# coding=utf-8 # Copyright 2024 AI21 Labs Ltd. 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. """PyTorch Jamba model.""" import math from typing import Any, Optional, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache, DynamicLayer from ...generation import GenerationMixin from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available from ...modeling_layers import ( GenericForSequenceClassification, GradientCheckpointingLayer, ) from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast from ...modeling_utils import PreTrainedModel from ...processing_utils import Unpack from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging from ...utils.import_utils import is_causal_conv1d_available, is_mamba_ssm_available from .configuration_jamba import JambaConfig if is_flash_attn_available(): from ...modeling_flash_attention_utils import _flash_attention_forward if is_mamba_ssm_available(): from mamba_ssm.ops.selective_scan_interface import mamba_inner_fn, selective_scan_fn from mamba_ssm.ops.triton.selective_state_update import selective_state_update else: selective_state_update, selective_scan_fn, mamba_inner_fn = None, None, 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 is_fast_path_available = all( (selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn) ) logger = logging.get_logger(__name__) # Copied from transformers.models.mixtral.modeling_mixtral.load_balancing_loss_func with gate->router def load_balancing_loss_func( router_logits: Union[torch.Tensor, tuple[torch.Tensor], None], num_experts: Optional[int] = None, top_k=2, attention_mask: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, int]: r""" Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between experts is too unbalanced. Args: router_logits: Logits from the `router`, should be a tuple of model.config.num_hidden_layers tensors of shape [batch_size X sequence_length, num_experts]. num_experts: Number of experts top_k: The number of experts to route per-token, can be also interpreted as the `top-k` routing parameter. attention_mask (`torch.Tensor`, *optional*): The attention_mask used in forward function shape [batch_size X sequence_length] if not None. Returns: The auxiliary loss. """ if router_logits is None or not isinstance(router_logits, tuple): return 0 if isinstance(router_logits, tuple): compute_device = router_logits[0].device concatenated_router_logits = torch.cat( [layer_router.to(compute_device) for layer_router in router_logits], dim=0 ) routing_weights = torch.nn.functional.softmax(concatenated_router_logits, dim=-1) _, selected_experts = torch.topk(routing_weights, top_k, dim=-1) expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts) if attention_mask is None: # Compute the percentage of tokens routed to each experts tokens_per_expert = torch.mean(expert_mask.float(), dim=0) # Compute the average probability of routing to these experts router_prob_per_expert = torch.mean(routing_weights, dim=0) else: batch_size, sequence_length = attention_mask.shape num_hidden_layers = concatenated_router_logits.shape[0] // (batch_size * sequence_length) # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask expert_attention_mask = ( attention_mask[None, :, :, None, None] .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts)) .reshape(-1, top_k, num_experts) .to(compute_device) ) # Compute the percentage of tokens routed to each experts tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum( expert_attention_mask, dim=0 ) # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert router_per_expert_attention_mask = ( attention_mask[None, :, :, None] .expand((num_hidden_layers, batch_size, sequence_length, num_experts)) .reshape(-1, num_experts) .to(compute_device) ) # Compute the average probability of routing to these experts router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum( router_per_expert_attention_mask, dim=0 ) overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0)) return overall_loss * num_experts # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Jamba class JambaRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ JambaRMSNorm 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}" # Copied from transformers.models.llama.modeling_llama.repeat_kv 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) class HybridMambaAttentionDynamicCache(Cache): """ 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)`. """ key_cache = None value_cache = None is_compileable = False def __init__(self, config, batch_size, dtype=torch.float16, device=None): super().__init__(layer_classes=DynamicLayer) self.dtype = dtype self.layers_block_type = config.layers_block_type self.has_previous_state = False # only used by mamba intermediate_size = config.mamba_expand * config.hidden_size ssm_state_size = config.mamba_d_state conv_kernel_size = config.mamba_d_conv 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, intermediate_size, conv_kernel_size, device=device, dtype=dtype) ] self.ssm_states += [ torch.zeros(batch_size, intermediate_size, 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 update( self, key_states: torch.Tensor, value_states: torch.Tensor, layer_idx: int, cache_kwargs: Optional[dict[str, Any]] = 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.""" 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_seq_length(self, layer_idx: Optional[int] = 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: return 0 return self.key_cache[layer_idx].shape[-2] def to_legacy_cache(self) -> tuple[tuple[torch.Tensor], tuple[torch.Tensor]]: raise NotImplementedError("HybridMambaAttentionDynamicCache does not have a legacy cache equivalent.") @classmethod def from_legacy_cache(cls, past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None) -> "DynamicCache": raise NotImplementedError("HybridMambaAttentionDynamicCache does not have a legacy cache equivalent.") # Adapted from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->Jamba class JambaAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer and "Generating Long Sequences with Sparse Transformers". """ def __init__(self, config: JambaConfig, layer_idx: Optional[int] = None): super().__init__() self.config = config self.layer_idx = layer_idx if layer_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.is_causal = True self.attention_dropout = config.attention_dropout if (self.head_dim * self.num_heads) != self.hidden_size: raise ValueError( f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" f" and `num_heads`: {self.num_heads})." ) self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) 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: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) if past_key_value is not None: key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attention_mask is not None: # no matter the length, we just slice it causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value # Adapted from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->Jamba class JambaFlashAttention2(JambaAttention): """ Jamba flash attention module. This module inherits from `JambaAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask() 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: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, **kwargs, ): bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim # therefore we just need to keep the original shape query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) if past_key_value is not None: key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) dropout_rate = 0.0 if not self.training else self.attention_dropout # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in float16 just to be sure everything works as expected. input_dtype = query_states.dtype device_type = query_states.device.type if query_states.device.type != "mps" else "cpu" if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = ( torch.get_autocast_dtype(device_type) if hasattr(torch, "get_autocast_dtype") else torch.get_autocast_gpu_dtype() ) # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) # Reashape to the expected shape for Flash Attention key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, sliding_window=getattr(self.config, "sliding_window", None), is_causal=self.is_causal, use_top_left_mask=self._flash_attn_uses_top_left_mask, ) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value # Adapted from transformers.models.mistral.modeling_mistral.MistralSdpaAttention with Mistral->Jamba class JambaSdpaAttention(JambaAttention): """ Jamba attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `JambaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. """ # Adapted from JambaAttention.forward 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: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: if output_attentions: # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. logger.warning_once( "JambaModel is using JambaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) return super().forward( 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, ) bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) if past_key_value is not None: key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) causal_mask = attention_mask if attention_mask is not None: causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, # Reference: https://github.com/pytorch/pytorch/issues/112577. if query_states.device.type == "cuda" and attention_mask is not None: query_states = query_states.contiguous() key_states = key_states.contiguous() value_states = value_states.contiguous() # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. is_causal = True if self.is_causal and causal_mask is None and q_len > 1 else False attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=causal_mask, dropout_p=self.attention_dropout if self.training else 0.0, is_causal=is_causal, ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.view(bsz, q_len, self.hidden_size) attn_output = self.o_proj(attn_output) return attn_output, None, past_key_value JAMBA_ATTENTION_CLASSES = { "eager": JambaAttention, "flash_attention_2": JambaFlashAttention2, "sdpa": JambaSdpaAttention, } # Adapted from transformers.models.mamba.modeling_mamba.MambaMixer class JambaMambaMixer(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) """ def __init__(self, config: JambaConfig, layer_idx): super().__init__() self.config = config self.layer_idx = layer_idx 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 = config.mamba_expand * config.hidden_size self.time_step_rank = config.mamba_dt_rank self.use_conv_bias = config.mamba_conv_bias self.use_bias = config.mamba_proj_bias self.conv1d = nn.Conv1d( in_channels=self.intermediate_size, out_channels=self.intermediate_size, bias=self.use_conv_bias, kernel_size=self.conv_kernel_size, groups=self.intermediate_size, padding=self.conv_kernel_size - 1, ) self.activation = config.hidden_act self.act = ACT2FN[config.hidden_act] self.use_fast_kernels = config.use_mamba_kernels # projection of the input hidden states self.in_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=self.use_bias) # selective projection used to make dt, B and C input dependent self.x_proj = nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False) # time step projection (discretization) self.dt_proj = nn.Linear(self.time_step_rank, self.intermediate_size, bias=True) # 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.ssm_state_size + 1)[None, :] A = A.expand(self.intermediate_size, -1).contiguous() self.A_log = nn.Parameter(torch.log(A)) self.D = nn.Parameter(torch.ones(self.intermediate_size)) self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias) self.dt_layernorm = JambaRMSNorm(self.time_step_rank, eps=config.rms_norm_eps) self.b_layernorm = JambaRMSNorm(self.ssm_state_size, eps=config.rms_norm_eps) self.c_layernorm = JambaRMSNorm(self.ssm_state_size, eps=config.rms_norm_eps) if not is_fast_path_available: logger.warning_once( "The fast path is not available because on of `(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)`" " is None. To install follow https://github.com/state-spaces/mamba/#installation and" " https://github.com/Dao-AILab/causal-conv1d. If you want to use the naive implementation, set `use_mamba_kernels=False` in the model config" ) def cuda_kernels_forward( self, hidden_states: torch.Tensor, cache_params: HybridMambaAttentionDynamicCache = None, attention_mask: Optional[torch.LongTensor] = None, ): batch_size, seq_len, _ = hidden_states.shape 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 ) # 1. Gated MLP's linear projection projected_states = self.in_proj(hidden_states).transpose(1, 2) # We can't use `mamba_inner_fn` even if in training and without cache params because we have the # inner layernorms which isn't supported by this fused kernel hidden_states, gate = projected_states.chunk(2, dim=1) if attention_mask is not None: hidden_states = hidden_states * attention_mask.unsqueeze(1) # 2. Convolution sequence transformation conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2)) if use_precomputed_states: hidden_states = causal_conv1d_update( hidden_states.squeeze(-1), cache_params.conv_states[self.layer_idx], conv_weights, self.conv1d.bias, self.activation, ) hidden_states = hidden_states.unsqueeze(-1) else: if cache_params is not None: conv_states = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0)) cache_params.conv_states[self.layer_idx].copy_(conv_states) hidden_states = causal_conv1d_fn(hidden_states, conv_weights, self.conv1d.bias, activation=self.activation) if attention_mask is not None: hidden_states = hidden_states * attention_mask.unsqueeze(1) # 3. State Space Model sequence transformation # 3.a. input varying initialization of time_step, B and C ssm_parameters = self.x_proj(hidden_states.transpose(1, 2)) time_step, B, C = torch.split( ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1 ) time_step = self.dt_layernorm(time_step) B = self.b_layernorm(B) C = self.c_layernorm(C) # Here we need to apply dt_proj without the bias, as the bias is added in the selective scan kernel. # This is a hack to apply dt_proj while still using the forward pass of `torch.nn.Linear`, which is needed # in order to make quantization work. Quantization code replaces `torch.nn.Linear` layers with quantized # linear layers, and requires to call the forward pass directly. # Quantized model can't work with the original code: # ```discrete_time_step = self.dt_proj.weight @ time_step.transpose(1, 2)``` time_proj_bias = self.dt_proj.bias.data with torch.no_grad(): self.dt_proj.bias.data = torch.zeros_like(self.dt_proj.bias.data) discrete_time_step = self.dt_proj(time_step).transpose(1, 2) with torch.no_grad(): self.dt_proj.bias.data = time_proj_bias A = -torch.exp(self.A_log.float()) # 3.c perform the recurrence y ← SSM(A, B, C)(x) time_proj_bias = time_proj_bias.float() if time_proj_bias is not None else None if use_precomputed_states: scan_outputs = selective_state_update( cache_params.ssm_states[self.layer_idx], hidden_states[..., 0], discrete_time_step[..., 0], A, B[:, 0], C[:, 0], self.D, gate[..., 0], time_proj_bias, dt_softplus=True, ).unsqueeze(-1) else: scan_outputs, ssm_state = selective_scan_fn( hidden_states, discrete_time_step, A, B.transpose(1, 2), C.transpose(1, 2), self.D.float(), gate, time_proj_bias, delta_softplus=True, return_last_state=True, ) if ssm_state is not None and cache_params is not None: cache_params.ssm_states[self.layer_idx].copy_(ssm_state) # 4. Final linear projection contextualized_states = self.out_proj(scan_outputs.transpose(1, 2)) return contextualized_states # fmt: off def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCache = None, attention_mask: Optional[torch.LongTensor] = None): batch_size, seq_len, _ = input_states.shape dtype = input_states.dtype # 1. Gated MLP's linear projection projected_states = self.in_proj(input_states).transpose(1, 2) # [batch, 2 * intermediate_size, seq_len] hidden_states, gate = projected_states.chunk(2, dim=1) if attention_mask is not None: hidden_states = hidden_states * attention_mask.unsqueeze(1) use_cache = isinstance(cache_params, HybridMambaAttentionDynamicCache) # 2. Convolution sequence transformation if use_cache and cache_params.ssm_states[self.layer_idx].shape[0] == batch_size: if self.training: # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass ssm_state = cache_params.ssm_states[self.layer_idx].clone() else: ssm_state = cache_params.ssm_states[self.layer_idx] ssm_state = ssm_state.to(hidden_states.device) if cache_params.has_previous_state and seq_len == 1 and \ cache_params.conv_states[self.layer_idx].shape[0] == batch_size: conv_state = cache_params.conv_states[self.layer_idx] # [batch, intermediate_size, conv_kernel_size] conv_state = torch.roll(conv_state, shifts=-1, dims=-1) conv_state[:, :, -1] = hidden_states[:, :, 0] cache_params.conv_states[self.layer_idx] = conv_state hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1) if self.use_conv_bias: hidden_states += self.conv1d.bias hidden_states = self.act(hidden_states).to(dtype).unsqueeze(-1) # [batch, intermediate_size, 1] : decoding else: conv_state = nn.functional.pad( hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0) ) cache_params.conv_states[self.layer_idx] = conv_state hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) # [batch, intermediate_size, seq_len] else: ssm_state = torch.zeros( (batch_size, self.intermediate_size, self.ssm_state_size), device=hidden_states.device, dtype=dtype ) hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) # [batch, intermediate_size, seq_len] if attention_mask is not None: hidden_states = hidden_states * attention_mask.unsqueeze(1) # 3. State Space Model sequence transformation # 3.a. Selection: [batch, seq_len, self.time_step_rank + self.ssm_state_size * 2] ssm_parameters = self.x_proj(hidden_states.transpose(1, 2)) time_step, B, C = torch.split( ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1 ) time_step = self.dt_layernorm(time_step) B = self.b_layernorm(B) C = self.c_layernorm(C) discrete_time_step = self.dt_proj(time_step) # [batch, seq_len, intermediate_size] discrete_time_step = nn.functional.softplus(discrete_time_step).transpose(1, 2) # [batch, intermediate_size, seq_len] # 3.b. Discretization: B and C to [batch, seq_len, intermediate_size, ssm_state_size] (SRAM) A = -torch.exp(self.A_log.float()) # [intermediate_size, ssm_state_size] discrete_A = torch.exp(A[None, :, None, :] * discrete_time_step[:, :, :, None]) # [batch, intermediate_size, seq_len, ssm_state_size] discrete_B = discrete_time_step[:, :, :, None] * B[:, None, :, :].float() # [batch, intermediate_size, seq_len, ssm_state_size] deltaB_u = discrete_B * hidden_states[:, :, :, None].float() # 3.c perform the recurrence y ← SSM(A, B, C)(x) scan_outputs = [] for i in range(seq_len): ssm_state = discrete_A[:, :, i, :] * ssm_state + deltaB_u[:, :, i, :] # [batch, intermediate_size, ssm_state] scan_output = torch.matmul(ssm_state.to(dtype), C[:, i, :].unsqueeze(-1)) # [batch, intermediate_size, 1] scan_outputs.append(scan_output[:, :, 0]) scan_output = torch.stack(scan_outputs, dim=-1) # [batch, intermediate_size, seq_len] scan_output = scan_output + (hidden_states * self.D[None, :, None]) scan_output = (scan_output * self.act(gate)) if use_cache: cache_params.ssm_states[self.layer_idx] = ssm_state # 4. Final linear projection contextualized_states = self.out_proj(scan_output.transpose(1, 2)) # [batch, seq_len, hidden_size] return contextualized_states # fmt: on def forward( self, hidden_states, cache_params: HybridMambaAttentionDynamicCache = None, attention_mask: Optional[torch.LongTensor] = None, ): if self.use_fast_kernels: if not is_fast_path_available or "cuda" not in self.x_proj.weight.device.type: raise ValueError( "Fast Mamba kernels are not available. Make sure to they are installed and that the mamba module is on a CUDA device" ) return self.cuda_kernels_forward(hidden_states, cache_params, attention_mask) return self.slow_forward(hidden_states, cache_params, attention_mask) # Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Jamba class JambaMLP(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=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) 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 # Adapted from transformers.models.mixtral.modeling_mixtral.MixtralSparseMoeBlock with Mistral->Jamba class JambaSparseMoeBlock(nn.Module): """ This implementation is strictly equivalent to standard MoE with full capacity (no dropped tokens). It's faster since it formulates MoE operations in terms of block-sparse operations to accommodate imbalanced assignments of tokens to experts, whereas standard MoE either (1) drop tokens at the cost of reduced performance or (2) set capacity factor to number of experts and thus waste computation and memory on padding. """ def __init__(self, config: JambaConfig): super().__init__() self.hidden_dim = config.hidden_size self.ffn_dim = config.intermediate_size self.num_experts = config.num_experts self.top_k = config.num_experts_per_tok self.router = nn.Linear(self.hidden_dim, self.num_experts, bias=False) self.experts = nn.ModuleList([JambaMLP(config) for _ in range(self.num_experts)]) def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: """ """ batch_size, sequence_length, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) # router_logits: (batch * sequence_length, n_experts) router_logits = self.router(hidden_states) routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float) routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1) # we cast back to the input dtype routing_weights = routing_weights.to(hidden_states.dtype) final_hidden_states = torch.zeros( (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device ) # One hot encode the selected experts to create an expert mask # this will be used to easily index which expert is going to be sollicitated expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0) # Loop over all available experts in the model and perform the computation on each expert for expert_idx in range(self.num_experts): expert_layer = self.experts[expert_idx] idx, top_x = torch.where(expert_mask[expert_idx]) if top_x.shape[0] == 0: continue # Index the correct hidden states and compute the expert hidden state for # the current expert. We need to make sure to multiply the output hidden # states by `routing_weights` on the corresponding tokens (top-1 and top-2) current_state = hidden_states[None, top_x].reshape(-1, hidden_dim) current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None] # However `index_add_` only support torch tensors for indexing so we'll use # the `top_x` tensor here. final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype)) final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim) return final_hidden_states, router_logits class JambaAttentionDecoderLayer(GradientCheckpointingLayer): def __init__(self, config: JambaConfig, layer_idx: int): super().__init__() num_experts = config.layers_num_experts[layer_idx] self.self_attn = JAMBA_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) ffn_layer_class = JambaSparseMoeBlock if num_experts > 1 else JambaMLP self.feed_forward = ffn_layer_class(config) self.input_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.pre_ff_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) 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, output_router_logits: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, ) -> 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. output_router_logits (`bool`, *optional*): Whether or not to return the logits of all the routers. They are useful for computing the router loss, and should not be returned during inference. 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. """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states, self_attn_weights, present_key_value = 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, ) # residual connection after attention hidden_states = residual + hidden_states # feed-forward (experts/MLP) residual = hidden_states hidden_states = self.pre_ff_layernorm(hidden_states) ff_outputs = self.feed_forward(hidden_states) if isinstance(ff_outputs, tuple): hidden_states, router_logits = ff_outputs else: hidden_states, router_logits = ff_outputs, None hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) if output_router_logits: outputs += (router_logits,) return outputs class JambaMambaDecoderLayer(GradientCheckpointingLayer): def __init__(self, config: JambaConfig, layer_idx: int): super().__init__() num_experts = config.layers_num_experts[layer_idx] self.mamba = JambaMambaMixer(config=config, layer_idx=layer_idx) ffn_layer_class = JambaSparseMoeBlock if num_experts > 1 else JambaMLP self.feed_forward = ffn_layer_class(config) self.input_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.pre_ff_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) 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, output_router_logits: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, ) -> 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. output_router_logits (`bool`, *optional*): Whether or not to return the logits of all the routers. They are useful for computing the router loss, and should not be returned during inference. 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. """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states = self.mamba( hidden_states=hidden_states, cache_params=past_key_value, attention_mask=attention_mask, ) self_attn_weights = None # residual connection after mamba hidden_states = residual + hidden_states # feed-forward (experts/MLP) residual = hidden_states hidden_states = self.pre_ff_layernorm(hidden_states) ff_outputs = self.feed_forward(hidden_states) if isinstance(ff_outputs, tuple): hidden_states, router_logits = ff_outputs else: hidden_states, router_logits = ff_outputs, None hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (past_key_value,) if output_router_logits: outputs += (router_logits,) return outputs @auto_docstring class JambaPreTrainedModel(PreTrainedModel): config: JambaConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["JambaAttentionDecoderLayer", "JambaMambaDecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn = True _supports_sdpa = 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, 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, JambaRMSNorm): module.weight.data.fill_(1.0) elif isinstance(module, JambaMambaMixer): A = torch.arange(1, module.ssm_state_size + 1)[None, :] A = A.expand(module.intermediate_size, -1).contiguous() module.A_log.data.copy_(torch.log(A)) module.D.data.fill_(1.0) ALL_DECODER_LAYER_TYPES = {"attention": JambaAttentionDecoderLayer, "mamba": JambaMambaDecoderLayer} # Adapted from transformers.models.mistral.modeling_mistral.MistralModel with MISTRAL->JAMBA, Mistral->Jamba @auto_docstring class JambaModel(JambaPreTrainedModel): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`JambaDecoderLayer`] Args: config: JambaConfig """ def __init__(self, config: JambaConfig): 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): layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[i]] decoder_layers.append(layer_class(config, layer_idx=i)) self.layers = nn.ModuleList(decoder_layers) self._attn_implementation = config._attn_implementation self.final_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() @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, output_router_logits: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[TransformersKwargs], ) -> MoeModelOutputWithPast: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_router_logits = ( output_router_logits if output_router_logits is not None else self.config.output_router_logits ) 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( "Jamba 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) mamba_mask = self._update_mamba_mask(attention_mask, cache_position) all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_router_logits = () if output_router_logits 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 isinstance(decoder_layer, JambaMambaDecoderLayer) 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_value=past_key_values, output_attentions=output_attentions, output_router_logits=output_router_logits, use_cache=use_cache, cache_position=cache_position, ) 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],) if output_router_logits: if layer_outputs[-1] is not None: # append router logits only of expert layers. Regular MLP layers return `None` as the router logits all_router_logits += (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 MoeModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, router_logits=all_router_logits, ) def _update_causal_mask(self, attention_mask, input_tensor, cache_position): 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 dtype, device = input_tensor.dtype, input_tensor.device min_dtype = torch.finfo(dtype).min sequence_length = input_tensor.shape[1] target_length = cache_position[-1] + 1 causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit if attention_mask.dim() == 2: mask_length = attention_mask.shape[-1] padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0) causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu", "npu"] ): # 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 causal_mask = AttentionMaskConverter._unmask_unattended(causal_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 # Adapted from transformers.models.mixtral.modeling_mixtral.MixtralForCausalLM with MIXTRAL->JAMBA, Mixtral->Jamba class JambaForCausalLM(JambaPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config: JambaConfig): super().__init__(config) self.model = JambaModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.router_aux_loss_coef = config.router_aux_loss_coef self.num_experts = config.num_experts self.num_experts_per_tok = config.num_experts_per_tok # Initialize weights and apply final processing self.post_init() 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, output_router_logits: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs: Unpack[TransformersKwargs], ) -> MoeCausalLMOutputWithPast: 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, JambaForCausalLM >>> model = JambaForCausalLM.from_pretrained("ai21labs/Jamba-v0.1") >>> tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1") >>> 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_router_logits = ( output_router_logits if output_router_logits is not None else self.config.output_router_logits ) 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: MoeModelOutputWithPast = 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, output_router_logits=output_router_logits, cache_position=cache_position, ) hidden_states = outputs.last_hidden_state 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, labels, self.vocab_size, **kwargs) aux_loss = None if output_router_logits: aux_loss = load_balancing_loss_func( outputs.router_logits, self.num_experts, self.num_experts_per_tok, attention_mask, ) if labels is not None: loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device return MoeCausalLMOutputWithPast( loss=loss, aux_loss=aux_loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, router_logits=outputs.router_logits, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, output_router_logits=False, 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, "output_router_logits": output_router_logits, "logits_to_keep": self.config.num_logits_to_keep, "cache_position": cache_position, } ) return model_inputs class JambaForSequenceClassification(GenericForSequenceClassification, JambaPreTrainedModel): ... __all__ = ["JambaForCausalLM", "JambaForSequenceClassification", "JambaModel", "JambaPreTrainedModel"]