mirror of
https://github.com/ml-explore/mlx-examples.git
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430 lines
18 KiB
Python
430 lines
18 KiB
Python
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import math
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from dataclasses import dataclass, field
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from typing import Tuple, Union
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import mlx.core as mx
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import mlx.nn as nn
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from .base import BaseModelArgs
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from .cache import Mamba2Cache
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@dataclass
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class ModelArgs(BaseModelArgs):
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num_heads: int
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head_dim: int
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vocab_size: int
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hidden_size: int
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state_size: int
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num_hidden_layers: int
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layer_norm_epsilon: float
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expand: int
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conv_kernel: int
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n_groups: int
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use_bias: bool
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use_conv_bias: bool
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initializer_range: float
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residual_in_fp32: bool
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time_step_min: float
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time_step_max: float
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time_step_floor: float
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rescale_prenorm_residual: bool
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rms_norm: bool
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chunk_size: int
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tie_word_embeddings: bool
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use_cache: bool = True
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time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
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time_step_rank: Union[int, str] = "auto"
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model_type: str = "mamba2"
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def __post_init__(self):
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if not hasattr(self, "intermediate_size"):
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self.intermediate_size = int(self.expand * self.hidden_size)
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if not hasattr(self, "head_dim"):
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self.head_dim = self.hidden_size // self.num_heads
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if self.time_step_rank == "auto":
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self.time_step_rank = math.ceil(self.hidden_size / 16)
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class MambaRMSNormGated(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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super().__init__()
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self.weight = mx.ones((hidden_size,))
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self.variance_epsilon = eps
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def __call__(self, hidden_states, gate=None):
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if gate is not None:
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hidden_states = hidden_states * nn.silu(gate)
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variance = mx.mean(hidden_states ** 2, axis=-1, keepdims=True)
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hidden_states = hidden_states * mx.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states
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def pad_tensor_by_size(input_tensor: mx.array, pad_size: int):
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"""
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Padding x tensor with `pad_size` on the seq_len dim (dim=1)
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Assumes that we only have tensors of either size 4 or 3
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"""
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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)
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return mx.pad(input_tensor, pad_shape, mode="constant", value=0)
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def reshape_into_chunks(input_tensor, pad_size, chunk_size):
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"""
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Padding input_tensor with `pad_size` on the seq_len dim (dim=1) and
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simultaneously splitting it into chunk sequences.
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Assumes that we only have tensors of either size 4 or 3
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"""
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# [bsz, seq_len, ...] -> [bsz, seq_len multiple of chunk_size, ...]
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input_tensor = pad_tensor_by_size(input_tensor, pad_size)
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if len(input_tensor.shape) == 3:
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# [bsz, seq_len multiple of chunk_size, num_heads] -> [bsz, -1, chunk_size, num_heads]
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return input_tensor.reshape(input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2])
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else:
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# [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]
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return input_tensor.reshape(
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input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2], input_tensor.shape[3]
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)
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def segment_sum(input_tensor):
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"""
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More stable segment sum calculation. Uses cumulative sums and masking instead of direct subtractions.
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"""
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chunk_size = input_tensor.size(-1)
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# 1. expand input tensor to have an additional dimension and repeat along that dimension
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# [..., chunk_size] -> [..., chunk_size, chunk_size]
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input_tensor = input_tensor[..., None].expand(*input_tensor.size(), chunk_size)
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# 2. create a lower triangular mask with the diagonal set to 0 to 0 out elements above diag
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mask = mx.tril(mx.ones(chunk_size, chunk_size, device=input_tensor.device), diagonal=-1)
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input_tensor = input_tensor.masked_fill(~mask, 0)
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# 3. compute actual cumsum
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tensor_segsum = mx.cumsum(input_tensor, dim=-2)
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# 4. apply mask to keep only the lower triangular part of the cumulative sum result (incl diagonal this time)
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mask = mx.tril(mx.ones(chunk_size, chunk_size, device=input_tensor.device), diagonal=0)
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tensor_segsum = tensor_segsum.masked_fill(~mask, -mx.inf)
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return tensor_segsum
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class Mamba2Block(nn.Module):
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def __init__(self, args: ModelArgs, layer_idx: int):
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super().__init__()
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self.layer_idx = layer_idx
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self.args = args
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self.hidden_size = args.hidden_size
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self.num_heads = args.num_heads
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self.head_dim = args.head_dim
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self.state_size = args.state_size
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self.n_groups = args.n_groups
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self.conv_kernel = args.conv_kernel
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self.intermediate_size = int(args.expand * args.hidden_size)
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self.time_step_rank = int(args.time_step_rank)
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self.time_step_min = args.time_step_min
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self.time_step_max = args.time_step_max
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self.chunk_size = args.chunk_size
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# Convolution dimension includes both intermediate sizes
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self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size
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self.conv1d = nn.Conv1d(
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in_channels=self.conv_dim,
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out_channels=self.conv_dim,
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bias=args.use_conv_bias,
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kernel_size=args.conv_kernel,
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groups=self.conv_dim,
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padding=args.conv_kernel - 1
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)
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# Compute input projection dimension
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projection_size = self.intermediate_size + self.conv_dim + self.num_heads
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self.in_proj = nn.Linear(args.hidden_size, projection_size, bias=args.use_bias)
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self.dt_bias = mx.ones(self.num_heads)
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A = mx.arange(1, self.num_heads + 1)
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self.A_log = mx.log(A)
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self.D = mx.ones(self.num_heads)
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self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon)
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self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
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def __call__(self, input_states: mx.array, cache):
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batch_size, seq_len, _ = input_states.shape
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# Gated MLP's linear projection
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projected_states = self.in_proj(input_states) # [1, 1, projection_size]
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d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size -
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2 * self.n_groups * self.state_size - self.num_heads) // 2
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# Split projected states
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*_, gate, hidden_states, dt = projected_states.split(
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[d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads],
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axis=-1
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)
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# hidden_states shape: [1, 1, conv_dim]
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# Get SSM state from cache
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ssm_state = cache.ssm_states[self.layer_idx]
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if cache.seqlen_offset > 0:
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# Handle cached generation case
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conv_state = cache.conv_states[self.layer_idx] # [batch, conv_dim, conv_kernel]
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conv_state = mx.roll(conv_state, shifts=-1, axis=-1)
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# Handle batched generation - states are copied through
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# Properly reshape hidden_states for the conv_state update
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conv_state = conv_state.at[:, :, -1].set(hidden_states[:, 0, :])
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cache.conv_states[self.layer_idx] = conv_state
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# Compute convolution output
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hidden_states = mx.sum(conv_state * self.conv1d.weight[:, 0, :], axis=-1)
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if self.args.use_conv_bias:
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hidden_states += self.conv1d.bias
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hidden_states = nn.silu(hidden_states)[:, None, ...] # [batch, 1, conv_dim] : decoding
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else:
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# Handle normal forward pass
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# Properly transpose while preserving the sequence dimension
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hidden_states = hidden_states.transpose(0, 2, 1) # [1, conv_dim, 1]
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# Pad the convolution state
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padding_size = self.conv_kernel - 1
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conv_state = mx.pad(
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hidden_states,
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((0, 0), (0, 0), (padding_size, 0))
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)
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# Store in cache
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cache.conv_states[self.layer_idx] = conv_state
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# Apply convolution with proper padding
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hidden_states = self.conv1d(hidden_states) # [1, conv_dim, 1]
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hidden_states = hidden_states.transpose(0, 2, 1) # [1, 1, conv_dim]
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hidden_states = nn.silu(hidden_states)
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# Split hidden states for SSM computation
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hidden_states, B, C = mx.split(
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hidden_states,
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[self.intermediate_size, self.n_groups * self.state_size, self.n_groups * self.state_size],
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axis=-1
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)
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# Compute A matrix
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A = -mx.exp(self.A_log)
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if cache is not None and cache.seqlen_offset > 0:
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# Note: there is no need to pad parameter matrices here, as there is just one new token
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# for batched generation
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dt = dt[:, None, ...] if dt.ndim == 2 else dt[:, 0, :][:, None, ...]
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dt = dt.transpose(0, 2, 1).expand(batch_size, dt.shape[-1], self.head_dim)
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# [num_heads] -> [num_heads, head_dim]
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dt_bias = self.dt_bias[..., None].expand(self.dt_bias.shape[0], self.head_dim)
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dt = nn.softplus(dt + dt_bias)
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dt = mx.clamp(dt, self.time_step_min) #, self.time_step_max)
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A = A[..., None, None].expand(self.num_heads, self.head_dim, self.state_size)
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# [bsz, num_heads, head_dim, state_size]
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dA = mx.exp(dt[..., None] * A)
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# Discretize B
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# [bsz, n_groups * state_size] -> [bsz, n_groups, 1, state_size] ->
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# -> [bsz, n_groups, group to head repetition factor, state_size] -> [bsz, num_heads, state_size]
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B = B.reshape(batch_size, self.n_groups, -1)[..., None, :]
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B = B.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, B.shape[-1]).contiguous()
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B = B.reshape(batch_size, -1, B.shape[-1])
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# [bsz, num_heads, head_dim, state_size]
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dB = dt[..., None] * B[..., None, :]
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# Discretize x into dB
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# [bsz, intermediate_size] -> [bsz, num_heads, head_dim]
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hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim)
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dBx = dB * hidden_states[..., None]
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# State calculation
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cache.ssm_states[self.layer_idx].copy_(
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cache.ssm_states[self.layer_idx] * dA + dBx
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)
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# Subsequent output
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# [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
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C = C.reshape(batch_size, self.n_groups, -1)[..., None, :]
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C = C.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, C.shape[-1]).contiguous()
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C = C.reshape(batch_size, -1, C.shape[-1])
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# [bsz, num_heads, head_dim]
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ssm_states = cache.ssm_states[self.layer_idx] # Shape: [b, h, d, n]
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# Reshape ssm_states to merge the first two dimensions
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ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.state_size) # Shape: [b*h, d, n]
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C_reshaped = C.view(batch_size * self.num_heads, self.state_size, 1) # Shape: [b*h, n, 1]
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y = ssm_states_reshaped @ C_reshaped
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y = y.view(batch_size, self.num_heads, self.head_dim)
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# D skip connection
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# [num_heads] -> [num_heads, head_dim]
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D = self.D[..., None].expand(self.D.shape[0], self.head_dim)
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y = (y + hidden_states * D)
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# [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size]
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y = y.reshape(batch_size, -1)[:, None, ...]
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else:
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# begin ssd naive implementation without einsums
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dt = nn.functional.softplus(dt + self.dt_bias)
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dt = mx.clamp(dt, self.time_step_min)
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hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim)
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B = B.reshape(batch_size, seq_len, -1, self.state_size)
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C = C.reshape(batch_size, seq_len, -1, self.state_size)
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B = B.repeat(1, 1, self.num_heads // self.n_groups, 1)
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C = C.repeat(1, 1, self.num_heads // self.n_groups, 1)
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pad_size = self.chunk_size - (seq_len % self.chunk_size)
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D_residual = self.D[..., None] * pad_tensor_by_size(hidden_states, pad_size)
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# Discretize x and A
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hidden_states = hidden_states * dt[..., None]
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A = A * dt
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# Rearrange into blocks/chunks
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hidden_states, A, B, C = [reshape_into_chunks(t, pad_size, self.chunk_size) for t in (hidden_states, A, B, C)]
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# [bsz, -1, chunk_size, num_heads] -> [bsz, num_heads, -1, chunk_size]
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A = A.permute(0, 3, 1, 2)
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A_cumsum = mx.cumsum(A, dim=-1)
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# 1. Compute the output for each intra-chunk (diagonal blocks)
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# This is the analog of a causal mask
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L = mx.exp(segment_sum(A))
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# First, contraction of C and B to get G (attention-weights like)
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G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, : ,:] # shape: (b, c, l, s, h, n)
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G = G_intermediate.sum(dim=-1) # shape: (b, c, l, s, h)
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# Step 2: Compute M, equivalent to applying attention mask to weights
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M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None]
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M = M_intermediate.sum(dim=-1)
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# Step 3: Compute Y_diag (apply to values)
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Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(3)
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|
||
|
|
# (right term of low-rank factorization of off-diagonal blocks; B terms)
|
||
|
|
|
||
|
|
decay_states = mx.exp((A_cumsum[:, :, :, -1:] - A_cumsum))
|
||
|
|
B_decay_contraction = B * decay_states.permute(0, 2, 3, 1)[..., None]
|
||
|
|
# permute back B * decay states
|
||
|
|
states = (B_decay_contraction.permute(0, 1, 3, 2, 4)[..., None] * hidden_states.permute(0, 1, 3, 2, 4)[..., None, :]).sum(dim=3).permute(0, 1, 2, 4, 3)
|
||
|
|
if cache is not None and cache.seqlen_offset > 0:
|
||
|
|
previous_states = cache.ssm_states[self.layer_idx][:, None, ...]
|
||
|
|
else:
|
||
|
|
previous_states = mx.zeros_like(states[:, :1])
|
||
|
|
states = mx.concat([previous_states, states], dim=1)
|
||
|
|
decay_chunk = mx.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
|
||
|
|
|
||
|
|
states_permuted = states.permute(0, 2, 1, 3, 4)
|
||
|
|
result = (decay_chunk[..., None, None] * states_permuted[:, :, None, ...]).sum(dim=2)
|
||
|
|
new_states = result.permute(0, 2, 1, 3, 4)
|
||
|
|
states, ssm_state = new_states[:, :-1], new_states[:, -1]
|
||
|
|
|
||
|
|
# Compute state -> output conversion per chunk
|
||
|
|
# (left term of low-rank factorization of off-diagonal blocks; C terms)
|
||
|
|
state_decay_out = mx.exp(A_cumsum)
|
||
|
|
# compute Yoff
|
||
|
|
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)
|
||
|
|
|
||
|
|
if ssm_state is not None and cache is not None:
|
||
|
|
cache.ssm_states[self.layer_idx] = ssm_state
|
||
|
|
|
||
|
|
scan_output = self.norm(y, gate)
|
||
|
|
# end ssd naive
|
||
|
|
|
||
|
|
# 4. Final linear projection
|
||
|
|
return self.out_proj(scan_output) # [batch, seq_len, hidden_size]
|
||
|
|
|
||
|
|
|
||
|
|
class ResidualBlock(nn.Module):
|
||
|
|
def __init__(self, args: ModelArgs, layer_idx: int):
|
||
|
|
super().__init__()
|
||
|
|
self.residual_in_fp32 = args.residual_in_fp32
|
||
|
|
self.mixer = Mamba2Block(args, layer_idx)
|
||
|
|
self.norm = nn.RMSNorm(args.hidden_size)
|
||
|
|
|
||
|
|
def __call__(self, x: mx.array, cache):
|
||
|
|
return self.mixer(self.norm(x), cache) + x
|
||
|
|
|
||
|
|
|
||
|
|
class Mamba2(nn.Module):
|
||
|
|
def __init__(self, args: ModelArgs):
|
||
|
|
super().__init__()
|
||
|
|
self.args = args
|
||
|
|
self.embeddings = nn.Embedding(args.vocab_size, args.hidden_size)
|
||
|
|
self.layers = [ResidualBlock(args, idx) for idx in range(args.num_hidden_layers)]
|
||
|
|
self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
|
||
|
|
|
||
|
|
def __call__(self, x: mx.array, cache):
|
||
|
|
x = self.embeddings(x)
|
||
|
|
if cache is None:
|
||
|
|
cache = [None] * len(self.layers)
|
||
|
|
for layer, c in zip(self.layers, cache):
|
||
|
|
x = layer(x, c)
|
||
|
|
return self.norm_f(x)
|
||
|
|
|
||
|
|
|
||
|
|
class Model(nn.Module):
|
||
|
|
def __init__(self, args: ModelArgs):
|
||
|
|
super().__init__()
|
||
|
|
self.args = args
|
||
|
|
self.model_type = args.model_type
|
||
|
|
|
||
|
|
self.backbone = Mamba2(args)
|
||
|
|
|
||
|
|
if not args.tie_word_embeddings:
|
||
|
|
self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
|
||
|
|
|
||
|
|
def __call__(self, inputs: mx.array, cache=None):
|
||
|
|
B, T = inputs.shape
|
||
|
|
|
||
|
|
x = self.backbone(inputs, cache)
|
||
|
|
|
||
|
|
if self.args.tie_word_embeddings:
|
||
|
|
logits = self.backbone.embeddings.as_linear(x)
|
||
|
|
else:
|
||
|
|
logits = self.lm_head(x)
|
||
|
|
|
||
|
|
return logits
|
||
|
|
|
||
|
|
def make_cache(self, batch_size=1):
|
||
|
|
return [Mamba2Cache(
|
||
|
|
batch_size,
|
||
|
|
self.args.intermediate_size,
|
||
|
|
self.args.conv_kernel,
|
||
|
|
self.args.head_dim,
|
||
|
|
self.args.num_heads,
|
||
|
|
self.args.n_groups,
|
||
|
|
self.args.state_size
|
||
|
|
) for _ in range(len(self.layers))]
|
||
|
|
|
||
|
|
def sanitize(self, weights):
|
||
|
|
for k, v in weights.items():
|
||
|
|
if "conv1d.weight" in k and v.ndim == 3:
|
||
|
|
weights[k] = v.moveaxis(2, 1)
|
||
|
|
return weights
|
||
|
|
|
||
|
|
@property
|
||
|
|
def layers(self):
|
||
|
|
return self.backbone.layers
|