# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # 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. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # 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 functools import partial from typing import Callable, Optional, Tuple, TypedDict, Union import torch from torch import nn import transformers.models.jamba.modeling_jamba as modeling_jamba from transformers.activations import ACT2FN from ...cache_utils import Cache # we need __iter__ and __len__ of pkv from ...generation import GenerationMixin from ...integrations import use_kernel_forward_from_hub from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_flash_attention_utils import FlashAttentionKwargs 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 auto_docstring, can_return_tuple, logging from ...utils.import_utils import is_causal_conv1d_available, is_mamba_2_ssm_available from .configuration_bamba import BambaConfig if is_mamba_2_ssm_available(): from mamba_ssm.ops.triton.selective_state_update import selective_state_update from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined else: selective_state_update = None if is_causal_conv1d_available(): from causal_conv1d import causal_conv1d_fn, causal_conv1d_update else: causal_conv1d_update, causal_conv1d_fn = None, None 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. Attributes: 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 # Adapted from transformers.models.jamba.modeling_jamba.HybridMambaAttentionDynamicCache for the v2 mixer class HybridMambaAttentionDynamicCache(modeling_jamba.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)`. """ def __init__(self, config: BambaConfig, batch_size, dtype=torch.float16, device=None): super().__init__(config, batch_size, dtype, device) 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)] class BambaRotaryEmbedding(nn.Module): def __init__(self, config: BambaConfig, device=None): super().__init__() # BC: "rope_type" was originally "type" if hasattr(config, "rope_scaling") and config.rope_scaling is not None: self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) else: self.rope_type = "default" self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq @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 torch.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: Optional[torch.Tensor], scaling: float, dropout: float = 0.0, **kwargs, ): 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: causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_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, position_ids=None, 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. position_ids (`torch.Tensor`, *optional*): Deprecated and unused. 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 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], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[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_value 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_value.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False): logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to " 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] 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 is_fast_path_available = all((selective_state_update, causal_conv1d_fn, causal_conv1d_update)) 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 """ 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 HybridCache 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 # FIXME: self.time_step_limit = (0.0, float("inf")) self.time_step_min = 0.001 self.time_step_max = 0.1 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.A_log._no_weight_decay = True self.norm = BambaRMSNormGated(self.intermediate_size, eps=self.layer_norm_epsilon) self.D = nn.Parameter(torch.ones(self.num_heads)) self.D._no_weight_decay = True self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias) if not is_fast_path_available: logger.warning_once( "The fast path is not available because on 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: Optional[HybridMambaAttentionDynamicCache] = None, cache_position: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, seq_idx: Optional[torch.IntTensor] = 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: Optional[HybridMambaAttentionDynamicCache] = None, cache_position: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = 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) cache_params.has_previous_state = True 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: Optional[HybridMambaAttentionDynamicCache] = None, cache_position: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, seq_idx: Optional[torch.IntTensor] = None, **kwargs, ): if is_fast_path_available and "cuda" in self.in_proj.weight.device.type: 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=1e-6): """ BambaRMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): 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(nn.Module): 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: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[HybridMambaAttentionDynamicCache] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC **kwargs: Unpack[BambaFlashAttentionKwargs], ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ 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_value (`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_value, 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_value=past_key_value, 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_class = BambaConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["BambaDecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_sdpa = True _supports_cache_class = True # Note: only supports HybridMambaAttentionDynamicCache _is_stateful = True def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, (nn.Linear, nn.Conv1d)): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, (BambaRMSNormGated, BambaRMSNorm)): module.weight.data.fill_(1.0) 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_() elif isinstance(module, BambaMixer): module.dt_bias.data.fill_(1.0) module.A_log.data = torch.log(torch.arange(1, module.num_heads + 1)) module.D.data.fill_(1.0) @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() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value @can_return_tuple @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[HybridMambaAttentionDynamicCache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = 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 = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) mamba_mask = self._update_mamba_mask(attention_mask, cache_position) # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, 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,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( partial(decoder_layer.__call__, **kwargs), hidden_states, layer_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, position_embeddings, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=layer_mask, position_ids=position_ids, past_key_value=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_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: HybridMambaAttentionDynamicCache, output_attentions: bool, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if self.config._attn_implementation == "sdpa" and not output_attentions: if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training, ): return None dtype = input_tensor.dtype sequence_length = input_tensor.shape[1] target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, cache_position=cache_position, batch_size=input_tensor.shape[0], ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu", "npu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 min_dtype = torch.finfo(dtype).min causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, cache_position: torch.Tensor, batch_size: int, **kwargs, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit mask_length = attention_mask.shape[-1] padding_attention_mask = (attention_mask[:, None, None, :] == attention_mask[:, None, :, None])[ :, :, -sequence_length:, : ].to(dtype) padding_mask = causal_mask[:, :, :, :mask_length] + padding_attention_mask padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask 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"] _tp_plan = {"lm_head": "colwise_rep"} _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) # 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 def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model = decoder def get_decoder(self): return self.model @can_return_tuple @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[HybridMambaAttentionDynamicCache] = 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, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[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) 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, **kwargs, ): # Overwritten -- has a unique cache type, `HybridMambaAttentionDynamicCache` empty_past_kv = past_key_values is None # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens # Exception 1: when passing input_embeds, input_ids may be missing entries # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case. # (we can't check exception 3 while compiling) if not empty_past_kv: if ( inputs_embeds is not None # Exception 1 or cache_position[-1] >= input_ids.shape[1] # Exception 3 ): input_ids = input_ids[:, -cache_position.shape[0] :] elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2) input_ids = input_ids[:, cache_position] else: past_key_values = HybridMambaAttentionDynamicCache( self.config, input_ids.shape[0], self.dtype, device=self.device ) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if not empty_past_kv: position_ids = position_ids[:, -input_ids.shape[1] :] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and empty_past_kv: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids.contiguous()} # `contiguous()` needed for compilation use cases model_inputs.update( { "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": use_cache, "attention_mask": attention_mask, "logits_to_keep": self.config.num_logits_to_keep, "cache_position": cache_position, } ) return model_inputs __all__ = ["BambaModel", "BambaForCausalLM", "BambaPreTrainedModel"]