Update modeling_minicpm.py

modeling_minicpm.py

@@ -144,12 +144,10 @@ def compressed_attention(
 def calc_chunks_with_stride(cu_seqlen, chunk_size, kernel_stride):
     """
     Compute the chunks that require Sparse attention, with stride support.
-
     Args:
         cu_seqlen (torch.Tensor): Cumulative sequence lengths for each sample.
         chunk_size (int): Chunk size used for Sparse attention.
         kernel_stride (int): Stride size when sliding over the sequence.
-
     Returns:
         filtered_indices (torch.Tensor): Indices used to directly index into the key/value tensors.
         cu_seqlens_compressed (torch.Tensor): Cumulative sequence lengths after compression.
@@ -192,7 +190,6 @@ class CompressK(torch.nn.Module):
     def __init__(self, head_num_k, head_dim, kernel_size, kernel_stride=16):
         """
         Module for compressing key (K) representations.
-
         Args:
             head_num_k (int): Number of key attention heads.
             head_dim (int): Dimension of each attention head.
@@ -208,15 +205,12 @@ class CompressK(torch.nn.Module):
     def forward(self, k: torch.Tensor, cu_seqlens):
         """
         Forward pass for compressing the key (K) tensor.
-
         Args:
             k (torch.Tensor): Input key tensor of shape (total_seq_len, num_heads, head_dim).
             cu_seqlens (torch.Tensor): Cumulative sequence lengths for each sample in the batch, typically used for handling variable-length sequences.
-
         Returns:
             compress_k (torch.Tensor): Compressed key tensor.
             cu_seqlens_compressed (torch.Tensor): Updated cumulative sequence lengths after compression.
-
         """
         # Compute chunk-related metadata, with stride support
         filtered_k_indices, cu_seqlens_compressed = calc_chunks_with_stride(
@@ -535,7 +529,6 @@ def rotate_half(x):
 
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
     """Applies Rotary Position Embedding to the query and key tensors.
-
     Args:
         q (`torch.Tensor`): The query tensor.
         k (`torch.Tensor`): The key tensor.
@@ -906,7 +899,6 @@ class MiniCPMFlashAttention2(MiniCPMAttention):
         """
         Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
         first unpad the input, then computes the attention scores and pad the final attention scores.
-
         Args:
             query_states (`torch.Tensor`):
                 Input query states to be passed to Flash Attention API
@@ -1136,7 +1128,6 @@ class MiniCPMInfLLMv2Attention(MiniCPMAttention):
             """
             Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
             first unpad the input, then computes the attention scores and pad the final attention scores.
-
             Args:
                 query_states (`torch.Tensor`):
                     Input query states to be passed to Flash Attention API
@@ -1364,7 +1355,6 @@ class MiniCPMInfLLMv2Attention(MiniCPMAttention):
         """
         Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
         first unpad the input, then computes the attention scores and pad the final attention scores.
-
         Args:
             query_states (`torch.Tensor`):
                 Input query states to be passed to Flash Attention API
@@ -1628,11 +1618,9 @@ MINICPM_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 ([`MiniCPMConfig`]):
             Model configuration class with all the parameters of the model. Initializing with a config file does not
@@ -1672,50 +1660,38 @@ MINICPM_INPUTS_DOCSTRING = r"""
         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 `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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
             Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
             blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
             returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
-
             Two formats are allowed:
             - a [`~cache_utils.Cache`] instance;
             - 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)`). This is also known as the legacy
             cache format.
-
             The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
             legacy cache format will be returned.
-
             If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
             have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
             of shape `(batch_size, sequence_length)`.
@@ -1744,7 +1720,6 @@ MINICPM_INPUTS_DOCSTRING = r"""
 class MiniCPMModel(MiniCPMPreTrainedModel):
     """
     Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]
-
     Args:
         config: MiniCPMConfig
     """
@@ -1971,20 +1946,14 @@ class MiniCPMForCausalLM(MiniCPMPreTrainedModel):
                 Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                 config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                 (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
         Returns:
-
         Example:
-
         ```python
         >>> from transformers import AutoTokenizer, MiniCPMForCausalLM
-
         >>> model = MiniCPMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
         >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
-
         >>> prompt = "Hey, are you conscious? Can you talk to me?"
         >>> inputs = tokenizer(prompt, return_tensors="pt")
-
         >>> # Generate
         >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
         >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
@@ -2164,10 +2133,8 @@ class MiniCPMForCausalLM(MiniCPMPreTrainedModel):
 @add_start_docstrings(
     """
     The MiniCPM Model transformer with a sequence classification head on top (linear layer).
-
     [`MiniCPMForSequenceClassification`] 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
@@ -2280,4 +2247,4 @@ class MiniCPMForSequenceClassification(MiniCPMPreTrainedModel):
             past_key_values=transformer_outputs.past_key_values,
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
-        )
+        )