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1512 lines
68 KiB
1512 lines
68 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/bamba/modular_bamba.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_bamba.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2024 IBM and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections.abc import Callable
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from typing import Any, Optional, TypedDict
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import torch
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from torch import nn
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from transformers.activations import ACT2FN
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from ... import initialization as init
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from ...cache_utils import Cache
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from ...generation import GenerationMixin
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from ...integrations import lazy_load_kernel, use_kernel_forward_from_hub, use_kernelized_func
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from ...modeling_attn_mask_utils import AttentionMaskConverter
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
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from ...utils.generic import is_flash_attention_requested, maybe_autocast
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from .configuration_bamba import BambaConfig
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logger = logging.get_logger(__name__)
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class BambaFlashAttentionKwargs(TypedDict, total=False):
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"""
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Keyword arguments for advanced Flash Attention, causal-conv1d, and mamba_ssm kernel usage.
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Use cases include padding-free training and fewer `torch.compile` graph breaks.
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cu_seq_lens_q (`torch.LongTensor`):
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Gets cumulative sequence length for query state.
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cu_seq_lens_k (`torch.LongTensor`):
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Gets cumulative sequence length for key state.
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max_length_q (`int`):
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Maximum sequence length for query state.
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max_length_k (`int`):
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Maximum sequence length for key state.
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seq_idx (`torch.IntTensor`):
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Index of each packed sequence.
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"""
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cu_seq_lens_q: torch.LongTensor
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cu_seq_lens_k: torch.LongTensor
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max_length_q: int
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max_length_k: int
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seq_idx: torch.IntTensor
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class HybridMambaAttentionDynamicCache:
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"""
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A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
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(which has a constant shape regardless of seq_len).
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This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
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and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
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For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
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while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
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For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
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while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
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and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
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"""
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is_compileable = False
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def __init__(self, config: BambaConfig, batch_size, dtype=torch.float16, device=None):
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self.layers_block_type = config.layers_block_type
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self.has_previous_state = False # only used by mamba
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conv_kernel_size = config.mamba_d_conv
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ssm_state_size = config.mamba_d_state
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self.conv_states = []
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self.ssm_states = []
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self.transformer_layers = []
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for i in range(config.num_hidden_layers):
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if self.layers_block_type[i] == "mamba":
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self.conv_states += [
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torch.zeros(
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batch_size,
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(config.mamba_expand * config.hidden_size + 2 * config.mamba_n_groups * ssm_state_size),
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conv_kernel_size,
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device=device,
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dtype=dtype,
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)
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]
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self.ssm_states += [
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torch.zeros(
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batch_size,
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config.mamba_n_heads,
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config.mamba_d_head,
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ssm_state_size,
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device=device,
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dtype=dtype,
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)
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]
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else:
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self.conv_states += [torch.tensor([[]] * batch_size, device=device)]
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self.ssm_states += [torch.tensor([[]] * batch_size, device=device)]
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self.transformer_layers.append(i)
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self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
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self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
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def __len__(self):
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return len(self.key_cache)
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def __getitem__(self, layer_idx):
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return self.key_cache[layer_idx], self.value_cache[layer_idx]
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def update(
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self,
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key_states: torch.Tensor,
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value_states: torch.Tensor,
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layer_idx: int,
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cache_kwargs: dict[str, Any] | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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# Update the cache
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if self.key_cache[layer_idx].shape[-1] == 0:
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self.key_cache[layer_idx] = key_states
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self.value_cache[layer_idx] = value_states
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else:
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self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
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self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
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return self.key_cache[layer_idx], self.value_cache[layer_idx]
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def reorder_cache(self, beam_idx: torch.LongTensor):
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"""Reorders the cache for beam search, given the selected beam indices."""
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if self.get_seq_length() > 0:
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for layer_idx in range(len(self.key_cache)):
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device = self.key_cache[layer_idx].device
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self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
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device = self.value_cache[layer_idx].device
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self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
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device = self.conv_states[layer_idx].device
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self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
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device = self.ssm_states[layer_idx].device
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self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))
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def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> tuple[int, int]:
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"""Return the length and offset of the cache, used to generate the mask"""
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kv_offset = 0
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query_length = cache_position.shape[0]
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kv_length = self.get_seq_length(layer_idx) + query_length
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return kv_length, kv_offset
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def get_seq_length(self, layer_idx: int | None = 0) -> int:
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"""Returns the sequence length of the cached states. A layer index can be optionally passed."""
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# take any layer that contains cache and not empty tensor
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layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
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if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].shape[-1] == 0:
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return 0
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return self.key_cache[layer_idx].shape[-2]
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class BambaRotaryEmbedding(nn.Module):
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inv_freq: torch.Tensor # fix linting for `register_buffer`
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def __init__(self, config: BambaConfig, device=None):
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super().__init__()
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self.max_seq_len_cached = config.max_position_embeddings
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self.original_max_seq_len = config.max_position_embeddings
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self.config = config
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self.rope_type = self.config.rope_parameters["rope_type"]
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rope_init_fn: Callable = self.compute_default_rope_parameters
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if self.rope_type != "default":
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rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
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@staticmethod
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def compute_default_rope_parameters(
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config: BambaConfig | None = None,
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device: Optional["torch.device"] = None,
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seq_len: int | None = None,
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) -> tuple["torch.Tensor", float]:
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"""
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Computes the inverse frequencies according to the original RoPE implementation
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Args:
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config ([`~transformers.PreTrainedConfig`]):
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The model configuration.
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device (`torch.device`):
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The device to use for initialization of the inverse frequencies.
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seq_len (`int`, *optional*):
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The current sequence length. Unused for this type of RoPE.
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Returns:
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Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
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post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
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"""
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base = config.rope_parameters["rope_theta"]
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dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
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attention_factor = 1.0 # Unused in this type of RoPE
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# Compute the inverse frequencies
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inv_freq = 1.0 / (
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base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
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)
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return inv_freq, attention_factor
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@torch.no_grad()
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@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
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def forward(self, x, position_ids):
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
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with maybe_autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos() * self.attention_scaling
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sin = emb.sin() * self.attention_scaling
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: torch.Tensor | None,
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scaling: float,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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# Adapted from transformers.models.glm.modular_glm.apply_rotary_pos_emb
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def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Removes the interleaving of cos and sin from GLM
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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# Keep half or full tensor for later concatenation
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rotary_dim = cos.shape[-1]
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q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
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k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
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# Apply rotary embeddings on the first half or full tensor
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q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
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k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
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# Concatenate back to full shape
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q_embed = torch.cat([q_embed, q_pass], dim=-1)
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k_embed = torch.cat([k_embed, k_pass], dim=-1)
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return q_embed, k_embed
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@use_kernelized_func(apply_rotary_pos_emb)
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class BambaAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: BambaConfig, layer_idx: int):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.scaling = self.head_dim**-0.5
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self.attention_dropout = config.attention_dropout
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self.is_causal = True
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self.q_proj = nn.Linear(
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config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
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)
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self.k_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.v_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.o_proj = nn.Linear(
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config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
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attention_mask: torch.Tensor | None = None,
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past_key_values: Cache | None = None,
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cache_position: torch.LongTensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor, torch.Tensor]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_values is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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**kwargs,
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)
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|
|
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
|
attn_output = self.o_proj(attn_output)
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class BambaRMSNormGated(torch.nn.Module):
|
|
def __init__(self, hidden_size, eps=1e-6):
|
|
super().__init__()
|
|
self.weight = nn.Parameter(torch.ones(hidden_size))
|
|
self.variance_epsilon = eps
|
|
|
|
def forward(self, hidden_states, gate=None):
|
|
input_dtype = hidden_states.dtype
|
|
hidden_states = hidden_states.to(torch.float32)
|
|
|
|
if gate is not None:
|
|
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
|
|
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
|
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
|
|
|
return self.weight * hidden_states.to(input_dtype)
|
|
|
|
|
|
# Helper methods for segment sum computation
|
|
|
|
|
|
def pad_tensor_by_size(input_tensor: torch.Tensor, pad_size: int):
|
|
"""
|
|
Padding x tensor with `pad_size` on the seq_len dim (dim=1)
|
|
|
|
Assumes that we only have tensors of either size 4 or 3
|
|
"""
|
|
pad_shape = (0, 0, 0, 0, 0, pad_size, 0, 0) if len(input_tensor.shape) == 4 else (0, 0, 0, pad_size, 0, 0)
|
|
|
|
return torch.nn.functional.pad(input_tensor, pad_shape, mode="constant", value=0)
|
|
|
|
|
|
def reshape_into_chunks(input_tensor, pad_size, chunk_size):
|
|
"""
|
|
Padding input_tensor with `pad_size` on the seq_len dim (dim=1) and
|
|
simultaneously splitting it into chunk sequences.
|
|
|
|
Assumes that we only have tensors of either size 4 or 3
|
|
"""
|
|
# [bsz, seq_len, ...] -> [bsz, seq_len multiple of chunk_size, ...]
|
|
input_tensor = pad_tensor_by_size(input_tensor, pad_size)
|
|
|
|
if len(input_tensor.shape) == 3:
|
|
# [bsz, seq_len multiple of chunk_size, num_heads] -> [bsz, -1, chunk_size, num_heads]
|
|
return input_tensor.reshape(input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2])
|
|
else:
|
|
# [bsz, seq_len multiple of chunk_size, num_heads, head_dim or state_size] -> [bsz, -1, chunk_size, num_heads, head_dim or state_size]
|
|
return input_tensor.reshape(
|
|
input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2], input_tensor.shape[3]
|
|
)
|
|
|
|
|
|
def segment_sum(input_tensor):
|
|
"""
|
|
More stable segment sum calculation. Uses cumulative sums and masking instead of direct subtractions.
|
|
"""
|
|
chunk_size = input_tensor.size(-1)
|
|
# 1. expand input tensor to have an additional dimension and repeat along that dimension
|
|
# [..., chunk_size] -> [..., chunk_size, chunk_size]
|
|
input_tensor = input_tensor[..., None].expand(*input_tensor.size(), chunk_size)
|
|
# 2. create a lower triangular mask with the diagonal set to 0 to 0 out elements above diag
|
|
mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=-1)
|
|
input_tensor = input_tensor.masked_fill(~mask, 0)
|
|
# 3. compute actual cumsum
|
|
tensor_segsum = torch.cumsum(input_tensor, dim=-2)
|
|
|
|
# 4. apply mask to keep only the lower triangular part of the cumulative sum result (incl diagonal this time)
|
|
mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=0)
|
|
tensor_segsum = tensor_segsum.masked_fill(~mask, -torch.inf)
|
|
return tensor_segsum
|
|
|
|
|
|
def apply_mask_to_padding_states(hidden_states, attention_mask):
|
|
"""
|
|
Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66
|
|
"""
|
|
# NOTE: attention mask is a 2D boolean tensor
|
|
if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1:
|
|
dtype = hidden_states.dtype
|
|
hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
|
|
|
|
return hidden_states
|
|
|
|
|
|
# Adapted from transformers.models.mamba2.modeling_mamba2.Mamba2Mixer
|
|
class BambaMixer(nn.Module):
|
|
"""
|
|
Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
|
|
A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
|
|
∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
|
|
and is why Mamba is called **selective** state spaces)
|
|
|
|
The are a few differences between this and Mamba2Mixer:
|
|
- The variable use_precomputed_states is slightly different due to the hybrid cache structure
|
|
- There's a few non-obvious bugs fixed with batching in the slow path that exist in main
|
|
- Some extra variables that our layer doesn't need have been removed
|
|
- We ported most of the refactors in https://github.com/huggingface/transformers/pull/35154, which is (as of Dec 18, 2024) unmerged
|
|
"""
|
|
|
|
def __init__(self, config: BambaConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.num_heads = config.mamba_n_heads
|
|
self.hidden_size = config.hidden_size
|
|
self.ssm_state_size = config.mamba_d_state
|
|
self.conv_kernel_size = config.mamba_d_conv
|
|
self.intermediate_size = int(config.mamba_expand * self.hidden_size)
|
|
self.layer_idx = layer_idx
|
|
self.use_conv_bias = config.mamba_conv_bias
|
|
self.activation = config.hidden_act
|
|
self.act = ACT2FN[config.hidden_act]
|
|
self.use_bias = config.mamba_proj_bias
|
|
|
|
self.layer_norm_epsilon = config.rms_norm_eps
|
|
|
|
self.n_groups = config.mamba_n_groups
|
|
self.head_dim = config.mamba_d_head
|
|
self.chunk_size = config.mamba_chunk_size
|
|
|
|
self.time_step_limit = config.time_step_limit
|
|
self.time_step_min = config.time_step_min
|
|
self.time_step_max = config.time_step_max
|
|
|
|
self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size
|
|
self.conv1d = nn.Conv1d(
|
|
in_channels=self.conv_dim,
|
|
out_channels=self.conv_dim,
|
|
bias=config.mamba_conv_bias,
|
|
kernel_size=self.conv_kernel_size,
|
|
groups=self.conv_dim,
|
|
padding=self.conv_kernel_size - 1,
|
|
)
|
|
|
|
# projection of the input hidden states
|
|
projection_size = self.intermediate_size + self.conv_dim + self.num_heads
|
|
self.in_proj = nn.Linear(
|
|
self.hidden_size,
|
|
projection_size,
|
|
bias=self.use_bias,
|
|
)
|
|
# selective projection used to make dt, B and C input dependent
|
|
|
|
# time step projection (discretization)
|
|
# instantiate once and copy inv_dt in init_weights of PretrainedModel
|
|
self.dt_bias = nn.Parameter(torch.ones(self.num_heads))
|
|
|
|
# S4D real initialization. These are not discretized!
|
|
# The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
|
|
A = torch.arange(1, self.num_heads + 1)
|
|
self.A_log = nn.Parameter(torch.log(A))
|
|
self.norm = BambaRMSNormGated(self.intermediate_size, eps=self.layer_norm_epsilon)
|
|
self.D = nn.Parameter(torch.ones(self.num_heads))
|
|
|
|
self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias)
|
|
|
|
global causal_conv1d_update, causal_conv1d_fn
|
|
causal_conv1d = lazy_load_kernel("causal-conv1d")
|
|
causal_conv1d_update = getattr(causal_conv1d, "causal_conv1d_update", None)
|
|
causal_conv1d_fn = getattr(causal_conv1d, "causal_conv1d_fn", None)
|
|
|
|
global selective_state_update, mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined
|
|
mamba_ssm = lazy_load_kernel("mamba-ssm")
|
|
selective_state_update = getattr(mamba_ssm, "selective_state_update", None)
|
|
mamba_chunk_scan_combined = getattr(mamba_ssm, "mamba_chunk_scan_combined", None)
|
|
mamba_split_conv1d_scan_combined = getattr(mamba_ssm, "mamba_split_conv1d_scan_combined", None)
|
|
|
|
global is_fast_path_available
|
|
is_fast_path_available = all((selective_state_update, causal_conv1d_fn, causal_conv1d_update))
|
|
|
|
if not is_fast_path_available:
|
|
logger.warning_once(
|
|
"The fast path is not available because one of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)`"
|
|
" is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and"
|
|
" https://github.com/Dao-AILab/causal-conv1d"
|
|
)
|
|
else:
|
|
logger.warning_once("The fast path for Bamba will be used when running the model on a GPU")
|
|
|
|
def cuda_kernels_forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
cache_params: HybridMambaAttentionDynamicCache | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
seq_idx: torch.IntTensor | None = None,
|
|
):
|
|
# 1. Gated MLP's linear projection
|
|
hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
|
|
projected_states = self.in_proj(hidden_states)
|
|
|
|
# Set up dimensions for reshapes later
|
|
batch_size, seq_len, _ = hidden_states.shape
|
|
groups_time_state_size = self.n_groups * self.ssm_state_size
|
|
|
|
use_precomputed_states = (
|
|
cache_params is not None
|
|
and cache_params.has_previous_state
|
|
and seq_len == 1
|
|
and cache_params.conv_states[self.layer_idx].shape[0]
|
|
== cache_params.ssm_states[self.layer_idx].shape[0]
|
|
== batch_size
|
|
and cache_position is not None
|
|
and cache_position[0] > 0
|
|
)
|
|
|
|
# getting projected states from cache if it exists
|
|
if use_precomputed_states:
|
|
gate, hidden_states_B_C, dt = projected_states.squeeze(1).split(
|
|
[self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
|
|
)
|
|
|
|
# 2. Convolution sequence transformation
|
|
hidden_states_B_C = causal_conv1d_update(
|
|
hidden_states_B_C,
|
|
cache_params.conv_states[self.layer_idx],
|
|
self.conv1d.weight.squeeze(1),
|
|
self.conv1d.bias,
|
|
self.activation,
|
|
)
|
|
|
|
hidden_states, B, C = torch.split(
|
|
hidden_states_B_C,
|
|
[self.intermediate_size, groups_time_state_size, groups_time_state_size],
|
|
dim=-1,
|
|
)
|
|
|
|
# 3. SSM transformation
|
|
A = -torch.exp(self.A_log.float()) # (nheads,)
|
|
A = A[:, None, ...][:, :, None].expand(-1, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
|
|
dt = dt[:, :, None].expand(-1, -1, self.head_dim)
|
|
dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim)
|
|
D = self.D[:, None, ...].expand(-1, self.head_dim)
|
|
B = B.view(batch_size, self.n_groups, B.shape[1] // self.n_groups)
|
|
C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
|
|
hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
|
|
hidden_states = selective_state_update(
|
|
cache_params.ssm_states[self.layer_idx],
|
|
hidden_states_reshaped,
|
|
dt,
|
|
A,
|
|
B,
|
|
C,
|
|
D,
|
|
z=None,
|
|
dt_bias=dt_bias,
|
|
dt_softplus=True,
|
|
)
|
|
hidden_states = hidden_states.view(batch_size, self.num_heads * self.head_dim)
|
|
hidden_states = self.norm(hidden_states, gate)
|
|
|
|
# 4. Final linear projection
|
|
out = self.out_proj(hidden_states)[:, None, ...]
|
|
# Fused calculations or step by step if no initialized cache is found
|
|
else:
|
|
A = -torch.exp(self.A_log.float()) # (num_heads) or (intermediate_size, state_size)
|
|
dt_limit_kwargs = {} if self.time_step_limit == (0.0, float("inf")) else {"dt_limit": self.time_step_limit}
|
|
|
|
# 2-4. Fused kernel for conv1d, SSM, and the final projection
|
|
if self.training and cache_params is None:
|
|
out = mamba_split_conv1d_scan_combined(
|
|
projected_states,
|
|
self.conv1d.weight.squeeze(1),
|
|
self.conv1d.bias,
|
|
self.dt_bias,
|
|
A,
|
|
D=self.D,
|
|
chunk_size=self.chunk_size,
|
|
seq_idx=seq_idx,
|
|
activation=self.activation,
|
|
rmsnorm_weight=self.norm.weight,
|
|
rmsnorm_eps=self.norm.variance_epsilon,
|
|
outproj_weight=self.out_proj.weight,
|
|
outproj_bias=self.out_proj.bias,
|
|
headdim=self.head_dim,
|
|
ngroups=self.n_groups,
|
|
norm_before_gate=False,
|
|
return_final_states=False,
|
|
**dt_limit_kwargs,
|
|
)
|
|
|
|
else:
|
|
gate, hidden_states_B_C, dt = projected_states.split(
|
|
[self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
|
|
)
|
|
|
|
# 2. Convolution sequence transformation
|
|
# Init cache
|
|
if cache_params is not None:
|
|
# storing the states
|
|
# If we just take xBC[:, :, -self.d_conv :], it will error if seqlen < self.d_conv
|
|
# Instead F.pad will pad with zeros if seqlen < self.d_conv, and truncate otherwise.
|
|
hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
|
|
conv_states = nn.functional.pad(
|
|
hidden_states_B_C_transposed,
|
|
(self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0),
|
|
)
|
|
cache_params.conv_states[self.layer_idx].copy_(conv_states)
|
|
|
|
if self.activation not in ["silu", "swish"]:
|
|
hidden_states_B_C = self.act(
|
|
self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2)
|
|
)
|
|
else:
|
|
hidden_states_B_C = causal_conv1d_fn(
|
|
x=hidden_states_B_C.transpose(1, 2),
|
|
weight=self.conv1d.weight.squeeze(1),
|
|
bias=self.conv1d.bias,
|
|
activation=self.activation,
|
|
seq_idx=seq_idx,
|
|
).transpose(1, 2)
|
|
|
|
hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
|
|
hidden_states, B, C = torch.split(
|
|
hidden_states_B_C,
|
|
[self.intermediate_size, groups_time_state_size, groups_time_state_size],
|
|
dim=-1,
|
|
)
|
|
|
|
# 3. SSM transformation
|
|
scan_output, ssm_state = mamba_chunk_scan_combined(
|
|
hidden_states.view(batch_size, seq_len, -1, self.head_dim),
|
|
dt,
|
|
A,
|
|
B.view(batch_size, seq_len, self.n_groups, -1),
|
|
C.view(batch_size, seq_len, self.n_groups, -1),
|
|
chunk_size=self.chunk_size,
|
|
D=self.D,
|
|
z=None,
|
|
seq_idx=seq_idx,
|
|
return_final_states=True,
|
|
dt_bias=self.dt_bias,
|
|
dt_softplus=True,
|
|
**dt_limit_kwargs,
|
|
)
|
|
|
|
# Init cache
|
|
if ssm_state is not None and cache_params is not None:
|
|
cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
|
|
|
|
scan_output = scan_output.view(batch_size, seq_len, -1)
|
|
# Multiply "gate" branch and apply extra normalization layer
|
|
scan_output = self.norm(scan_output, gate)
|
|
|
|
# 4. Final linear projection
|
|
out = self.out_proj(scan_output)
|
|
return out
|
|
|
|
# fmt: off
|
|
def torch_forward(
|
|
self,
|
|
input_states,
|
|
cache_params: HybridMambaAttentionDynamicCache | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
):
|
|
batch_size, seq_len, _ = input_states.shape
|
|
dtype = input_states.dtype
|
|
|
|
# 1. Gated MLP's linear projection
|
|
input_states = apply_mask_to_padding_states(input_states, attention_mask)
|
|
projected_states = self.in_proj(input_states)
|
|
gate, hidden_states_B_C, dt = projected_states.split(
|
|
[self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
|
|
)
|
|
|
|
use_precomputed_states = (
|
|
cache_params is not None
|
|
and cache_params.has_previous_state
|
|
and seq_len == 1
|
|
and cache_params.conv_states[self.layer_idx].shape[0]
|
|
== cache_params.ssm_states[self.layer_idx].shape[0]
|
|
== batch_size
|
|
and cache_position is not None
|
|
and cache_position[0] > 0
|
|
)
|
|
|
|
# 2. Convolution sequence transformation
|
|
if use_precomputed_states:
|
|
cache_params.conv_states[self.layer_idx] = cache_params.conv_states[self.layer_idx].roll(shifts=-1, dims=-1)
|
|
cache_params.conv_states[self.layer_idx][:, :, -1] = hidden_states_B_C[:, 0, :].to(cache_params.conv_states[self.layer_idx].device)
|
|
|
|
# We need to guarantee that anything regarding the cache is on the same device
|
|
conv_states = cache_params.conv_states[self.layer_idx].to(device=self.conv1d.weight.device)
|
|
|
|
hidden_states_B_C = torch.sum(
|
|
conv_states * self.conv1d.weight.squeeze(1), dim=-1
|
|
)
|
|
if self.use_conv_bias:
|
|
hidden_states_B_C = hidden_states_B_C + self.conv1d.bias
|
|
hidden_states_B_C = self.act(hidden_states_B_C)
|
|
else:
|
|
# Init cache
|
|
if cache_params is not None:
|
|
hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
|
|
conv_states = nn.functional.pad(
|
|
hidden_states_B_C_transposed, (self.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0)
|
|
)
|
|
cache_params.conv_states[self.layer_idx].copy_(conv_states)
|
|
|
|
hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2))
|
|
|
|
hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
|
|
hidden_states, B, C = torch.split(
|
|
hidden_states_B_C,
|
|
[self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size],
|
|
dim=-1
|
|
)
|
|
|
|
# 3. SSM transformation
|
|
A = -torch.exp(self.A_log.float()) # [num_heads]
|
|
if use_precomputed_states:
|
|
# We need to guarantee that anything regarding the cache is on the same device
|
|
cache_device = cache_params.ssm_states[self.layer_idx].device
|
|
|
|
# Note: there is no need to pad parameter matrices here, as there is just one new token
|
|
# for batched generation
|
|
dt = dt[:, 0, :][:, None, ...]
|
|
dt = dt.transpose(1, 2).expand(batch_size, dt.shape[-1], self.head_dim)
|
|
# [num_heads] -> [num_heads, head_dim]
|
|
dt_bias = self.dt_bias[..., None].expand(self.dt_bias.shape[0], self.head_dim)
|
|
|
|
dt = torch.nn.functional.softplus(dt + dt_bias.to(dt.dtype))
|
|
dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1])
|
|
A = A[..., None, None].expand(self.num_heads, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
|
|
# [bsz, num_heads, head_dim, state_size]
|
|
dA = (torch.exp(dt[..., None] * A)).to(device=cache_device)
|
|
|
|
# Discretize B
|
|
# [bsz, n_groups * state_size] -> [bsz, n_groups, 1, state_size] ->
|
|
# -> [bsz, n_groups, group to head repetition factor, state_size] -> [bsz, num_heads, state_size]
|
|
B = B.reshape(batch_size, self.n_groups, -1)[..., None, :]
|
|
B = B.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, B.shape[-1]).contiguous()
|
|
B = B.reshape(batch_size, -1, B.shape[-1])
|
|
# [bsz, num_heads, head_dim, state_size]
|
|
dB = dt[..., None] * B[..., None, :]
|
|
|
|
# Discretize x into dB
|
|
# [bsz, intermediate_size] -> [bsz, num_heads, head_dim]
|
|
hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim)
|
|
dBx = (dB * hidden_states[..., None]).to(device=cache_device)
|
|
|
|
# State calculation
|
|
cache_params.ssm_states[self.layer_idx].copy_(
|
|
cache_params.ssm_states[self.layer_idx] * dA + dBx
|
|
)
|
|
|
|
# Subsequent output
|
|
# [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
|
|
C = C.reshape(batch_size, self.n_groups, -1)[..., None, :]
|
|
C = C.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, C.shape[-1]).contiguous()
|
|
C = C.reshape(batch_size, -1, C.shape[-1])
|
|
# [bsz, num_heads, head_dim]
|
|
|
|
ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype) # Shape: [b, h, d, n]
|
|
# Reshape ssm_states to merge the first two dimensions
|
|
ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size) # Shape: [b*h, d, n]
|
|
C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1) # Shape: [b*h, n, 1]
|
|
y = torch.bmm(ssm_states_reshaped, C_reshaped)
|
|
y = y.view(batch_size, self.num_heads, self.head_dim)
|
|
|
|
# D skip connection
|
|
# [num_heads] -> [num_heads, head_dim]
|
|
D = self.D[..., None].expand(self.D.shape[0], self.head_dim)
|
|
y = (y + hidden_states * D).to(y.dtype)
|
|
|
|
# [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size]
|
|
y = y.reshape(batch_size, -1)[:, None, ...]
|
|
else:
|
|
# begin ssd naive implementation without einsums
|
|
dt = nn.functional.softplus(dt + self.dt_bias)
|
|
dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1])
|
|
hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).float()
|
|
B = B.reshape(batch_size, seq_len, -1, self.ssm_state_size).float()
|
|
C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).float()
|
|
B = B.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads)
|
|
C = C.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads)
|
|
pad_size = (self.chunk_size - seq_len % self.chunk_size) % self.chunk_size
|
|
|
|
D_residual = self.D[..., None] * pad_tensor_by_size(hidden_states, pad_size)
|
|
|
|
# Discretize x and A
|
|
hidden_states = hidden_states * dt[..., None]
|
|
A = A.to(hidden_states.dtype) * dt
|
|
|
|
# Rearrange into blocks/chunks
|
|
hidden_states, A, B, C = [reshape_into_chunks(t, pad_size, self.chunk_size) for t in (hidden_states, A, B, C)]
|
|
|
|
# [bsz, -1, chunk_size, num_heads] -> [bsz, num_heads, -1, chunk_size]
|
|
A = A.permute(0, 3, 1, 2)
|
|
A_cumsum = torch.cumsum(A, dim=-1)
|
|
|
|
# 1. Compute the output for each intra-chunk (diagonal blocks)
|
|
# This is the analog of a causal mask
|
|
L = torch.exp(segment_sum(A))
|
|
|
|
# Contraction of C and B to get G (attention-weights like)
|
|
G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, :, :] # shape: (b, c, l, s, h, n)
|
|
G = G_intermediate.sum(dim=-1) # shape: (b, c, l, s, h)
|
|
|
|
# Compute M, equivalent to applying attention mask to weights
|
|
M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None]
|
|
M = M_intermediate.sum(dim=-1)
|
|
|
|
# Compute Y_diag (apply to values)
|
|
Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(dim=3)
|
|
|
|
# 2. Compute the state for each intra-chunk
|
|
# (right term of low-rank factorization of off-diagonal blocks; B terms)
|
|
decay_states = torch.exp(A_cumsum[:, :, :, -1:] - A_cumsum)
|
|
B_decay = B * decay_states.permute(0, -2, -1, 1)[..., None]
|
|
states = (B_decay[..., None, :] * hidden_states[..., None]).sum(dim=2)
|
|
|
|
# 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
|
|
# (middle term of factorization of off-diag blocks; A terms)
|
|
if use_precomputed_states:
|
|
previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device)
|
|
else:
|
|
previous_states = torch.zeros_like(states[:, :1])
|
|
states = torch.cat([previous_states, states], dim=1)
|
|
decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
|
|
decay_chunk = decay_chunk.transpose(1, 3)
|
|
new_states = (decay_chunk[..., None, None] * states[:, :, None, ...]).sum(dim=1)
|
|
states, ssm_state = new_states[:, :-1], new_states[:, -1]
|
|
|
|
# 4. Compute state -> output conversion per chunk
|
|
# (left term of low-rank factorization of off-diagonal blocks; C terms)
|
|
state_decay_out = torch.exp(A_cumsum)
|
|
C_times_states = (C[..., None, :] * states[:, :, None, ...])
|
|
state_decay_out_permuted = state_decay_out.permute(0, 2, 3, 1)
|
|
Y_off = (C_times_states.sum(-1) * state_decay_out_permuted[..., None])
|
|
|
|
# Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks)
|
|
y = Y_diag + Y_off
|
|
# [bsz, -1, self.chunk_size, num_heads, head_dim] -> [bsz, (padded) seq_len, num_heads, head_dim]
|
|
y = y.reshape(batch_size, -1, self.num_heads, self.head_dim)
|
|
|
|
y = y + D_residual
|
|
# Cutting off padded chunks
|
|
if pad_size > 0:
|
|
y = y[:, :seq_len, :, :]
|
|
y = y.reshape(batch_size, seq_len, -1)
|
|
|
|
# Init cache
|
|
if ssm_state is not None and cache_params is not None:
|
|
cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
|
|
|
|
scan_output = self.norm(y, gate)
|
|
|
|
# end ssd naive
|
|
|
|
# 4. Final linear projection
|
|
contextualized_states = self.out_proj(scan_output.to(dtype)) # [batch, seq_len, hidden_size]
|
|
return contextualized_states
|
|
# fmt: on
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
cache_params: HybridMambaAttentionDynamicCache | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
seq_idx: torch.IntTensor | None = None,
|
|
**kwargs,
|
|
):
|
|
if is_fast_path_available and "cuda" in self.in_proj.weight.device.type and not is_torchdynamo_compiling():
|
|
return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask, seq_idx)
|
|
if seq_idx is not None:
|
|
raise NotImplementedError(
|
|
"`seq_idx` support requires fast path support. Please install `mamba_ssm` and `causal_conv1d`"
|
|
)
|
|
dtype = hidden_states.dtype
|
|
if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1:
|
|
# tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
|
|
hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
|
|
|
|
return self.torch_forward(hidden_states, cache_params, cache_position, attention_mask)
|
|
|
|
|
|
class BambaMLP(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
self.intermediate_size = config.intermediate_size
|
|
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
|
|
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
|
|
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
|
|
self.act_fn = ACT2FN[config.hidden_act]
|
|
|
|
def forward(self, x):
|
|
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
|
return down_proj
|
|
|
|
|
|
@use_kernel_forward_from_hub("RMSNorm")
|
|
class BambaRMSNorm(nn.Module):
|
|
def __init__(self, hidden_size, eps=1e-6):
|
|
"""
|
|
BambaRMSNorm is equivalent to T5LayerNorm
|
|
"""
|
|
super().__init__()
|
|
self.weight = nn.Parameter(torch.ones(hidden_size))
|
|
self.variance_epsilon = eps
|
|
|
|
def forward(self, hidden_states):
|
|
input_dtype = hidden_states.dtype
|
|
hidden_states = hidden_states.to(torch.float32)
|
|
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
|
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
|
return self.weight * hidden_states.to(input_dtype)
|
|
|
|
def extra_repr(self):
|
|
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
|
|
|
|
|
|
class BambaDecoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: BambaConfig, layer_idx: int, layer_type: str = "mamba"):
|
|
super().__init__()
|
|
|
|
num_experts = 1
|
|
ffn_layer_class = BambaMLP if num_experts == 1 else None
|
|
self.feed_forward = ffn_layer_class(config)
|
|
self.input_layernorm = BambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.pre_ff_layernorm = BambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
self.layer_type = layer_type
|
|
if layer_type == "mamba":
|
|
self.mamba = BambaMixer(config=config, layer_idx=layer_idx)
|
|
elif layer_type == "attention":
|
|
self.self_attn = BambaAttention(config, layer_idx)
|
|
else:
|
|
raise ValueError("Invalid layer_type")
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: HybridMambaAttentionDynamicCache | None = None,
|
|
output_attentions: bool | None = False,
|
|
use_cache: bool | None = False,
|
|
cache_position: torch.LongTensor | None = None,
|
|
position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
|
|
**kwargs: Unpack[BambaFlashAttentionKwargs],
|
|
) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
|
|
"""
|
|
Args:
|
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
|
`(batch, sequence_length)` where padding elements are indicated by 0.
|
|
past_key_values (`HybridMambaAttentionDynamicCache`, *optional*): cached past key and value projection states
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
|
(see `past_key_values`).
|
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
|
|
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
|
|
with `head_dim` being the embedding dimension of each attention head.
|
|
kwargs (`dict`, *optional*):
|
|
Arbitrary kwargs. Can be used to provide `BambaFlashAttentionKwargs` for
|
|
padding-free training and/or improve torch.compile performance.
|
|
"""
|
|
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
# this is a hybrid decoder layer
|
|
if self.layer_type == "mamba":
|
|
hidden_states = self.mamba(
|
|
hidden_states=hidden_states,
|
|
cache_params=past_key_values,
|
|
cache_position=cache_position,
|
|
attention_mask=attention_mask,
|
|
**kwargs,
|
|
)
|
|
self_attn_weights = None
|
|
elif self.layer_type == "attention":
|
|
hidden_states, self_attn_weights = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
# residual connection after attention
|
|
hidden_states = residual + hidden_states
|
|
|
|
# feed-forward
|
|
residual = hidden_states
|
|
hidden_states = self.pre_ff_layernorm(hidden_states)
|
|
hidden_states = self.feed_forward(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights,)
|
|
|
|
return outputs
|
|
|
|
|
|
@auto_docstring
|
|
class BambaPreTrainedModel(PreTrainedModel):
|
|
config: BambaConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["BambaDecoderLayer"]
|
|
_skip_keys_device_placement = "past_key_values"
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
# Note: only supports HybridMambaAttentionDynamicCache
|
|
_is_stateful = True
|
|
|
|
@torch.no_grad()
|
|
def _init_weights(self, module):
|
|
super()._init_weights(module)
|
|
if isinstance(module, BambaMixer):
|
|
init.ones_(module.dt_bias)
|
|
init.copy_(module.A_log, torch.log(torch.arange(1, module.num_heads + 1)))
|
|
init.ones_(module.D)
|
|
|
|
|
|
@auto_docstring
|
|
class BambaModel(BambaPreTrainedModel):
|
|
def __init__(self, config: BambaConfig):
|
|
super().__init__(config)
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
|
decoder_layers = []
|
|
for i in range(config.num_hidden_layers):
|
|
decoder_layers.append(BambaDecoderLayer(config, layer_idx=i, layer_type=config.layers_block_type[i]))
|
|
self.layers = nn.ModuleList(decoder_layers)
|
|
|
|
self._attn_implementation = config._attn_implementation
|
|
self.final_layernorm = BambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = BambaRotaryEmbedding(config=config)
|
|
|
|
self.gradient_checkpointing = False
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: HybridMambaAttentionDynamicCache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
output_attentions: bool | None = None,
|
|
output_hidden_states: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[BambaFlashAttentionKwargs],
|
|
) -> BaseModelOutputWithPast:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if self.gradient_checkpointing and self.training and use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
|
)
|
|
use_cache = False
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
hidden_states = inputs_embeds
|
|
|
|
if use_cache and past_key_values is None:
|
|
logger.warning_once(
|
|
"Bamba requires an initialized `HybridMambaAttentionDynamicCache` to return a cache. None was "
|
|
"provided, so no cache will be returned."
|
|
)
|
|
|
|
if cache_position is None:
|
|
cache_position = torch.arange(hidden_states.shape[1], device=hidden_states.device)
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0)
|
|
|
|
causal_mask = self._update_causal_mask(
|
|
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
|
)
|
|
mamba_mask = self._update_mamba_mask(attention_mask, cache_position)
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)
|
|
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
|
|
for decoder_layer in self.layers:
|
|
# Depending on the layer type we opt for 2D base attention mask (Mamba) or 4D causal mask (Attention)
|
|
layer_mask = mamba_mask if decoder_layer.layer_type == "mamba" else causal_mask
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=layer_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
if layer_outputs[1] is not None:
|
|
# append attentions only of attention layers. Mamba layers return `None` as the attention weights
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
hidden_states = self.final_layernorm(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
if past_key_values and not past_key_values.has_previous_state:
|
|
past_key_values.has_previous_state = True
|
|
|
|
next_cache = None if not use_cache else past_key_values
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
def _update_causal_mask(
|
|
self,
|
|
attention_mask: torch.Tensor,
|
|
input_tensor: torch.Tensor,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: HybridMambaAttentionDynamicCache,
|
|
output_attentions: bool,
|
|
):
|
|
if is_flash_attention_requested(self.config):
|
|
if attention_mask is not None and 0.0 in attention_mask:
|
|
return attention_mask
|
|
return None
|
|
|
|
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
|
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
|
# to infer the attention mask.
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
|
|
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
|
if self.config._attn_implementation == "sdpa" and not output_attentions:
|
|
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
|
attention_mask,
|
|
inputs_embeds=input_tensor,
|
|
past_key_values_length=past_seen_tokens,
|
|
is_training=self.training,
|
|
):
|
|
return None
|
|
|
|
dtype = input_tensor.dtype
|
|
sequence_length = input_tensor.shape[1]
|
|
target_length = (
|
|
attention_mask.shape[-1]
|
|
if isinstance(attention_mask, torch.Tensor)
|
|
else past_seen_tokens + sequence_length + 1
|
|
)
|
|
|
|
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
|
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask,
|
|
sequence_length=sequence_length,
|
|
target_length=target_length,
|
|
dtype=dtype,
|
|
cache_position=cache_position,
|
|
batch_size=input_tensor.shape[0],
|
|
)
|
|
|
|
if (
|
|
self.config._attn_implementation == "sdpa"
|
|
and attention_mask is not None
|
|
and attention_mask.device.type in ["cuda", "xpu", "npu"]
|
|
and not output_attentions
|
|
):
|
|
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
|
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
|
# Details: https://github.com/pytorch/pytorch/issues/110213
|
|
min_dtype = torch.finfo(dtype).min
|
|
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
|
|
|
return causal_mask
|
|
|
|
@staticmethod
|
|
def _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask: torch.Tensor,
|
|
sequence_length: int,
|
|
target_length: int,
|
|
dtype: torch.dtype,
|
|
cache_position: torch.Tensor,
|
|
batch_size: int,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
|
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
|
|
|
|
Args:
|
|
attention_mask (`torch.Tensor`):
|
|
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
|
|
`(batch_size, 1, query_length, key_value_length)`.
|
|
sequence_length (`int`):
|
|
The sequence length being processed.
|
|
target_length (`int`):
|
|
The target length: when generating with static cache, the mask should be as long as the static cache,
|
|
to account for the 0 padding, the part of the cache that is not filled yet.
|
|
dtype (`torch.dtype`):
|
|
The dtype to use for the 4D attention mask.
|
|
cache_position (`torch.Tensor`):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
batch_size (`torch.Tensor`):
|
|
Batch size.
|
|
"""
|
|
if attention_mask is not None and attention_mask.dim() == 4:
|
|
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
|
|
causal_mask = attention_mask
|
|
else:
|
|
min_dtype = torch.finfo(dtype).min
|
|
causal_mask = torch.full(
|
|
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
|
|
)
|
|
if sequence_length != 1:
|
|
causal_mask = torch.triu(causal_mask, diagonal=1)
|
|
causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
|
|
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
|
if attention_mask is not None:
|
|
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
|
mask_length = attention_mask.shape[-1]
|
|
padding_attention_mask = (attention_mask[:, None, None, :] == attention_mask[:, None, :, None])[
|
|
:, :, -sequence_length:, :
|
|
].to(dtype)
|
|
padding_mask = causal_mask[:, :, :, :mask_length] + padding_attention_mask
|
|
padding_mask = padding_mask == 0
|
|
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
|
padding_mask, min_dtype
|
|
)
|
|
|
|
return causal_mask
|
|
|
|
def _update_mamba_mask(self, attention_mask, cache_position):
|
|
"""
|
|
No need for zeroing states when
|
|
1. Cached forward
|
|
2. Attending to all inputs
|
|
"""
|
|
mamba_mask = attention_mask
|
|
if cache_position[0] > 0 or (attention_mask is not None and torch.all(attention_mask == 1)):
|
|
mamba_mask = None
|
|
return mamba_mask
|
|
|
|
|
|
@auto_docstring
|
|
class BambaForCausalLM(BambaPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
|
|
_tp_plan = {"lm_head": "colwise_gather_output"}
|
|
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = BambaModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
self.z_loss_coefficient = config.z_loss_coefficient
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: HybridMambaAttentionDynamicCache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
output_attentions: bool | None = None,
|
|
output_hidden_states: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**kwargs,
|
|
) -> CausalLMOutputWithPast:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, BambaForCausalLM
|
|
|
|
>>> model = BambaForCausalLM.from_pretrained("...")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("...")
|
|
|
|
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
|
```"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs: BaseModelOutputWithPast = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs.last_hidden_state
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
|
if self.z_loss_coefficient > 0:
|
|
# Type-match loss, but avoid upcasting large logits tensor until after it's been reduced on dim -1
|
|
z_loss = logits.logsumexp(dim=-1).to(dtype=loss.dtype).pow(2).mean()
|
|
loss = loss + self.z_loss_coefficient * z_loss
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
attention_mask=None,
|
|
inputs_embeds=None,
|
|
cache_position=None,
|
|
position_ids=None,
|
|
use_cache=True,
|
|
is_first_iteration=False,
|
|
**kwargs,
|
|
):
|
|
# Overwritten -- has a unique cache type, `HybridMambaAttentionDynamicCache`
|
|
|
|
if past_key_values is None:
|
|
past_key_values = HybridMambaAttentionDynamicCache(
|
|
self.config, input_ids.shape[0], self.dtype, device=self.device
|
|
)
|
|
|
|
kwargs["logits_to_keep"] = self.config.num_logits_to_keep
|
|
model_inputs = super().prepare_inputs_for_generation(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
cache_position=cache_position,
|
|
position_ids=position_ids,
|
|
use_cache=use_cache,
|
|
is_first_iteration=is_first_iteration,
|
|
**kwargs,
|
|
)
|
|
|
|
return model_inputs
|
|
|
|
|
|
__all__ = ["BambaModel", "BambaForCausalLM", "BambaPreTrainedModel"]
|