length
-    )
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(
-        inverted_mask.to(torch.bool), torch.finfo(dtype).min
-    )
-
-
-class LlamaRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        LlamaRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-
-        # convert into half-precision if necessary
-        if self.weight.dtype in [torch.float16, torch.bfloat16]:
-            hidden_states = hidden_states.to(self.weight.dtype)
-
-        return self.weight * hidden_states
-
-
-class LlamaRotaryEmbedding(torch.nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
-        super().__init__()
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
-        self.register_buffer("inv_freq", inv_freq)
-
-        # Build here to make `torch.jit.trace` work.
-        self.max_seq_len_cached = max_position_embeddings
-        t = torch.arange(
-            self.max_seq_len_cached,
-            device=self.inv_freq.device,
-            dtype=self.inv_freq.dtype,
-        )
-        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-        # Different from paper, but it uses a different permutation in order to obtain the same calculation
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer(
-            "cos_cached", emb.cos()[None, None, :, :], persistent=False
-        )
-        self.register_buffer(
-            "sin_cached", emb.sin()[None, None, :, :], persistent=False
-        )
-
-    def forward(self, x, seq_len=None):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
-        if seq_len > self.max_seq_len_cached:
-            self.max_seq_len_cached = seq_len
-            t = torch.arange(
-                self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype
-            )
-            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-            # Different from paper, but it uses a different permutation in order to obtain the same calculation
-            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
-            self.register_buffer(
-                "cos_cached", emb.cos()[None, None, :, :], persistent=False
-            )
-            self.register_buffer(
-                "sin_cached", emb.sin()[None, None, :, :], persistent=False
-            )
-        return (
-            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-        )
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
     # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
     cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
@@ -190,24 +73,11 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
     return q_embed, k_embed
 
 
-class LlamaMLP(nn.Module):
-    def __init__(
-        self,
-        hidden_size: int,
-        intermediate_size: int,
-        hidden_act: str,
-    ):
-        super().__init__()
-        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
-        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
-        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
-        self.act_fn = ACT2FN[hidden_act]
-
-    def forward(self, x):
-        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-
-
 class LandmarkGroupedSoftmaxFunction(torch.autograd.Function):
+    """
+    Landmark grouped softmax function.
+    """
+
     # Note that forward, setup_context, and backward are @staticmethods
     @staticmethod
     def forward(ctx, x, dim, mem_cnt, resp_mem_idx):
@@ -682,16 +552,14 @@ class LlamaAttention(nn.Module):
         # upcast attention to fp32
         if is_mem is None:
             raise ValueError("Don't use this without landmarks")
-            # attn_weights = nn.functional.softmax(
-            #     attn_weights, dim=-1, dtype=torch.float32
-            # ).to(query_states.dtype)
-        else:
-            attn_weights = landmark_grouped_softmax(
-                attn_weights,
-                dim=-1,
-                is_mem=is_mem.expand(-1, self.num_heads, -1, -1),
-                last_section_mask=last_section_mask,
-            ).to(query_states.dtype)
+
+        attn_weights = landmark_grouped_softmax(
+            attn_weights,
+            dim=-1,
+            is_mem=is_mem.expand(-1, self.num_heads, -1, -1),
+            last_section_mask=last_section_mask,
+        ).to(query_states.dtype)
+
         if attn_prefix is not None:
             attn_prefix, attn_weights = torch.split(
                 attn_weights,
@@ -722,6 +590,10 @@ class LlamaAttention(nn.Module):
 
 
 class LlamaDecoderLayer(nn.Module):
+    """
+    Llama Decoder layer
+    """
+
     def __init__(self, config: LlamaConfig):
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -802,114 +674,6 @@ class LlamaDecoderLayer(nn.Module):
         return outputs
 
 
-LLAMA_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`LlamaConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-@add_start_docstrings(
-    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
-    LLAMA_START_DOCSTRING,
-)
-class LlamaPreTrainedModel(PreTrainedModel):
-    config_class = LlamaConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["LlamaDecoderLayer"]
-    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, LlamaModel):
-            module.gradient_checkpointing = value
-
-
-LLAMA_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
-            `past_key_values`).
-
-            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
-            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
-            information on the default strategy.
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
-            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
-            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
-
-            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
 @add_start_docstrings(
     "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
     LLAMA_START_DOCSTRING,
@@ -1178,6 +942,10 @@ class LlamaModel(LlamaPreTrainedModel):
 
 
 class LlamaForCausalLM(LlamaPreTrainedModel):
+    """
+    Llama model with a causal language modeling head.
+    """
+
     def __init__(self, config):
         super().__init__(config)
         self.model = LlamaModel(config)
@@ -1448,149 +1216,15 @@ class LlamaForCausalLM(LlamaPreTrainedModel):
         return reordered_past
 
 
-@add_start_docstrings(
-    """
-    The LLaMa Model transformer with a sequence classification head on top (linear layer).
-
-    [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """,
-    LLAMA_START_DOCSTRING,
-)
-class LlamaForSequenceClassification(LlamaPreTrainedModel):
-    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = LlamaModel(config)
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        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, SequenceClassifierOutputWithPast]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        transformer_outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_outputs[0]
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.pad_token_id is None and batch_size != 1:
-            raise ValueError(
-                "Cannot handle batch sizes > 1 if no padding token is defined."
-            )
-        if self.config.pad_token_id is None:
-            sequence_lengths = -1
-        else:
-            if input_ids is not None:
-                sequence_lengths = (
-                    torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
-                ).to(logits.device)
-            else:
-                sequence_lengths = -1
-
-        pooled_logits = logits[
-            torch.arange(batch_size, device=logits.device), sequence_lengths
-        ]
-
-        loss = None
-        if labels is not None:
-            labels = labels.to(logits.device)
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (
-                    labels.dtype == torch.long or labels.dtype == torch.int
-                ):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(pooled_logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(
-                    pooled_logits.view(-1, self.num_labels), labels.view(-1)
-                )
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(pooled_logits, labels)
-        if not return_dict:
-            output = (pooled_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-
 def add_mem_tokens(example, mem_freq, mem_id):
-    x = example["input_ids"]
+    ids = example["input_ids"]
     ret = []
     prev_idx = 0
-    for t_idx in range(mem_freq, len(x), mem_freq):
-        ret.extend(x[prev_idx:t_idx])
+    for t_idx in range(mem_freq, len(ids), mem_freq):
+        ret.extend(ids[prev_idx:t_idx])
         ret.append(mem_id)
         prev_idx = t_idx
-    ret.extend(x[prev_idx:])
+    ret.extend(ids[prev_idx:])
     # drop attention_mask
     return {"input_ids": ret}
 
@@ -1602,3 +1236,4 @@ def patch_llama_with_landmark_attn():
     transformers.models.llama.modeling_llama.LlamaModel = LlamaModel
     transformers.models.llama.modeling_llama.LlamaAttention = LlamaAttention
     transformers.models.llama.modeling_llama.LlamaDecoderLayer = LlamaDecoderLayer
+    transformers.models.llama.modeling_llama.apply_rotary_pos_emb = apply_rotary_pos_emb