diff --git "a/modeling_hymba.py" "b/modeling_hymba.py" new file mode 100644--- /dev/null +++ "b/modeling_hymba.py" @@ -0,0 +1,2611 @@ +import inspect +import math +import warnings +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional, Tuple, Union +import numpy as np + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache +from transformers.modeling_attn_mask_utils import ( + _prepare_4d_causal_attention_mask, + _prepare_4d_causal_attention_mask_for_sdpa, +) + +from transformers.modeling_outputs import ( + MoeCausalLMOutputWithPast, + MoeModelOutputWithPast, + SequenceClassifierOutputWithPast, +) + +from transformers.modeling_utils import PreTrainedModel +from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_13 +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) + +from transformers.utils.import_utils import is_torch_fx_available +from .configuration_hymba import HymbaConfig +from torch.utils.checkpoint import checkpoint + + +try: + from flash_attn import flash_attn_func, flash_attn_varlen_func + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + + _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) + + from einops import rearrange, repeat, reduce, pack, unpack + from einops.layers.torch import Rearrange +except ImportError: + pass + + +if is_torch_fx_available(): + if not is_torch_greater_or_equal_than_1_13: + import torch.fx + + _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) + + +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 +from causal_conv1d import causal_conv1d_fn, causal_conv1d_update + + +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__) + +_CONFIG_FOR_DOC = "HymbaConfig" + + +def pad_at_dim(t, pad: Tuple[int, int], dim = -1, value = 0.): + if pad == (0, 0): + return t + + dims_from_right = (- dim - 1) if dim < 0 else (t.ndim - dim - 1) + zeros = ((0, 0) * dims_from_right) + return F.pad(t, (*zeros, *pad), value = value) + +# Adapted from transformers.models.mixtral.modeling_mixtral.load_balancing_loss_func +def load_balancing_loss_func( + gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2, attention_mask: Optional[torch.Tensor] = None +) -> float: + r""" + Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. + + See Switch Transformer (https://arxiv.org/abs/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: + gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]): + Logits from the `router`, should be a tuple of model.config.num_hidden_layers tensors of + shape [batch_size X sequence_length, num_experts]. + attention_mask (`torch.Tensor`, None): + The attention_mask used in forward function + shape [batch_size X sequence_length] if not None. + num_experts (`int`, *optional*): + Number of experts + + Returns: + The auxiliary loss. + """ + if gate_logits is None or not isinstance(gate_logits, tuple): + return 0 + + if isinstance(gate_logits, tuple): + compute_device = gate_logits[0].device + concatenated_gate_logits = torch.cat( + [layer_gate.to(compute_device) for layer_gate in gate_logits if layer_gate.shape[1] > 1], dim=0 + ) + + routing_weights = torch.nn.functional.softmax(concatenated_gate_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_gate_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._get_unpad_data +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +class HymbaRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + HymbaRMSNorm 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) + + +class PerheadHymbaRMSNorm(nn.Module): + def __init__(self, hidden_size, num_heads, eps=1e-6): + """ + For per-head kq normalization + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(1, num_heads, 1, hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + # assert 1==0, f"hiddens_states shape: {hidden_states.shape}" # [bsz, num_heads, seq_len, head_dim] + assert hidden_states.shape[1] == self.weight.shape[1], f"hidden_state: {hidden_states.shape}, weight: {self.weight.shape}" + assert hidden_states.shape[3] == self.weight.shape[3], f"hidden_state: {hidden_states.shape}, weight: {self.weight.shape}" + 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) + + 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) + + +class HymbaOnlyNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + HymbaRMSNorm 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 hidden_states.to(input_dtype) + + +class LlamaRotaryEmbedding(nn.Module): + def __init__(self, config, dim, base=10000, device=None, scaling_factor=1.0): + super().__init__() + self.scaling_factor = scaling_factor + self.dim = dim + self.base = base + self.config = config + + self.rope_type = config.rope_type + + self.factor = 2 + + max_position_embeddings = self.config.max_position_embeddings + + if config.rope_type is None or config.rope_type == "default": + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) + self.max_seq_len_cached = max_position_embeddings + + elif config.rope_type == 'ntk': + assert self.config.orig_max_position_embeddings is not None + orig_max_position_embeddings = self.config.orig_max_position_embeddings + + base = base * ((self.factor * max_position_embeddings / orig_max_position_embeddings) - (self.factor - 1)) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) + + self.max_seq_len_cached = orig_max_position_embeddings + + elif config.rope_type == 'dynamic_ntk': + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) + self.original_inv_freq = inv_freq + self.max_seq_len_cached = self.config.orig_max_position_embeddings + + else: + raise ValueError(f"Not support rope_type: {config.rope_type}") + + self.register_buffer("inv_freq", inv_freq, persistent=False) + + + def _dynamic_frequency_update(self, position_ids, device): + """ + dynamic RoPE layers should recompute `inv_freq` in the following situations: + 1 - growing beyond the cached sequence length (allow scaling) + 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) + """ + + seq_len = torch.max(position_ids) + 1 + if seq_len > self.max_seq_len_cached: # growth + base = self.base * ((self.factor * seq_len / self.config.orig_max_position_embeddings) - (self.factor - 1)) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.max_seq_len_cached = seq_len + + if seq_len < self.config.orig_max_position_embeddings and self.max_seq_len_cached > self.config.orig_max_position_embeddings: # reset + self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) + self.max_seq_len_cached = self.config.orig_max_position_embeddings + + + + @torch.no_grad() + def forward(self, x, position_ids): + if self.rope_type == 'dynamic_ntk': + self._dynamic_frequency_update(position_ids, device=x.device) + + # x: [bs, num_attention_heads, seq_len, head_size] + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + # Force float32 since bfloat16 loses precision on long contexts + # See https://github.com/huggingface/transformers/pull/29285 + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + 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 apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + 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) + if q is not None: + q_embed = (q * cos) + (rotate_half(q) * sin) + + else: + q_embed = None + + if k is not None: + k_embed = (k * cos) + (rotate_half(k) * sin) + else: + k_embed = None + return q_embed, k_embed + +# 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(DynamicCache): + """ + 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, batch_size, dtype=torch.float16, device=None, layer_type=None): + 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.layer_type = layer_type + + for i in range(config.num_hidden_layers): + if layer_type is None: + has_mamba_state = True + else: + has_mamba_state = self.layer_type[i] == 'h' or self.layer_type[i] == 'm' + + if has_mamba_state: + if hasattr(config, 'conv_dim'): + conv_dim = config.conv_dim[i] + else: + conv_dim = intermediate_size + self.conv_states += [ + torch.zeros(batch_size, conv_dim, 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.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)] + + self.mamba_past_length = [0 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 + + if self.layer_type[layer_idx] == 'm': + return self.mamba_past_length[layer_idx] + + if self.key_cache[layer_idx].shape[-1] == 0: + 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.") + + +@dataclass +class MambaCacheParams: + seqlen_offset: int = 0 + conv_states: Dict[int, torch.Tensor] = field(default_factory=dict) + ssm_states: Dict[int, torch.Tensor] = field(default_factory=dict) + + + +# Adapted from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->Hymba +class HymbaAttention(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: HymbaConfig, layer_idx: Optional[int] = None, reuse_kv=False, output_hidden_size=None, attn_only_wo_proj=False): + 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.hidden_size = config.attn_hidden_size if config.attn_hidden_size > 0 else config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + + self.attn_only_wo_proj = attn_only_wo_proj + + self.kq_head_dim = config.kq_head_dim if config.kq_head_dim > 0 else self.head_dim + self.v_head_dim = config.v_head_dim if config.v_head_dim > 0 else self.head_dim + + 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})." + ) + + if not self.attn_only_wo_proj: + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.kq_head_dim, bias=False) + + self.reuse_kv = reuse_kv + + if not self.attn_only_wo_proj and not self.reuse_kv: + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.kq_head_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.v_head_dim, bias=False) + + if output_hidden_size is None: + output_hidden_size = self.hidden_size + + if not self.attn_only_wo_proj: + self.o_proj = nn.Linear(self.num_heads * self.v_head_dim, output_hidden_size, bias=False) + + if self.config.kq_norm == "rms": + self.k_norm = HymbaRMSNorm(self.kq_head_dim) + self.q_norm = HymbaRMSNorm(self.kq_head_dim) + elif self.config.kq_norm == "perhead-rms": + self.k_norm = PerheadHymbaRMSNorm(self.kq_head_dim, self.num_key_value_heads) + self.q_norm = PerheadHymbaRMSNorm(self.kq_head_dim, self.num_heads) + elif self.config.kq_norm == "none": + self.k_norm = None + self.q_norm = None + else: + raise NotImplementedError(f"Unknown kq_norm: {self.config.kq_norm}") + + if self.config.rope: + self._init_rope() + + + def _init_rope(self): + self.rotary_emb = LlamaRotaryEmbedding( + config=self.config, + dim=self.kq_head_dim, + base=self.rope_theta, + device=torch.device("cuda"), + ) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + kv_last_layer = None, + # kv_proj_last_layer = None, + use_swa=False, + query_states = None, + key_states=None, + value_states=None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + raise NotImplementedError("HymbaAttention is an abstract class. Use one of the subclasses.") + + +# Adapted from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->Hymba +class HymbaFlashAttention2(HymbaAttention): + """ + Hymba flash attention module. This module inherits from `HymbaAttention` 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. + """ + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + kv_last_layer=None, + # kv_proj_last_layer = None, + use_swa=False, + query_states = None, + key_states=None, + value_states=None, + **kwargs, + ): + + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + + # overwrite attention_mask with padding_mask + attention_mask = kwargs.pop("padding_mask") + + if self.attn_only_wo_proj: + assert query_states is not None + bsz, q_len, _ = query_states.size() + else: + bsz, q_len, _ = hidden_states.size() + + if not self.attn_only_wo_proj: + query_states = self.q_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + if self.q_norm is not None: + query_states = self.q_norm(query_states) + + if self.config.rope: + if self.attn_only_wo_proj: + cos, sin = self.rotary_emb(query_states, position_ids) + else: + cos, sin = self.rotary_emb(hidden_states, position_ids) + query_states, _ = apply_rotary_pos_emb(query_states, None, cos, sin) + + + if self.reuse_kv: + assert kv_last_layer is not None + key_states, value_states = kv_last_layer # (batch, num_heads, slen, head_dim) + + else: + if not self.attn_only_wo_proj: + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.kq_head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.v_head_dim).transpose(1, 2) + + if self.k_norm is not None: + key_states = self.k_norm(key_states) + + if self.config.rope: + _, key_states = apply_rotary_pos_emb(None, key_states, cos, sin) + + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None and not self.reuse_kv: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + + use_sliding_windows = ( + _flash_supports_window_size + and getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > (self.config.sliding_window + self.config.num_memory_tokens if self.config.num_memory_tokens > 0 else self.config.sliding_window) + and use_swa + ) + + if not _flash_supports_window_size: + logger.warning_once( + "The current flash attention version does not support sliding window attention, for a more memory efficient implementation" + " make sure to upgrade flash-attn library." + ) + + swa_processed_flag = False + if past_key_value is not None and use_cache and not self.reuse_kv: + kv_layer_idx = self.layer_idx + + cache_has_contents = past_key_value.get_seq_length(kv_layer_idx) > 0 + + if ( + getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > (self.config.sliding_window + self.config.num_memory_tokens if self.config.num_memory_tokens > 0 else self.config.sliding_window) + and cache_has_contents + and use_swa + ): + slicing_tokens = 1 - self.config.sliding_window + + past_key = past_key_value[kv_layer_idx][0] + past_value = past_key_value[kv_layer_idx][1] + + if self.config.num_memory_tokens > 0: + # num_fetched_memory_tokens = min(kv_seq_len - self.config.sliding_window, self.config.num_memory_tokens) + num_fetched_memory_tokens = self.config.num_memory_tokens + + past_key = torch.cat([past_key[:, :, :num_fetched_memory_tokens, :], past_key[:, :, slicing_tokens:, :]], dim=-2).contiguous() + past_value = torch.cat([past_value[:, :, :num_fetched_memory_tokens, :], past_value[:, :, slicing_tokens:, :]], dim=-2).contiguous() + + else: + past_key = past_key[:, :, slicing_tokens:, :].contiguous() + past_value = past_value[:, :, slicing_tokens:, :].contiguous() + + past_key_value.key_cache[kv_layer_idx] = past_key + past_key_value.value_cache[kv_layer_idx] = past_value + + if attention_mask is not None: + attention_mask = attention_mask[:, slicing_tokens:] + attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1) + + swa_processed_flag = True + + key_states, value_states = past_key_value.update(key_states, value_states, kv_layer_idx) + + # repeat k/v heads if n_kv_heads < n_heads + key_states_no_repeat = key_states + value_states_no_repeat = value_states + + 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 + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = 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 + query_states = query_states.transpose(1, 2) # (batch, slen, num_heads, head_dim) + key_states = key_states.transpose(1, 2) # (batch, slen, num_heads, head_dim) + value_states = value_states.transpose(1, 2) # (batch, slen, num_heads, head_dim) + + attn_output = self._flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + use_sliding_windows=use_sliding_windows and not swa_processed_flag, + ) + + v_dim = value_states.shape[-2] * value_states.shape[-1] + attn_output = attn_output.reshape(bsz, q_len, v_dim).contiguous() + + if self.attn_only_wo_proj: + return attn_output, (key_states_no_repeat, value_states_no_repeat) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value, (key_states_no_repeat, value_states_no_repeat) + + def _flash_attention_forward( + self, + query_states, + key_states, + value_states, + attention_mask, + query_length, + dropout=0.0, + softmax_scale=None, + use_sliding_windows=False, + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`int`, *optional*): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + use_sliding_windows (`bool`, *optional*): + Whether to activate sliding window attention. + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + causal = self.is_causal and query_length != 1 + + # Contains at least one padding token in the sequence + if attention_mask is not None: + if value_states.shape[-1] == query_states.shape[-1] * 2: + value_states1 = value_states[...,:query_states.shape[-1]] + + batch_size = query_states.shape[0] + + query_states1, key_states1, value_states1, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states1, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if not use_sliding_windows: + attn_output_unpad1 = flash_attn_varlen_func( + query_states1, + key_states1, + value_states1, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output_unpad1 = flash_attn_varlen_func( + query_states1, + key_states1, + value_states1, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + attn_output1 = pad_input(attn_output_unpad1, indices_q, batch_size, query_length) + + value_states2 = value_states[...,query_states.shape[-1]:] + + query_states2, key_states2, value_states2, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states2, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if not use_sliding_windows: + attn_output_unpad2 = flash_attn_varlen_func( + query_states2, + key_states2, + value_states2, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output_unpad2 = flash_attn_varlen_func( + query_states2, + key_states2, + value_states2, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + attn_output2 = pad_input(attn_output_unpad2, indices_q, batch_size, query_length) + + attn_output = torch.cat([attn_output1, attn_output2], dim=-1) + + else: + batch_size = query_states.shape[0] + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if not use_sliding_windows: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + if value_states.shape[-1] == query_states.shape[-1] * 2: + if not use_sliding_windows: + attn_output1 = flash_attn_func( + query_states, + key_states, + value_states[...,:query_states.shape[-1]], + dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + + attn_output2 = flash_attn_func( + query_states, + key_states, + value_states[...,query_states.shape[-1]:], + dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + + attn_output = torch.cat([attn_output1, attn_output2], dim=-1) + + else: + attn_output1 = flash_attn_func( + query_states, + key_states, + value_states[...,:query_states.shape[-1]], + dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + attn_output2 = flash_attn_func( + query_states, + key_states, + value_states[...,query_states.shape[-1]:], + dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + attn_output = torch.cat([attn_output1, attn_output2], dim=-1) + + else: + if not use_sliding_windows: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + return attn_output + + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape + + # On the first iteration we need to properly re-create the padding mask + # by slicing it on the proper place + if kv_seq_len != attention_mask.shape[-1]: + attention_mask_num_tokens = attention_mask.shape[-1] + attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :] + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + + key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + + +# Adapted from transformers.models.mistral.modeling_mistral.MistralSdpaAttention with Mistral->Hymba +class HymbaSdpaAttention(HymbaAttention): + """ + Hymba attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `HymbaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from HymbaAttention.forward + def forward( + self, + hidden_states: torch.Tensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + kv_last_layer=None, + # kv_proj_last_layer = None, + use_swa=False, + query_states = None, + key_states=None, + value_states=None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + + if output_attentions: + 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, + ) + + if self.attn_only_wo_proj: + assert query_states is not None + bsz, q_len, _ = query_states.size() + else: + bsz, q_len, _ = hidden_states.size() + + if not self.attn_only_wo_proj: + query_states = self.q_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.kq_head_dim).transpose(1, 2).contiguous() + + if self.q_norm is not None: + query_states = self.q_norm(query_states) + + if self.config.rope: + if self.attn_only_wo_proj: + cos, sin = self.rotary_emb(query_states, position_ids) + else: + cos, sin = self.rotary_emb(hidden_states, position_ids) + query_states, _ = apply_rotary_pos_emb(query_states, None, cos, sin) + + if self.reuse_kv: + assert kv_last_layer is not None + key_states, value_states = kv_last_layer # (batch, num_heads, slen, head_dim) + + else: + if not self.attn_only_wo_proj: + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.kq_head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.v_head_dim).transpose(1, 2) + + if self.k_norm is not None: + key_states = self.k_norm(key_states) + + if self.config.rope: + _, key_states = apply_rotary_pos_emb(None, key_states, cos, sin) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None and not self.reuse_kv and use_cache: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx) + + key_states_no_repeat = key_states + value_states_no_repeat = value_states + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + # 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() + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + # 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=self.is_causal and attention_mask is None and q_len > 1, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.v_head_dim * self.num_heads) + + if self.attn_only_wo_proj: + return attn_output, (key_states_no_repeat, value_states_no_repeat) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value, (key_states_no_repeat, value_states_no_repeat) + + + + +# Adapted from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->Hymba +class HymbaFlexAttention(HymbaFlashAttention2): + """ + Hymba flash attention module. This module inherits from `HymbaAttention` 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. + """ + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + assert self.config.num_memory_tokens > 0 + # assert self.config.sliding_window is not None + + from torch.nn.attention.flex_attention import flex_attention, create_block_mask, and_masks, or_masks + from functools import partial + + self.create_block_mask = create_block_mask + + def sliding_window(b, h, q_idx, kv_idx): + return q_idx - kv_idx <= self.config.sliding_window + + def causal_mask(b, h, q_idx, kv_idx): + return q_idx >= kv_idx + + if self.config.sliding_window is not None and self.config.global_attn_idx is not None and self.layer_idx not in self.config.global_attn_idx: + attn_mask = and_masks(causal_mask, sliding_window) + else: + attn_mask = causal_mask + + if self.config.memory_tokens_interspersed_every > 0: + # !If see errors, note that deprecated n_ctx, using seq_length or max_position_embeddings instead + num_memory_band = self.config.seq_length // self.config.memory_tokens_interspersed_every + qk_length = self.config.seq_length + num_memory_band * self.config.num_memory_tokens + num_tokens_per_band = qk_length // num_memory_band + + for i in range(num_memory_band): + left_mask = lambda b, h, q_idx, kv_idx, i=i: kv_idx > i * num_tokens_per_band + right_mask = lambda b, h, q_idx, kv_idx, i=i: kv_idx < i * num_tokens_per_band + self.config.num_memory_tokens + + band_mask = and_masks(left_mask, right_mask) + + if i == 0: + prefix_mask_interspersed = band_mask + else: + prefix_mask_interspersed = or_masks(prefix_mask_interspersed, band_mask) + + register_mask = and_masks(causal_mask, prefix_mask_interspersed) + else: + def prefix_mask(b, h, q_idx, kv_idx): + return kv_idx < self.config.num_memory_tokens + + register_mask = and_masks(causal_mask, prefix_mask) + qk_length = self.config.seq_length + self.config.num_memory_tokens + + self.attn_mask = or_masks(attn_mask, register_mask) + + self.block_mask = create_block_mask(self.attn_mask, B=None, H=None, Q_LEN=qk_length, KV_LEN=qk_length, _compile=True) + + self.flex_attention = torch.compile(flex_attention) + + + def recompile_flexattn(self): + from torch.nn.attention.flex_attention import flex_attention + self.flex_attention = torch.compile(flex_attention) + + + def forward( + self, + hidden_states: torch.Tensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + kv_last_layer=None, + # kv_proj_last_layer = None, + use_swa=False, + query_states = None, + key_states=None, + value_states=None, + **kwargs, + ): + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + + attention_mask = kwargs.pop("padding_mask") + + if self.attn_only_wo_proj: + assert query_states is not None + bsz, q_len, _ = query_states.size() + else: + bsz, q_len, _ = hidden_states.size() + + if not self.attn_only_wo_proj: + query_states = self.q_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + if self.q_norm is not None: + query_states = self.q_norm(query_states) + + if self.config.rope: + if self.attn_only_wo_proj: + cos, sin = self.rotary_emb(query_states, position_ids) + else: + cos, sin = self.rotary_emb(hidden_states, position_ids) + query_states, _ = apply_rotary_pos_emb(query_states, None, cos, sin) + + if self.reuse_kv: + assert kv_last_layer is not None + key_states, value_states = kv_last_layer # (batch, num_heads, slen, head_dim) + + else: + if not self.attn_only_wo_proj: + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.kq_head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.v_head_dim).transpose(1, 2) + + if self.k_norm is not None: + key_states = self.k_norm(key_states) + + if self.config.rope: + # cos, sin = self.rotary_emb(hidden_states, position_ids) + _, key_states = apply_rotary_pos_emb(None, key_states, cos, sin) + + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None and not self.reuse_kv: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + + use_sliding_windows = ( + _flash_supports_window_size + and getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > (self.config.sliding_window + self.config.num_memory_tokens if self.config.num_memory_tokens > 0 else self.config.sliding_window) + and use_swa + ) + + if not _flash_supports_window_size: + logger.warning_once( + "The current flash attention version does not support sliding window attention, for a more memory efficient implementation" + " make sure to upgrade flash-attn library." + ) + + swa_processed_flag = False + if past_key_value is not None and use_cache and not self.reuse_kv: + kv_layer_idx = self.layer_idx + + cache_has_contents = past_key_value.get_seq_length(kv_layer_idx) > 0 + + if ( + getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > (self.config.sliding_window + self.config.num_memory_tokens if self.config.num_memory_tokens > 0 else self.config.sliding_window) + and cache_has_contents + and use_swa + ): + slicing_tokens = 1 - self.config.sliding_window + + past_key = past_key_value[kv_layer_idx][0] + past_value = past_key_value[kv_layer_idx][1] + + if self.config.num_memory_tokens > 0: + # num_fetched_memory_tokens = min(kv_seq_len - self.config.sliding_window, self.config.num_memory_tokens) + num_fetched_memory_tokens = self.config.num_memory_tokens + + past_key = torch.cat([past_key[:, :, :num_fetched_memory_tokens, :], past_key[:, :, slicing_tokens:, :]], dim=-2).contiguous() + past_value = torch.cat([past_value[:, :, :num_fetched_memory_tokens, :], past_value[:, :, slicing_tokens:, :]], dim=-2).contiguous() + + else: + past_key = past_key[:, :, slicing_tokens:, :].contiguous() + past_value = past_value[:, :, slicing_tokens:, :].contiguous() + + ### only keep sliding_window tokens in kv cache: Removed as this will impact the kv_seq_len calculation, resulting in errors for all swa cases + past_key_value.key_cache[kv_layer_idx] = past_key + past_key_value.value_cache[kv_layer_idx] = past_value + + if attention_mask is not None: + attention_mask = attention_mask[:, slicing_tokens:] + attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1) + + swa_processed_flag = True + + key_states, value_states = past_key_value.update(key_states, value_states, kv_layer_idx) + + # print(key_states.shape, value_states.shape) + else: + cache_has_contents = False + + + # repeat k/v heads if n_kv_heads < n_heads + key_states_no_repeat = key_states + value_states_no_repeat = value_states + + 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 + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = 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) + + + if past_key_value is not None and use_cache and (not use_swa or query_states.shape[-2] <= self.config.sliding_window): + query_states = query_states.transpose(1, 2) # (batch, slen, num_heads, head_dim) + key_states = key_states.transpose(1, 2) # (batch, slen, num_heads, head_dim) + value_states = value_states.transpose(1, 2) # (batch, slen, num_heads, head_dim) + + attn_output = self._flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + use_sliding_windows=use_sliding_windows and not swa_processed_flag, + ) + + v_dim = value_states.shape[-2] * value_states.shape[-1] + attn_output = attn_output.reshape(bsz, q_len, v_dim).contiguous() + + else: + if key_states.shape[-2] <= self.block_mask.shape[-2] - 128 or key_states.shape[-2] > self.block_mask.shape[-2]: + block_mask = self.create_block_mask(self.attn_mask, B=None, H=None, Q_LEN=key_states.shape[-2], KV_LEN=key_states.shape[-2]) # , _compile=True) + else: + block_mask = self.block_mask + + if value_states.shape[-1] == query_states.shape[-1] * 2: + attn_output1 = self.flex_attention(query_states, key_states, value_states[...,:query_states.shape[-1]], block_mask=block_mask) + attn_output2 = self.flex_attention(query_states, key_states, value_states[...,query_states.shape[-1]:], block_mask=block_mask) + + attn_output = torch.cat([attn_output1, attn_output2], dim=-1) + else: + attn_output = self.flex_attention(query_states, key_states, value_states, block_mask=block_mask) + + attn_output = attn_output.transpose(1, 2).contiguous() ## [batch_size, seq_length, num_head, v_head_dim] + + if hasattr(self, 'head_mask') and self.head_mask is not None: + head_mask = self.head_mask.to(attn_output) + head_mask = head_mask.view(1, 1, -1, 1) + attn_output = attn_output * head_mask + + attn_output = attn_output.reshape(bsz, q_len, self.v_head_dim * self.num_heads) + + if self.attn_only_wo_proj: + return attn_output, (key_states_no_repeat, value_states_no_repeat) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value, (key_states_no_repeat, value_states_no_repeat) + + def set_head_mask(self, mask): + self.head_mask = mask + + +JAMBA_ATTENTION_CLASSES = { + "eager": HymbaAttention, + "flash_attention_2": HymbaFlashAttention2, + "sdpa": HymbaSdpaAttention, ## the default attention + "flex": HymbaFlexAttention, +} + + +# Adapted from transformers.models.mamba.modeling_mamba.MambaMixer +class HymbaBlock(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: HymbaConfig, layer_idx, reuse_kv=None): + 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 = int(config.mamba_expand * config.hidden_size) + + self.reuse_kv = reuse_kv + + self.attn_hidden_size = config.hidden_size + self.num_attention_heads = config.num_attention_heads + self.num_key_value_heads = config.num_key_value_heads + + config.v_head_dim = self.intermediate_size // self.num_attention_heads + + self.k_hidden_size = int(self.num_key_value_heads/self.num_attention_heads * self.attn_hidden_size) + self.v_hidden_size = int(self.num_key_value_heads/self.num_attention_heads * self.attn_hidden_size * config.mamba_expand) + + self.self_attn = JAMBA_ATTENTION_CLASSES[config.attn_implementation](config, layer_idx, attn_only_wo_proj=True, reuse_kv=reuse_kv) + + self.time_step_rank = config.mamba_dt_rank + self.use_conv_bias = config.mamba_conv_bias + self.use_bias = config.mamba_proj_bias + + self.activation = config.hidden_act + self.act = ACT2FN[config.hidden_act] + self.apply_inner_layernorms = config.mamba_inner_layernorms + + self.use_fast_kernels = True # config.use_mamba_kernels + + if self.reuse_kv: + self.latent_dim = self.intermediate_size + self.attn_hidden_size ## mamba plus q + else: + self.latent_dim = self.intermediate_size + self.attn_hidden_size + self.k_hidden_size + self.v_hidden_size ## mamba plus qkv + + self.pre_avg_layernorm1 = HymbaRMSNorm(self.intermediate_size, eps=config.rms_norm_eps) + self.pre_avg_layernorm2 = HymbaRMSNorm(self.intermediate_size, eps=config.rms_norm_eps) + + self.in_proj = nn.Linear(self.hidden_size, self.latent_dim + self.intermediate_size, bias=self.use_bias) + self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias) + + num_ssm_param = 1 + + if not hasattr(config, 'conv_dim'): + config.conv_dim = {i:0 for i in range(config.num_hidden_layers)} + + 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 + ) + + config.conv_dim[self.layer_idx] = self.intermediate_size + + self.x_proj = nn.ModuleList([nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False) for _ in range(num_ssm_param)]) + self.dt_proj = nn.ModuleList([nn.Linear(self.time_step_rank, self.intermediate_size, bias=True) for _ in range(num_ssm_param)]) + + A = torch.arange(1, self.ssm_state_size + 1, dtype=torch.float32)[None, :] + A = A.expand(self.intermediate_size, -1).contiguous() + self.A_log = nn.ParameterList([nn.Parameter(torch.log(A)) for _ in range(num_ssm_param)]) + + self.D = nn.ParameterList([nn.Parameter(torch.ones(self.intermediate_size)) for _ in range(num_ssm_param)]) + + if self.apply_inner_layernorms: + self.dt_layernorm = HymbaRMSNorm(self.time_step_rank, eps=config.rms_norm_eps) + self.B_layernorm = HymbaRMSNorm(self.ssm_state_size, eps=config.rms_norm_eps) + self.C_layernorm = HymbaRMSNorm(self.ssm_state_size, eps=config.rms_norm_eps) + + else: + self.dt_layernorm = None + self.B_layernorm = None + self.C_layernorm = None + + 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 set_attn_mamba_mask(self, attn_branch_mask, mamba_branch_mask): + self.attn_branch_mask = attn_branch_mask + self.mamba_branch_mask = mamba_branch_mask + + + def _apply_layernorms(self, dt, B, C): + if self.dt_layernorm is not None: + dt = self.dt_layernorm(dt) + if self.B_layernorm is not None: + B = self.B_layernorm(B) + if self.C_layernorm is not None: + C = self.C_layernorm(C) + return dt, B, C + + def cuda_kernels_forward(self, hidden_states: torch.Tensor, cache_params: HybridMambaAttentionDynamicCache = None, attention_mask=None, position_ids=None, kv_last_layer=None, use_cache=False, use_swa=False): + projected_states = self.in_proj(hidden_states).transpose(1, 2) ## (bs, latent_dim, seq_len) + + if ( + self.training and cache_params is None and not self.apply_inner_layernorms + ): # Doesn't support outputting the states -> used for training + contextualized_states = mamba_inner_fn( + projected_states, + self.conv1d.weight, + self.conv1d.bias if self.use_conv_bias else None, + self.x_proj.weight, + self.dt_proj.weight, + self.out_proj.weight, + self.out_proj.bias.float() if self.use_bias else None, + -torch.exp(self.A_log.float()), + None, # input-dependent B + None, # input-dependent C + self.D.float(), + delta_bias=self.dt_proj.bias.float(), + delta_softplus=True, + ) + + else: + 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 + and use_cache + ) + + hidden_states, gate = projected_states.tensor_split((self.latent_dim,), dim=1) + + conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2)) + + if self.reuse_kv: + query_states, hidden_states = hidden_states.tensor_split((self.attn_hidden_size,), dim=1) + query_states = query_states.transpose(1,2) + else: + query_states, key_states, value_states, hidden_states = hidden_states.tensor_split((self.attn_hidden_size, self.attn_hidden_size + self.k_hidden_size, self.attn_hidden_size + self.k_hidden_size + self.v_hidden_size), dim=1) + + query_states = query_states.transpose(1,2) + key_states = key_states.transpose(1,2) + value_states = value_states.transpose(1,2) + + 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) + + cache_params.mamba_past_length[self.layer_idx] += seq_len + 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) + + cache_params.mamba_past_length[self.layer_idx] += seq_len + + hidden_states = causal_conv1d_fn( + hidden_states, conv_weights, self.conv1d.bias, activation=self.activation + ) + + if self.reuse_kv: + assert kv_last_layer is not None + attn_outputs, attn_key_value = self.self_attn(attention_mask=attention_mask, position_ids=position_ids, query_states=query_states, kv_last_layer=kv_last_layer, use_swa=use_swa, use_cache=use_cache, past_key_value=cache_params) + else: + attn_outputs, attn_key_value = self.self_attn(attention_mask=attention_mask, position_ids=position_ids, query_states=query_states, key_states=key_states, value_states=value_states, use_swa=use_swa, use_cache=use_cache, past_key_value=cache_params) + + ## Mamba head + index = 0 + ssm_parameters = self.x_proj[index](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, B, C = self._apply_layernorms(time_step, B, C) + + if hasattr(self.dt_proj[index], "base_layer"): + time_proj_bias = self.dt_proj[index].base_layer.bias + self.dt_proj[index].base_layer.bias = None + else: + time_proj_bias = self.dt_proj[index].bias + self.dt_proj[index].bias = None + discrete_time_step = self.dt_proj[index](time_step).transpose(1, 2) # [batch, intermediate_size, seq_len] + + if hasattr(self.dt_proj[index], "base_layer"): + self.dt_proj[index].base_layer.bias = time_proj_bias + else: + self.dt_proj[index].bias = time_proj_bias + + A = -torch.exp(self.A_log[index].float()) + + 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[index], + gate[..., 0], + time_proj_bias, + dt_softplus=True, + ).unsqueeze(-1) + else: + outputs = selective_scan_fn( + hidden_states, + discrete_time_step, + A, + B.transpose(1, 2), + C.transpose(1, 2), + self.D[index].float(), + z=gate, + delta_bias=time_proj_bias, + delta_softplus=True, + return_last_state=True, + ) + + if len(outputs) == 3: + scan_outputs, ssm_state, _ = outputs + else: + scan_outputs, ssm_state = outputs + + if ssm_state is not None and cache_params is not None: + cache_params.ssm_states[self.layer_idx].copy_(ssm_state) + + scan_outputs = scan_outputs.transpose(1, 2) + + hidden_states = (self.pre_avg_layernorm1(attn_outputs) + self.pre_avg_layernorm2(scan_outputs)) / 2 + contextualized_states = self.out_proj(hidden_states) + + return contextualized_states, attn_key_value + + + def mixer_forward(self, hidden_states, cache_params: HybridMambaAttentionDynamicCache = None, attention_mask=None, position_ids=None, kv_last_layer=None, use_cache=False, use_swa=False): + if self.use_fast_kernels: + if not is_fast_path_available or "cuda" not in self.x_proj[0].weight.device.type: + # 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=attention_mask, position_ids=position_ids, kv_last_layer=kv_last_layer, use_cache=use_cache, use_swa=use_swa) + else: + raise ValueError("Support Mamba kernel only") + + + def forward( + self, + hidden_states: torch.Tensor, + past_key_value: Optional[HybridMambaAttentionDynamicCache] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]: + + res, attn_key_value = self.mixer_forward(hidden_states, cache_params=past_key_value, attention_mask=kwargs['attention_mask'], kv_last_layer=kwargs['kv_last_layer'], position_ids=kwargs['position_ids'], use_cache=kwargs['use_cache'], use_swa=kwargs['use_swa']) + + return res, attn_key_value, past_key_value + + + +class HymbaMLP(nn.Module): + def __init__(self, config: HymbaConfig): + super().__init__() + # self.config = config + self.act_fn_name = config.mlp_hidden_act + self.act_fn = ACT2FN[self.act_fn_name] + self.ffn_dim = config.intermediate_size + self.hidden_dim = config.hidden_size + + if self.act_fn_name == "silu": + self.gate_proj = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False) + self.down_proj = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False) + self.up_proj = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False) + + + def forward(self, x): + if self.act_fn_name == "silu": + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + elif self.act_fn_name == "relu2": + return self.down_proj(self.act_fn(self.up_proj(x))) + else: + raise NotImplementedError(f"No such hidden_act: {self.act_fn_name}") + + +# Adapted from transformers.models.mixtral.modeling_mixtral.MixtralSparseMoeBlock with Mistral->Hymba +class HymbaSparseMoeBlock(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 accomodate 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: HymbaConfig, num_experts: int, num_experts_per_tok: int): + super().__init__() + self.hidden_dim = config.hidden_size + self.ffn_dim = config.intermediate_size + + # these values are decided on runtime depending on the layer index + self.num_experts = num_experts + self.top_k = num_experts_per_tok + + if num_experts > 1: + # expert routing + self.router = nn.Linear(self.hidden_dim, self.num_experts, bias=False) + else: + self.router = None + + self.experts = nn.ModuleList([HymbaMLP(config) for _ in range(self.num_experts)]) + + def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ """ + if len(hidden_states.shape) == 3: + batch_size, sequence_length, hidden_dim = hidden_states.shape + bs_times_seq_len = batch_size * sequence_length + elif len(hidden_states.shape) == 2: + assert self.num_experts == 1 + bs_times_seq_len, hidden_dim = hidden_states.shape + else: + batch_size, sequence_length, _, hidden_dim = hidden_states.shape + bs_times_seq_len = batch_size * sequence_length + + if self.num_experts == 1: + # in this case we have a single MLP block and don't need to do any routing + final_hidden_states = self.experts[0](hidden_states) + router_logits = torch.ones( + (bs_times_seq_len, 1), + device=hidden_states.device, + dtype=hidden_states.dtype, + requires_grad=hidden_states.requires_grad, + ) + return final_hidden_states, router_logits + + # in this case we have multiple experts and need to do routing + 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 + + # in torch it is faster to index using lists than torch tensors + top_x_list = top_x.tolist() + idx_list = idx.tolist() + + # 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_list].reshape(-1, hidden_dim) + current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, 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 HymbaDecoderLayer(nn.Module): + def __init__(self, config: HymbaConfig, num_experts: int, layer_idx: int, reuse_kv: bool = False): + super().__init__() + + self.config = config + self.layer_idx = layer_idx + self.reuse_kv = reuse_kv + + self.mamba = HymbaBlock(config=config, layer_idx=layer_idx, reuse_kv=reuse_kv) + + self.input_layernorm = HymbaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.intermediate_size = config.intermediate_size + if self.intermediate_size > 0: + num_experts_per_tok = config.num_experts_per_tok if num_experts > 1 else 1 + + self.moe = HymbaSparseMoeBlock(config, num_experts=num_experts, num_experts_per_tok=num_experts_per_tok) + + self.pre_moe_layernorm = HymbaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + attention_mask_raw: 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, + kv_last_layer = None, + use_swa=False, + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + """ + 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 (`Tuple(torch.FloatTensor)`, *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`). + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + hidden_states, attn_key_value, present_key_value = self.mamba( + hidden_states=hidden_states, + past_key_value=past_key_value, + attention_mask=attention_mask, + position_ids=position_ids, + kv_last_layer=kv_last_layer, + use_cache=use_cache, + use_swa=use_swa + ) + + bs, seqlen, _ = hidden_states.shape + past_seqlen = self._get_past_seqlen(past_key_value, seqlen) + num_attention_heads = self.mamba.config.num_attention_heads + self_attn_weights = torch.empty(bs, num_attention_heads, seqlen, past_seqlen, device="meta") + + # residual connection after mamba + hidden_states = residual + hidden_states + + if self.intermediate_size > 0: + residual = hidden_states + hidden_states = self.pre_moe_layernorm(hidden_states) + hidden_states, router_logits = self.moe(hidden_states) + 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,) + + outputs += (attn_key_value,) + + return outputs + + def _get_past_seqlen(self, past_key_value, seqlen): + if past_key_value is None: + return seqlen + past_seqlen = past_key_value.get_seq_length() + + if past_seqlen == 0: + return seqlen + + return past_seqlen + + + +class HymbaPreTrainedModel(PreTrainedModel): + config_class = HymbaConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["HymbaDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = 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_() + + @staticmethod + def _convert_to_standard_cache( + past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]: + """ + Standardizes the format of the cache so as to match most implementations, i.e. have the seqlen as the third dim + also for mamba layers + """ + attn_layer_index = [k.shape == v.shape for k, v in past_key_value].index(True) + seqlen = past_key_value[attn_layer_index][0].shape[2] + standard_past_key_value = () + for k, v in past_key_value: + if k.shape != v.shape: + # mamba layer + # expand doesn't use more memory, so it's fine to do it here + standard_past_key_value += ((k.expand(-1, -1, seqlen, -1), v.expand(-1, -1, seqlen, -1)),) + else: + standard_past_key_value += ((k, v),) + return standard_past_key_value + + @staticmethod + def _convert_to_hymba_cache( + past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]: + """ + Converts the cache to the format expected by Hymba, i.e. dummy seqlen dimesion with size 1 for mamba layers + """ + hymba_past_key_value = () + for k, v in past_key_value: + if k.shape != v.shape: + # mamba layer + hymba_past_key_value += ((k[:, :, :1, :], v[:, :, :1, :]),) + else: + hymba_past_key_value += ((k, v),) + return hymba_past_key_value + + + +HYMBA_INPUTS_DOCSTRING = r""" + Args: To be added later. Please refer to the forward function. +""" + + +# Adapted from transformers.models.mistral.modeling_mistral.MistralModel with MISTRAL->JAMBA, Mistral->Hymba +class HymbaModel(HymbaPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HymbaDecoderLayer`] + + Args: + config: HymbaConfig + """ + + def __init__(self, config: HymbaConfig): + super().__init__(config) + config.attn_implementation = config.attn_implementation_new + config._attn_implementation = config.attn_implementation_new + + self.config = 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) + + self.inter_layer_kv_reuse = config.kv_reuse_every_i_layer > 0 or config.kv_reuse_group is not None + self.kv_reuse_group = config.kv_reuse_group + self.kv_reuse_every_i_layer = config.kv_reuse_every_i_layer + + decoder_layers = [] + + if self.kv_reuse_group is not None: + self.kv_reuse_group = [{'producer': group[0], 'consumer': group[1:]} for group in self.kv_reuse_group] + + layer_type = [] + for i in range(config.num_hidden_layers): + if self.inter_layer_kv_reuse: + if self.kv_reuse_group is not None: + reuse_kv = False + for group_id, item in enumerate(self.kv_reuse_group): + if i in item['consumer']: + reuse_kv = True + + else: + if i % config.kv_reuse_every_i_layer == 0: + reuse_kv = False + else: + reuse_kv = True + else: + reuse_kv = False + + layer_type.append('h') + decoder_layer = HymbaDecoderLayer(config, num_experts=1, layer_idx=i, reuse_kv=reuse_kv) + + decoder_layers.append(decoder_layer) + + config.layer_type = layer_type + + if config.sliding_window is not None: + self.sliding_window = config.sliding_window + self.global_attn_idx = config.global_attn_idx + else: + self.sliding_window = None + self.global_attn_idx = None + + self._attn_layer_index = [] + self._hymba_layer_index = [isinstance(layer, HymbaDecoderLayer) for layer in decoder_layers].index(True) + + self.layers = nn.ModuleList(decoder_layers) + + self._attn_implementation = config.attn_implementation + self.final_layernorm = HymbaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + if self.config.num_memory_tokens > 0: + self.memory_tokens = nn.Parameter(torch.randn(self.config.num_memory_tokens, self.config.hidden_size)) + self.gradient_checkpointing = False + + self.post_init() + + # Ignore copy + @add_start_docstrings_to_model_forward(HYMBA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[List[torch.FloatTensor], 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, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, 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 + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + past_key_values_length = 0 + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + if use_cache: + use_legacy_cache = False + # past_key_values_length = past_key_values.get_usable_length(seq_length, self._attn_layer_index) + if past_key_values is not None: + past_key_values_length = past_key_values.get_usable_length(seq_length, 0) + else: + use_cache = False + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + if self.config.num_memory_tokens > 0 and past_key_values is not None and past_key_values.get_seq_length() == 0: + position_ids = position_ids.view(-1, seq_length + self.config.num_memory_tokens).long() + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if self.config.num_memory_tokens > 0 and (past_key_values is None or past_key_values.get_seq_length() == 0): + ori_b, ori_n = inputs_embeds.shape[0], inputs_embeds.shape[1] + + if self.config.memory_tokens_interspersed_every > 0: + mem_every = self.config.memory_tokens_interspersed_every + next_seq_len = math.ceil(ori_n / mem_every) * mem_every + + # print(f"before padding: {inputs_embeds.shape}") + inputs_embeds = pad_at_dim(inputs_embeds, (0, next_seq_len - ori_n), dim = -2, value = 0.) + # print(f"after padding: {inputs_embeds.shape}") + inputs_embeds = rearrange(inputs_embeds, 'b (n m) d -> (b n) m d', m = mem_every) # m is the segment length + + mem = repeat(self.memory_tokens, 'n d -> b n d', b = inputs_embeds.shape[0]) # prepend the memory to every segment of m by repeating the memory tokens + inputs_embeds, mem_packed_shape = pack((mem, inputs_embeds), 'b * d') + + if self.config.memory_tokens_interspersed_every > 0: + inputs_embeds = rearrange(inputs_embeds, '(b n) m d -> b (n m) d', b = ori_b) + + if position_ids is not None and position_ids.shape[1] != inputs_embeds.shape[1]: + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0) + + attention_mask_raw = attention_mask + + if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache: + is_padding_right = attention_mask[:, -1].sum().item() != batch_size + if is_padding_right: + raise ValueError( + "You are attempting to perform batched generation with padding_side='right'" + " this may lead to unexpected behaviour for Flash Attention version of Hymba. Make sure to " + " call `tokenizer.padding_side = 'left'` before tokenizing the input. " + ) + + if self._attn_implementation == "flash_attention_2" or self._attn_implementation == "flex": + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + attention_mask_swa = attention_mask + + elif self._attn_implementation == "sdpa" and not output_attentions: + attention_mask_input = attention_mask + + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + ) + + if self.sliding_window is not None: + attention_mask_swa = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask_input, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + sliding_window=self.sliding_window + ) + + else: + + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + ) + + + if self.sliding_window is not None: + attention_mask_swa = _prepare_4d_causal_attention_mask( + attention_mask_input, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + sliding_window=self.sliding_window + ) + + hidden_states = inputs_embeds + + # decoder layers + 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 + next_decoder_cache = None + + kv_last_layer = None + + shared_kv_cache_dict = {} + + for i, decoder_layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.inter_layer_kv_reuse and self.kv_reuse_group is not None: + no_reuse_flag = True + for group_id, item in enumerate(self.kv_reuse_group): + if i in item['consumer']: + kv_last_layer = shared_kv_cache_dict[group_id] + no_reuse_flag = False + # print(f'[Layer-{i}]: Reuse KV cache from Layer-{self.kv_reuse_group[group_id]["producer"]}') + break + + if no_reuse_flag: + kv_last_layer = None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask if (self.sliding_window is None or i in self.global_attn_idx) else attention_mask_swa, + attention_mask_raw, + position_ids, + past_key_values, + output_attentions, + output_router_logits, + use_cache, + kv_last_layer, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask if (self.sliding_window is None or i in self.global_attn_idx) else attention_mask_swa, + attention_mask_raw=attention_mask_raw, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + output_router_logits=output_router_logits, + use_cache=use_cache, + kv_last_layer=kv_last_layer if self.inter_layer_kv_reuse else None, + use_swa=self.sliding_window is not None and i not in self.global_attn_idx, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if output_router_logits: + all_router_logits += (layer_outputs[3],) + + if self.inter_layer_kv_reuse: + kv_last_layer = layer_outputs[-1] + + if self.kv_reuse_group is not None: + for group_id, item in enumerate(self.kv_reuse_group): + if i == item['producer']: + shared_kv_cache_dict[group_id] = kv_last_layer + break + + del shared_kv_cache_dict + + 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 self.config.num_memory_tokens > 0 and (past_key_values is None or past_key_values.get_seq_length() == 0): + if self.config.memory_tokens_interspersed_every > 0: + hidden_states = rearrange(hidden_states, 'b (n m) d -> (b n) m d', m = (self.config.num_memory_tokens + self.config.memory_tokens_interspersed_every)) + + mem, hidden_states = unpack(hidden_states, mem_packed_shape, 'b * d') + + if self.config.memory_tokens_interspersed_every > 0: + hidden_states = rearrange(hidden_states, '(b n) m d -> b (n m) d', b = ori_b) + + hidden_states = hidden_states[:, :ori_n, :] + + if past_key_values and not past_key_values.has_previous_state: + past_key_values.has_previous_state = True + + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + + if not return_dict: + return tuple( + v + for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits] + if v is not None + ) + 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, + ) + + + + +# Adapted from transformers.models.mixtral.modeling_mixtral.MixtralForCausalLM with MIXTRAL->JAMBA, Mixtral->Hymba +class HymbaForCausalLM(HymbaPreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config: HymbaConfig): + super().__init__(config) + self.config = config + self.model = HymbaModel(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 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 + + @add_start_docstrings_to_model_forward(HYMBA_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + output_router_logits: Optional[bool] = None, + return_dict: Optional[bool] = None, + calc_logits_for_entire_prompt: Optional[bool] = True, + ) -> Union[Tuple, MoeCausalLMOutputWithPast]: + + r""" + Args: + 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]`. + + calc_logits_for_entire_prompt (`bool`, *optional*): + Whether or not to calculate the logits for the entire prompt, or just the last token. Only last token + logits are needed for generation, and calculating them only for that token can save memory, + which becomes pretty significant for long sequences. + + Returns: + ```""" + + 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 + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = 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, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + if calc_logits_for_entire_prompt: + logits = self.lm_head(hidden_states) + else: + logits = self.lm_head(hidden_states[..., -1:, :]) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + aux_loss = None + if output_router_logits: + aux_loss = load_balancing_loss_func( + outputs.router_logits if return_dict else outputs[-1], + 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 + + if not return_dict: + output = (logits,) + outputs[1:] + if output_router_logits: + output = (aux_loss,) + output + return (loss,) + output if loss is not None else output + + # print("hidden_states.shape:", hidden_states.shape, "input_ids.shape:", input_ids.shape, "logits.shape:", logits.shape) + + 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, + **kwargs, + ): + if self.config.num_memory_tokens > 0: + attention_mask = torch.cat([torch.ones(input_ids.shape[0], self.config.num_memory_tokens, device=attention_mask.device), attention_mask], dim=1) + + if past_key_values is not None and past_key_values.get_seq_length() > 0: + if isinstance(past_key_values, Tuple): + if past_key_values[self.model._hymba_layer_index][0].shape[2] > 1: + past_key_values = self._convert_to_hymba_cache(past_key_values) + + if isinstance(past_key_values, Cache): + cache_length = past_key_values.get_seq_length() + past_length = past_key_values.seen_tokens + max_cache_length = past_key_values.get_max_length() + + past_length = cache_length + + else: + cache_length = past_length = past_key_values[self.model._attn_layer_index][0].shape[2] + max_cache_length = None + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as + # input) + + if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif self.config.num_memory_tokens <= 0 and past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + + elif self.config.num_memory_tokens > 0 and past_length < input_ids.shape[1] + self.config.num_memory_tokens: + new_query_id = past_length - self.config.num_memory_tokens + input_ids = input_ids[:, new_query_id:] + + if self.config.sliding_window is not None and (self.config.global_attn_idx is None or len(self.config.global_attn_idx) == 0): + input_ids = input_ids[:, -1:] + + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + + # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. + if ( + max_cache_length is not None + and attention_mask is not None + and cache_length + input_ids.shape[1] > max_cache_length + ): + attention_mask = attention_mask[:, -max_cache_length:] + else: + past_key_values = HybridMambaAttentionDynamicCache( + self.config, input_ids.shape[0], self.dtype, device=self.device, layer_type=self.config.layer_type + ) + + position_ids = kwargs.get("position_ids", None) + 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 past_key_values.get_seq_length() > 0: + 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 past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + "output_router_logits": output_router_logits, + "calc_logits_for_entire_prompt": self.config.calc_logits_for_entire_prompt, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past