# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/bamba/modular_bamba.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_bamba.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2024 IBM and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections.abc import Callable from typing import Any, Optional, TypedDict import torch from torch import nn from transformers.activations import ACT2FN from ... import initialization as init from ...cache_utils import Cache from ...generation import GenerationMixin from ...integrations import use_kernel_forward_from_hub, use_kernelized_func from ...integrations.hub_kernels import lazy_load_kernel from ...masking_utils import create_causal_mask from ...modeling_layers import GradientCheckpointingLayer from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from ...processing_utils import Unpack from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging from ...utils.generic import maybe_autocast from ...utils.import_utils import resolve_internal_import from .configuration_bamba import BambaConfig logger = logging.get_logger(__name__) class BambaFlashAttentionKwargs(TypedDict, total=False): """ Keyword arguments for advanced Flash Attention, causal-conv1d, and mamba_ssm kernel usage. Use cases include padding-free training and fewer `torch.compile` graph breaks. cu_seq_lens_q (`torch.LongTensor`): Gets cumulative sequence length for query state. cu_seq_lens_k (`torch.LongTensor`): Gets cumulative sequence length for key state. max_length_q (`int`): Maximum sequence length for query state. max_length_k (`int`): Maximum sequence length for key state. seq_idx (`torch.IntTensor`): Index of each packed sequence. """ cu_seq_lens_q: torch.LongTensor cu_seq_lens_k: torch.LongTensor max_length_q: int max_length_k: int seq_idx: torch.IntTensor class HybridMambaAttentionDynamicCache: """ A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache (which has a constant shape regardless of seq_len). This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states` and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`, while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors). For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors), while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`, and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`. """ is_compileable = False def __init__(self, config: BambaConfig, batch_size, dtype=torch.float16, device=None): self.layers_block_type = config.layers_block_type self.has_previous_state = False # only used by mamba conv_kernel_size = config.mamba_d_conv ssm_state_size = config.mamba_d_state self.conv_states = [] self.ssm_states = [] self.transformer_layers = [] for i in range(config.num_hidden_layers): if self.layers_block_type[i] == "mamba": self.conv_states += [ torch.zeros( batch_size, (config.mamba_expand * config.hidden_size + 2 * config.mamba_n_groups * ssm_state_size), conv_kernel_size, device=device, dtype=dtype, ) ] self.ssm_states += [ torch.zeros( batch_size, config.mamba_n_heads, config.mamba_d_head, ssm_state_size, device=device, dtype=dtype, ) ] else: self.conv_states += [torch.tensor([[]] * batch_size, device=device)] self.ssm_states += [torch.tensor([[]] * batch_size, device=device)] self.transformer_layers.append(i) self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)] self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)] def __len__(self): return len(self.key_cache) def __getitem__(self, layer_idx): return self.key_cache[layer_idx], self.value_cache[layer_idx] def update( self, key_states: torch.Tensor, value_states: torch.Tensor, layer_idx: int, cache_kwargs: dict[str, Any] | None = None, ) -> tuple[torch.Tensor, torch.Tensor]: # Update the cache if self.key_cache[layer_idx].shape[-1] == 0: self.key_cache[layer_idx] = key_states self.value_cache[layer_idx] = value_states else: self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2) self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2) return self.key_cache[layer_idx], self.value_cache[layer_idx] def reorder_cache(self, beam_idx: torch.LongTensor): """Reorders the cache for beam search, given the selected beam indices.""" if self.get_seq_length() > 0: for layer_idx in range(len(self.key_cache)): device = self.key_cache[layer_idx].device self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device)) device = self.value_cache[layer_idx].device self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device)) device = self.conv_states[layer_idx].device self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device)) device = self.ssm_states[layer_idx].device self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device)) def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> tuple[int, int]: """Return the length and offset of the cache, used to generate the mask""" kv_offset = 0 query_length = cache_position.shape[0] kv_length = self.get_seq_length(layer_idx) + query_length return kv_length, kv_offset def get_seq_length(self, layer_idx: int | None = 0) -> int: """Returns the sequence length of the cached states. A layer index can be optionally passed.""" # take any layer that contains cache and not empty tensor layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].shape[-1] == 0: return 0 return self.key_cache[layer_idx].shape[-2] class BambaRotaryEmbedding(nn.Module): inv_freq: torch.Tensor # fix linting for `register_buffer` def __init__(self, config: BambaConfig, device=None): super().__init__() self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_type = self.config.rope_parameters["rope_type"] rope_init_fn: Callable = self.compute_default_rope_parameters if self.rope_type != "default": rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = rope_init_fn(self.config, device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) @staticmethod def compute_default_rope_parameters( config: BambaConfig | None = None, device: Optional["torch.device"] = None, seq_len: int | None = None, ) -> tuple["torch.Tensor", float]: """ Computes the inverse frequencies according to the original RoPE implementation Args: config ([`~transformers.PreTrainedConfig`]): The model configuration. device (`torch.device`): The device to use for initialization of the inverse frequencies. seq_len (`int`, *optional*): The current sequence length. Unused for this type of RoPE. Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). """ base = config.rope_parameters["rope_theta"] dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads attention_factor = 1.0 # Unused in this type of RoPE # Compute the inverse frequencies inv_freq = 1.0 / ( base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) ) return inv_freq, attention_factor @torch.no_grad() @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) def forward(self, x, position_ids): inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) position_ids_expanded = position_ids[:, None, :].float() device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" with maybe_autocast(device_type=device_type, enabled=False): # Force float32 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() * self.attention_scaling sin = emb.sin() * self.attention_scaling return cos.to(dtype=x.dtype), sin.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 repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) def eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: torch.Tensor | None, scaling: float, dropout: float = 0.0, **kwargs: Unpack[TransformersKwargs], ): key_states = repeat_kv(key, module.num_key_value_groups) value_states = repeat_kv(value, module.num_key_value_groups) attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling if attention_mask is not None: attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights # Adapted from transformers.models.glm.modular_glm.apply_rotary_pos_emb def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Removes the interleaving of cos and sin from GLM Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) # Keep half or full tensor for later concatenation rotary_dim = cos.shape[-1] q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:] k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:] # Apply rotary embeddings on the first half or full tensor q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin) k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin) # Concatenate back to full shape q_embed = torch.cat([q_embed, q_pass], dim=-1) k_embed = torch.cat([k_embed, k_pass], dim=-1) return q_embed, k_embed @use_kernelized_func(apply_rotary_pos_emb) class BambaAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: BambaConfig, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.scaling = self.head_dim**-0.5 self.attention_dropout = config.attention_dropout self.is_causal = True self.q_proj = nn.Linear( config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias ) self.k_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.v_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.o_proj = nn.Linear( config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias ) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, attention_mask: torch.Tensor | None = None, past_key_values: Cache | None = None, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor, torch.Tensor]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_values is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( self.config._attn_implementation, eager_attention_forward ) attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class BambaRMSNormGated(torch.nn.Module): def __init__(self, hidden_size, eps=1e-6): super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states, gate=None): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) if gate is not None: hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32)) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) # Helper methods for segment sum computation def pad_tensor_by_size(input_tensor: torch.Tensor, pad_size: int): """ Padding x tensor with `pad_size` on the seq_len dim (dim=1) Assumes that we only have tensors of either size 4 or 3 """ pad_shape = (0, 0, 0, 0, 0, pad_size, 0, 0) if len(input_tensor.shape) == 4 else (0, 0, 0, pad_size, 0, 0) return torch.nn.functional.pad(input_tensor, pad_shape, mode="constant", value=0) def reshape_into_chunks(input_tensor, pad_size, chunk_size): """ Padding input_tensor with `pad_size` on the seq_len dim (dim=1) and simultaneously splitting it into chunk sequences. Assumes that we only have tensors of either size 4 or 3 """ # [bsz, seq_len, ...] -> [bsz, seq_len multiple of chunk_size, ...] input_tensor = pad_tensor_by_size(input_tensor, pad_size) if len(input_tensor.shape) == 3: # [bsz, seq_len multiple of chunk_size, num_heads] -> [bsz, -1, chunk_size, num_heads] return input_tensor.reshape(input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2]) else: # [bsz, seq_len multiple of chunk_size, num_heads, head_dim or state_size] -> [bsz, -1, chunk_size, num_heads, head_dim or state_size] return input_tensor.reshape( input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2], input_tensor.shape[3] ) def segment_sum(input_tensor): """ More stable segment sum calculation. Uses cumulative sums and masking instead of direct subtractions. """ chunk_size = input_tensor.size(-1) # 1. expand input tensor to have an additional dimension and repeat along that dimension # [..., chunk_size] -> [..., chunk_size, chunk_size] input_tensor = input_tensor[..., None].expand(*input_tensor.size(), chunk_size) # 2. create a lower triangular mask with the diagonal set to 0 to 0 out elements above diag mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=-1) input_tensor = input_tensor.masked_fill(~mask, 0) # 3. compute actual cumsum tensor_segsum = torch.cumsum(input_tensor, dim=-2) # 4. apply mask to keep only the lower triangular part of the cumulative sum result (incl diagonal this time) mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=0) tensor_segsum = tensor_segsum.masked_fill(~mask, -torch.inf) return tensor_segsum def apply_mask_to_padding_states(hidden_states, attention_mask): """ Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66 """ # NOTE: attention mask is a 2D boolean tensor if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1: dtype = hidden_states.dtype hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype) return hidden_states # Adapted from transformers.models.mamba2.modeling_mamba2.Mamba2Mixer class BambaMixer(nn.Module): """ Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`. A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective) ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4, and is why Mamba is called **selective** state spaces) The are a few differences between this and Mamba2Mixer: - The variable use_precomputed_states is slightly different due to the hybrid cache structure - There's a few non-obvious bugs fixed with batching in the slow path that exist in main - Some extra variables that our layer doesn't need have been removed - We ported most of the refactors in https://github.com/huggingface/transformers/pull/35154, which is (as of Dec 18, 2024) unmerged """ def __init__(self, config: BambaConfig, layer_idx: int): super().__init__() self.num_heads = config.mamba_n_heads self.hidden_size = config.hidden_size self.ssm_state_size = config.mamba_d_state self.conv_kernel_size = config.mamba_d_conv self.intermediate_size = int(config.mamba_expand * self.hidden_size) self.layer_idx = layer_idx self.use_conv_bias = config.mamba_conv_bias self.activation = config.hidden_act self.act = ACT2FN[config.hidden_act] self.use_bias = config.mamba_proj_bias self.layer_norm_epsilon = config.rms_norm_eps self.n_groups = config.mamba_n_groups self.head_dim = config.mamba_d_head self.chunk_size = config.mamba_chunk_size self.time_step_limit = config.time_step_limit self.time_step_min = config.time_step_min self.time_step_max = config.time_step_max self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size self.conv1d = nn.Conv1d( in_channels=self.conv_dim, out_channels=self.conv_dim, bias=config.mamba_conv_bias, kernel_size=self.conv_kernel_size, groups=self.conv_dim, padding=self.conv_kernel_size - 1, ) # projection of the input hidden states projection_size = self.intermediate_size + self.conv_dim + self.num_heads self.in_proj = nn.Linear( self.hidden_size, projection_size, bias=self.use_bias, ) # selective projection used to make dt, B and C input dependent # time step projection (discretization) # instantiate once and copy inv_dt in init_weights of PretrainedModel self.dt_bias = nn.Parameter(torch.ones(self.num_heads)) # S4D real initialization. These are not discretized! # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded A = torch.arange(1, self.num_heads + 1) self.A_log = nn.Parameter(torch.log(A)) self.norm = BambaRMSNormGated(self.intermediate_size, eps=self.layer_norm_epsilon) self.D = nn.Parameter(torch.ones(self.num_heads)) self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias) global causal_conv1d_update, causal_conv1d_fn causal_conv1d = lazy_load_kernel("causal-conv1d") causal_conv1d_update = getattr(causal_conv1d, "causal_conv1d_update", None) causal_conv1d_fn = getattr(causal_conv1d, "causal_conv1d_fn", None) global selective_state_update, mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined mamba_ssm = lazy_load_kernel("mamba-ssm") selective_state_update = resolve_internal_import( mamba_ssm, chained_path="ops.triton.selective_state_update.selective_state_update" ) mamba_chunk_scan_combined = resolve_internal_import( mamba_ssm, chained_path="ops.triton.ssd_combined.mamba_chunk_scan_combined" ) mamba_split_conv1d_scan_combined = resolve_internal_import( mamba_ssm, chained_path="ops.triton.ssd_combined.mamba_split_conv1d_scan_combined" ) global is_fast_path_available is_fast_path_available = all( ( selective_state_update, mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined, causal_conv1d_fn, causal_conv1d_update, ) ) if not is_fast_path_available: logger.warning_once( "The fast path is not available because one of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)`" " is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and" " https://github.com/Dao-AILab/causal-conv1d" ) else: logger.warning_once("The fast path for Bamba will be used when running the model on a GPU") def cuda_kernels_forward( self, hidden_states: torch.Tensor, cache_params: HybridMambaAttentionDynamicCache | None = None, cache_position: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, seq_idx: torch.IntTensor | None = None, ): # 1. Gated MLP's linear projection hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask) projected_states = self.in_proj(hidden_states) # Set up dimensions for reshapes later batch_size, seq_len, _ = hidden_states.shape groups_time_state_size = self.n_groups * self.ssm_state_size use_precomputed_states = ( cache_params is not None and cache_params.has_previous_state and seq_len == 1 and cache_params.conv_states[self.layer_idx].shape[0] == cache_params.ssm_states[self.layer_idx].shape[0] == batch_size and cache_position is not None and cache_position[0] > 0 ) # getting projected states from cache if it exists if use_precomputed_states: gate, hidden_states_B_C, dt = projected_states.squeeze(1).split( [self.intermediate_size, self.conv_dim, self.num_heads], dim=-1 ) # 2. Convolution sequence transformation hidden_states_B_C = causal_conv1d_update( hidden_states_B_C, cache_params.conv_states[self.layer_idx], self.conv1d.weight.squeeze(1), self.conv1d.bias, self.activation, ) hidden_states, B, C = torch.split( hidden_states_B_C, [self.intermediate_size, groups_time_state_size, groups_time_state_size], dim=-1, ) # 3. SSM transformation A = -torch.exp(self.A_log.float()) # (nheads,) A = A[:, None, ...][:, :, None].expand(-1, self.head_dim, self.ssm_state_size).to(dtype=torch.float32) dt = dt[:, :, None].expand(-1, -1, self.head_dim) dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim) D = self.D[:, None, ...].expand(-1, self.head_dim) B = B.view(batch_size, self.n_groups, B.shape[1] // self.n_groups) C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups) hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim) hidden_states = selective_state_update( cache_params.ssm_states[self.layer_idx], hidden_states_reshaped, dt, A, B, C, D, z=None, dt_bias=dt_bias, dt_softplus=True, ) hidden_states = hidden_states.view(batch_size, self.num_heads * self.head_dim) hidden_states = self.norm(hidden_states, gate) # 4. Final linear projection out = self.out_proj(hidden_states)[:, None, ...] # Fused calculations or step by step if no initialized cache is found else: A = -torch.exp(self.A_log.float()) # (num_heads) or (intermediate_size, state_size) dt_limit_kwargs = {} if self.time_step_limit == (0.0, float("inf")) else {"dt_limit": self.time_step_limit} # 2-4. Fused kernel for conv1d, SSM, and the final projection if self.training and cache_params is None: out = mamba_split_conv1d_scan_combined( projected_states, self.conv1d.weight.squeeze(1), self.conv1d.bias, self.dt_bias, A, D=self.D, chunk_size=self.chunk_size, seq_idx=seq_idx, activation=self.activation, rmsnorm_weight=self.norm.weight, rmsnorm_eps=self.norm.variance_epsilon, outproj_weight=self.out_proj.weight, outproj_bias=self.out_proj.bias, headdim=self.head_dim, ngroups=self.n_groups, norm_before_gate=False, return_final_states=False, **dt_limit_kwargs, ) else: gate, hidden_states_B_C, dt = projected_states.split( [self.intermediate_size, self.conv_dim, self.num_heads], dim=-1 ) # 2. Convolution sequence transformation # Init cache if cache_params is not None: # storing the states # If we just take xBC[:, :, -self.d_conv :], it will error if seqlen < self.d_conv # Instead F.pad will pad with zeros if seqlen < self.d_conv, and truncate otherwise. hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2) conv_states = nn.functional.pad( hidden_states_B_C_transposed, (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0), ) cache_params.conv_states[self.layer_idx].copy_(conv_states) if self.activation not in ["silu", "swish"]: hidden_states_B_C = self.act( self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2) ) else: hidden_states_B_C = causal_conv1d_fn( x=hidden_states_B_C.transpose(1, 2), weight=self.conv1d.weight.squeeze(1), bias=self.conv1d.bias, activation=self.activation, seq_idx=seq_idx, ).transpose(1, 2) hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask) hidden_states, B, C = torch.split( hidden_states_B_C, [self.intermediate_size, groups_time_state_size, groups_time_state_size], dim=-1, ) # 3. SSM transformation scan_output, ssm_state = mamba_chunk_scan_combined( hidden_states.view(batch_size, seq_len, -1, self.head_dim), dt, A, B.view(batch_size, seq_len, self.n_groups, -1), C.view(batch_size, seq_len, self.n_groups, -1), chunk_size=self.chunk_size, D=self.D, z=None, seq_idx=seq_idx, return_final_states=True, dt_bias=self.dt_bias, dt_softplus=True, **dt_limit_kwargs, ) # Init cache if ssm_state is not None and cache_params is not None: cache_params.ssm_states[self.layer_idx].copy_(ssm_state) scan_output = scan_output.view(batch_size, seq_len, -1) # Multiply "gate" branch and apply extra normalization layer scan_output = self.norm(scan_output, gate) # 4. Final linear projection out = self.out_proj(scan_output) return out # fmt: off def torch_forward( self, input_states, cache_params: HybridMambaAttentionDynamicCache | None = None, cache_position: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, ): batch_size, seq_len, _ = input_states.shape dtype = input_states.dtype # 1. Gated MLP's linear projection input_states = apply_mask_to_padding_states(input_states, attention_mask) projected_states = self.in_proj(input_states) gate, hidden_states_B_C, dt = projected_states.split( [self.intermediate_size, self.conv_dim, self.num_heads], dim=-1 ) use_precomputed_states = ( cache_params is not None and cache_params.has_previous_state and seq_len == 1 and cache_params.conv_states[self.layer_idx].shape[0] == cache_params.ssm_states[self.layer_idx].shape[0] == batch_size and cache_position is not None and cache_position[0] > 0 ) # 2. Convolution sequence transformation if use_precomputed_states: cache_params.conv_states[self.layer_idx] = cache_params.conv_states[self.layer_idx].roll(shifts=-1, dims=-1) cache_params.conv_states[self.layer_idx][:, :, -1] = hidden_states_B_C[:, 0, :].to(cache_params.conv_states[self.layer_idx].device) # We need to guarantee that anything regarding the cache is on the same device conv_states = cache_params.conv_states[self.layer_idx].to(device=self.conv1d.weight.device) hidden_states_B_C = torch.sum( conv_states * self.conv1d.weight.squeeze(1), dim=-1 ) if self.use_conv_bias: hidden_states_B_C = hidden_states_B_C + self.conv1d.bias hidden_states_B_C = self.act(hidden_states_B_C) else: # Init cache if cache_params is not None: hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2) conv_states = nn.functional.pad( hidden_states_B_C_transposed, (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0) ) cache_params.conv_states[self.layer_idx].copy_(conv_states) hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2)) hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask) hidden_states, B, C = torch.split( hidden_states_B_C, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1 ) # 3. SSM transformation A = -torch.exp(self.A_log.float()) # [num_heads] if use_precomputed_states: # We need to guarantee that anything regarding the cache is on the same device cache_device = cache_params.ssm_states[self.layer_idx].device # Note: there is no need to pad parameter matrices here, as there is just one new token # for batched generation dt = dt[:, 0, :][:, None, ...] dt = dt.transpose(1, 2).expand(batch_size, dt.shape[-1], self.head_dim) # [num_heads] -> [num_heads, head_dim] dt_bias = self.dt_bias[..., None].expand(self.dt_bias.shape[0], self.head_dim) dt = torch.nn.functional.softplus(dt + dt_bias.to(dt.dtype)) dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1]) A = A[..., None, None].expand(self.num_heads, self.head_dim, self.ssm_state_size).to(dtype=torch.float32) # [bsz, num_heads, head_dim, state_size] dA = (torch.exp(dt[..., None] * A)).to(device=cache_device) # Discretize B # [bsz, n_groups * state_size] -> [bsz, n_groups, 1, state_size] -> # -> [bsz, n_groups, group to head repetition factor, state_size] -> [bsz, num_heads, state_size] B = B.reshape(batch_size, self.n_groups, -1)[..., None, :] B = B.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, B.shape[-1]).contiguous() B = B.reshape(batch_size, -1, B.shape[-1]) # [bsz, num_heads, head_dim, state_size] dB = dt[..., None] * B[..., None, :] # Discretize x into dB # [bsz, intermediate_size] -> [bsz, num_heads, head_dim] hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim) dBx = (dB * hidden_states[..., None]).to(device=cache_device) # State calculation cache_params.ssm_states[self.layer_idx].copy_( cache_params.ssm_states[self.layer_idx] * dA + dBx ) # Subsequent output # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size] C = C.reshape(batch_size, self.n_groups, -1)[..., None, :] C = C.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, C.shape[-1]).contiguous() C = C.reshape(batch_size, -1, C.shape[-1]) # [bsz, num_heads, head_dim] ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype) # Shape: [b, h, d, n] # Reshape ssm_states to merge the first two dimensions ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size) # Shape: [b*h, d, n] C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1) # Shape: [b*h, n, 1] y = torch.bmm(ssm_states_reshaped, C_reshaped) y = y.view(batch_size, self.num_heads, self.head_dim) # D skip connection # [num_heads] -> [num_heads, head_dim] D = self.D[..., None].expand(self.D.shape[0], self.head_dim) y = (y + hidden_states * D).to(y.dtype) # [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size] y = y.reshape(batch_size, -1)[:, None, ...] else: # begin ssd naive implementation without einsums dt = nn.functional.softplus(dt + self.dt_bias) dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1]) hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).float() B = B.reshape(batch_size, seq_len, -1, self.ssm_state_size).float() C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).float() B = B.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads) C = C.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads) pad_size = (self.chunk_size - seq_len % self.chunk_size) % self.chunk_size D_residual = self.D[..., None] * pad_tensor_by_size(hidden_states, pad_size) # Discretize x and A hidden_states = hidden_states * dt[..., None] A = A.to(hidden_states.dtype) * dt # Rearrange into blocks/chunks hidden_states, A, B, C = [reshape_into_chunks(t, pad_size, self.chunk_size) for t in (hidden_states, A, B, C)] # [bsz, -1, chunk_size, num_heads] -> [bsz, num_heads, -1, chunk_size] A = A.permute(0, 3, 1, 2) A_cumsum = torch.cumsum(A, dim=-1) # 1. Compute the output for each intra-chunk (diagonal blocks) # This is the analog of a causal mask L = torch.exp(segment_sum(A)) # Contraction of C and B to get G (attention-weights like) G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, :, :] # shape: (b, c, l, s, h, n) G = G_intermediate.sum(dim=-1) # shape: (b, c, l, s, h) # Compute M, equivalent to applying attention mask to weights M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None] M = M_intermediate.sum(dim=-1) # Compute Y_diag (apply to values) Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(dim=3) # 2. Compute the state for each intra-chunk # (right term of low-rank factorization of off-diagonal blocks; B terms) decay_states = torch.exp(A_cumsum[:, :, :, -1:] - A_cumsum) B_decay = B * decay_states.permute(0, -2, -1, 1)[..., None] states = (B_decay[..., None, :] * hidden_states[..., None]).sum(dim=2) # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries # (middle term of factorization of off-diag blocks; A terms) if use_precomputed_states: previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device) else: previous_states = torch.zeros_like(states[:, :1]) states = torch.cat([previous_states, states], dim=1) decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0)))) decay_chunk = decay_chunk.transpose(1, 3) new_states = (decay_chunk[..., None, None] * states[:, :, None, ...]).sum(dim=1) states, ssm_state = new_states[:, :-1], new_states[:, -1] # 4. Compute state -> output conversion per chunk # (left term of low-rank factorization of off-diagonal blocks; C terms) state_decay_out = torch.exp(A_cumsum) C_times_states = (C[..., None, :] * states[:, :, None, ...]) state_decay_out_permuted = state_decay_out.permute(0, 2, 3, 1) Y_off = (C_times_states.sum(-1) * state_decay_out_permuted[..., None]) # Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks) y = Y_diag + Y_off # [bsz, -1, self.chunk_size, num_heads, head_dim] -> [bsz, (padded) seq_len, num_heads, head_dim] y = y.reshape(batch_size, -1, self.num_heads, self.head_dim) y = y + D_residual # Cutting off padded chunks if pad_size > 0: y = y[:, :seq_len, :, :] y = y.reshape(batch_size, seq_len, -1) # Init cache if ssm_state is not None and cache_params is not None: cache_params.ssm_states[self.layer_idx].copy_(ssm_state) scan_output = self.norm(y, gate) # end ssd naive # 4. Final linear projection contextualized_states = self.out_proj(scan_output.to(dtype)) # [batch, seq_len, hidden_size] return contextualized_states # fmt: on def forward( self, hidden_states, cache_params: HybridMambaAttentionDynamicCache | None = None, cache_position: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, seq_idx: torch.IntTensor | None = None, **kwargs, ): if is_fast_path_available and "cuda" in self.in_proj.weight.device.type and not is_torchdynamo_compiling(): return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask, seq_idx) if seq_idx is not None: raise NotImplementedError( "`seq_idx` support requires fast path support. Please install `mamba_ssm` and `causal_conv1d`" ) dtype = hidden_states.dtype if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1: # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66 hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype) return self.torch_forward(hidden_states, cache_params, cache_position, attention_mask) class BambaMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj @use_kernel_forward_from_hub("RMSNorm") class BambaRMSNorm(nn.Module): def __init__(self, hidden_size, eps: float = 1e-6) -> None: """ BambaRMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" class BambaDecoderLayer(GradientCheckpointingLayer): def __init__(self, config: BambaConfig, layer_idx: int, layer_type: str = "mamba"): super().__init__() num_experts = 1 ffn_layer_class = BambaMLP if num_experts == 1 else None self.feed_forward = ffn_layer_class(config) self.input_layernorm = BambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.pre_ff_layernorm = BambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.layer_type = layer_type if layer_type == "mamba": self.mamba = BambaMixer(config=config, layer_idx=layer_idx) elif layer_type == "attention": self.self_attn = BambaAttention(config, layer_idx) else: raise ValueError("Invalid layer_type") def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: HybridMambaAttentionDynamicCache | None = None, output_attentions: bool | None = False, use_cache: bool | None = False, cache_position: torch.LongTensor | None = None, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, **kwargs: Unpack[BambaFlashAttentionKwargs], ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch, sequence_length)` where padding elements are indicated by 0. past_key_values (`HybridMambaAttentionDynamicCache`, *optional*): cached past key and value projection states output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. 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`). cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*): Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`, with `head_dim` being the embedding dimension of each attention head. kwargs (`dict`, *optional*): Arbitrary kwargs. Can be used to provide `BambaFlashAttentionKwargs` for padding-free training and/or improve torch.compile performance. """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # this is a hybrid decoder layer if self.layer_type == "mamba": hidden_states = self.mamba( hidden_states=hidden_states, cache_params=past_key_values, cache_position=cache_position, attention_mask=attention_mask, **kwargs, ) self_attn_weights = None elif self.layer_type == "attention": hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) # residual connection after attention hidden_states = residual + hidden_states # feed-forward residual = hidden_states hidden_states = self.pre_ff_layernorm(hidden_states) hidden_states = self.feed_forward(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) return outputs @auto_docstring class BambaPreTrainedModel(PreTrainedModel): config: BambaConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["BambaDecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn = True _supports_sdpa = True # Note: only supports HybridMambaAttentionDynamicCache _is_stateful = True @torch.no_grad() def _init_weights(self, module): super()._init_weights(module) if isinstance(module, BambaMixer): init.ones_(module.dt_bias) init.copy_(module.A_log, torch.log(torch.arange(1, module.num_heads + 1))) init.ones_(module.D) @auto_docstring class BambaModel(BambaPreTrainedModel): def __init__(self, config: BambaConfig): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) decoder_layers = [] for i in range(config.num_hidden_layers): decoder_layers.append(BambaDecoderLayer(config, layer_idx=i, layer_type=config.layers_block_type[i])) self.layers = nn.ModuleList(decoder_layers) self._attn_implementation = config._attn_implementation self.final_layernorm = BambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = BambaRotaryEmbedding(config=config) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() @can_return_tuple @auto_docstring def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: HybridMambaAttentionDynamicCache | None = None, inputs_embeds: torch.FloatTensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[BambaFlashAttentionKwargs], ) -> BaseModelOutputWithPast: 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 ) use_cache = use_cache if use_cache is not None else self.config.use_cache if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) hidden_states = inputs_embeds if use_cache and past_key_values is None: logger.warning_once( "Bamba requires an initialized `HybridMambaAttentionDynamicCache` to return a cache. None was " "provided, so no cache will be returned." ) if cache_position is None: cache_position = torch.arange(hidden_states.shape[1], device=hidden_states.device) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = create_causal_mask( config=self.config, inputs_embeds=inputs_embeds, attention_mask=attention_mask, cache_position=cache_position, past_key_values=past_key_values, position_ids=position_ids, ) mamba_mask = self._update_mamba_mask(attention_mask, cache_position) position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for decoder_layer in self.layers: # Depending on the layer type we opt for 2D base attention mask (Mamba) or 4D causal mask (Attention) layer_mask = mamba_mask if decoder_layer.layer_type == "mamba" else causal_mask if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = decoder_layer( hidden_states, attention_mask=layer_mask, position_ids=position_ids, past_key_values=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = layer_outputs[0] if output_attentions: if layer_outputs[1] is not None: # append attentions only of attention layers. Mamba layers return `None` as the attention weights all_self_attns += (layer_outputs[1],) hidden_states = self.final_layernorm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) if past_key_values and not past_key_values.has_previous_state: past_key_values.has_previous_state = True next_cache = None if not use_cache else past_key_values return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) def _update_mamba_mask(self, attention_mask, cache_position): """ No need for zeroing states when 1. Cached forward 2. Attending to all inputs """ mamba_mask = attention_mask if cache_position[0] > 0 or (attention_mask is not None and torch.all(attention_mask == 1)): mamba_mask = None return mamba_mask @auto_docstring class BambaForCausalLM(BambaPreTrainedModel, GenerationMixin): _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} _tp_plan = {"lm_head": "colwise_gather_output"} _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} def __init__(self, config): super().__init__(config) self.model = BambaModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.z_loss_coefficient = config.z_loss_coefficient # Initialize weights and apply final processing self.post_init() @can_return_tuple @auto_docstring def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: HybridMambaAttentionDynamicCache | None = None, inputs_embeds: torch.FloatTensor | None = None, labels: torch.LongTensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, cache_position: torch.LongTensor | None = None, logits_to_keep: int | torch.Tensor = 0, **kwargs, ) -> CausalLMOutputWithPast: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): 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]`. Example: ```python >>> from transformers import AutoTokenizer, BambaForCausalLM >>> model = BambaForCausalLM.from_pretrained("...") >>> tokenizer = AutoTokenizer.from_pretrained("...") >>> 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] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```""" 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 ) # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs: BaseModelOutputWithPast = self.model( input_ids=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, cache_position=cache_position, **kwargs, ) hidden_states = outputs.last_hidden_state # Only compute necessary logits, and do not upcast them to float if we are not computing the loss slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None if labels is not None: loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) if self.z_loss_coefficient > 0: # Type-match loss, but avoid upcasting large logits tensor until after it's been reduced on dim -1 z_loss = logits.logsumexp(dim=-1).to(dtype=loss.dtype).pow(2).mean() loss = loss + self.z_loss_coefficient * z_loss return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, position_ids=None, use_cache=True, is_first_iteration=False, **kwargs, ): # Overwritten -- has a unique cache type, `HybridMambaAttentionDynamicCache` if past_key_values is None: past_key_values = HybridMambaAttentionDynamicCache( self.config, input_ids.shape[0], self.dtype, device=self.device ) kwargs["logits_to_keep"] = self.config.num_logits_to_keep model_inputs = super().prepare_inputs_for_generation( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds, cache_position=cache_position, position_ids=position_ids, use_cache=use_cache, is_first_iteration=is_first_iteration, **kwargs, ) return model_inputs __all__ = ["BambaModel", "BambaForCausalLM", "BambaPreTrainedModel"]