mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-26 02:33:23 +08:00
362 lines
12 KiB
Python
362 lines
12 KiB
Python
import math
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from dataclasses import dataclass, field
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from typing import Optional, 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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use_cache: 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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intermediate_size: int = None
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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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self.intermediate_size = int(self.expand * self.hidden_size) # E*D = ED
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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 forward(self, hidden_states, gate=None):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(mx.float32)
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if gate is not None:
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hidden_states = hidden_states * nn.functional.silu(gate.to(mx.float32))
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * math.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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class Mamba2Mixer(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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# Model dimensions
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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.ssm_state_size = args.state_size
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self.n_groups = args.n_groups
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self.intermediate_size = int(args.expand * args.hidden_size)
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# Convolution parameters
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self.conv_kernel = args.conv_kernel
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self.use_conv_bias = args.use_conv_bias
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# Time step parameters
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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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# Processing parameters
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self.chunk_size = args.chunk_size
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self.layer_norm_epsilon = args.layer_norm_epsilon
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# Calculate dimensions
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self.conv_dim = (self.intermediate_size +
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2 * self.n_groups * self.ssm_state_size)
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projection_size = (self.intermediate_size +
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self.conv_dim +
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self.num_heads)
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# Initialize layers
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self.in_proj = nn.Linear(
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self.hidden_size,
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projection_size,
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bias=args.use_bias
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)
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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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kernel_size=self.conv_kernel,
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groups=self.conv_dim,
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padding=self.conv_kernel - 1,
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bias=self.use_conv_bias
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)
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# Initialize parameters
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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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# Output layers
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self.norm = MambaRMSNormGated(
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self.intermediate_size,
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eps=self.layer_norm_epsilon
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)
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self.out_proj = nn.Linear(
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self.intermediate_size,
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self.hidden_size,
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bias=args.use_bias
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)
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def reshape_into_chunks(self, tensor, pad_size, chunk_size):
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if pad_size > 0:
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pad_shape = list(tensor.shape)
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pad_shape[1] = pad_size
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padding = mx.zeros(pad_shape, dtype=tensor.dtype)
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tensor = mx.concatenate([tensor, padding], axis=1)
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chunk_shape = list(tensor.shape)
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chunk_shape[1] = -1
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chunk_shape.insert(2, chunk_size)
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return tensor.reshape(chunk_shape)
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def segment_sum(self, x):
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return mx.cumsum(x, axis=-1)
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def process_single_token(self, hidden_states, B, C, dt, cache):
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batch_size = hidden_states.shape[0]
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# Process convolution state
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if cache is not None:
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conv_state = cache.conv_states
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# Roll the conv state and update the last position
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conv_state = mx.roll(conv_state, shift=-1, axis=-1)
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# Create new conv state with updated last position
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new_conv_state = mx.array(conv_state)
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new_conv_state = new_conv_state.at[:, :, -1].add(hidden_states)
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conv_state = new_conv_state
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# Compute convolution
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conv_out = mx.sum(conv_state * self.conv1d.weight[:, 0, :], axis=-1)
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if self.use_conv_bias:
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conv_out = conv_out + self.conv1d.bias
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# Apply SiLU activation
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conv_out = mx.sigmoid(conv_out) * conv_out
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else:
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# Initialize new cache
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conv_state = mx.zeros((batch_size, self.conv_dim, self.conv_kernel - 1))
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conv_out = self.conv1d(hidden_states)
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conv_out = mx.sigmoid(conv_out) * conv_out
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# Process SSM
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dt = mx.clip(
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nn.softplus(dt + self.dt_bias),
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self.time_step_min,
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self.time_step_max
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)
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A = -mx.exp(self.A_log)
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dA = mx.exp(dt * A[None, :])
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if cache is not None:
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ssm_state = cache.ssm_states
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else:
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ssm_state = mx.zeros(
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(batch_size, self.num_heads, self.head_dim, self.ssm_state_size)
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)
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# Compute SSM updates
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dBx = mx.einsum('bh,bhs,bhd->bhds', dt, B, hidden_states)
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next_state = ssm_state * dA[:, :, None, None] + dBx
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y = mx.einsum('bhds,bhs->bhd', next_state, C)
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# Add skip connection
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y = y + hidden_states * self.D[None, :, None]
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return y, conv_state, next_state
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def process_long_sequence(self, hidden_states, B, C, dt, ssm_state):
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batch_size, seq_len = hidden_states.shape[:2]
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pad_size = self.chunk_size - (seq_len % self.chunk_size)
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# Reshape into chunks
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x_chunks = self.reshape_into_chunks(hidden_states, pad_size, self.chunk_size)
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B_chunks = self.reshape_into_chunks(B, pad_size, self.chunk_size)
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C_chunks = self.reshape_into_chunks(C, pad_size, self.chunk_size)
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# Process time steps
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dt = nn.softplus(dt + self.dt_bias)
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dt = mx.clip(dt, self.time_step_min)
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# Prepare matrices
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A = -mx.exp(self.A_log)
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A = A * dt[:, None]
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# Process chunks
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A_chunks = self.reshape_into_chunks(
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mx.broadcast_to(A, (batch_size, seq_len + pad_size, self.num_heads)),
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pad_size,
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self.chunk_size
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)
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# Compute cumulative sums
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A_cumsum = mx.cumsum(A_chunks, axis=-1)
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L = mx.exp(self.segment_sum(A_chunks))
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# Process diagonal blocks
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G = mx.einsum('...lhn,...shn->...lsh', C_chunks, B_chunks)
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M = G * L[..., None, :]
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Y_diag = mx.einsum('...lsh,...sh->...lh', M, x_chunks)
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# Process off-diagonal blocks
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decay_states = mx.exp(A_cumsum[..., -1:] - A_cumsum)
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B_decay = B_chunks * decay_states[..., None]
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states = mx.einsum('...shn,...sh->...hn', B_decay, x_chunks)
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# Combine results
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y = Y_diag + states
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# Remove padding if necessary
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if pad_size > 0:
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y = y[:, :seq_len]
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return y, ssm_state
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def __call__(self, x: mx.array, cache: Optional[Mamba2Cache] = None) -> mx.array:
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batch_size, seq_len, _ = x.shape
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# Project input
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projected_states = self.in_proj(x.squeeze(1))
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# Calculate d_mlp based on projection size
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d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 *
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self.n_groups * self.ssm_state_size - self.num_heads) // 2
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# Split projections with corrected dimensions
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splits = [
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d_mlp, # z0
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d_mlp, # x0
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self.intermediate_size, # gate
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self.conv_dim, # hidden_states
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self.num_heads # dt
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]
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z0, x0, x1, gate, hidden_states, dt = projected_states.split(splits, axis=-1)
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# Split hidden states into components
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x_conv, BC = mx.split(hidden_states, [self.intermediate_size], axis=-1)
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B, C = mx.split(BC, [self.n_groups * self.ssm_state_size], axis=-1)
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# Process based on sequence length
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if seq_len > 1 and cache is None:
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y, next_state = self.process_long_sequence(
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x_conv, B, C, dt,
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mx.zeros((batch_size, self.num_heads, self.head_dim, self.ssm_state_size))
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)
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else:
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# Reshape for single token processing
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x_conv = x_conv.reshape(batch_size, -1, self.head_dim)
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B = B.reshape(batch_size, self.num_heads, -1)
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C = C.reshape(batch_size, self.num_heads, -1)
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y, conv_state, next_state = self.process_single_token(x_conv, B, C, dt, cache)
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if cache is not None:
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cache.update(conv_state, next_state)
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# Apply normalization and final projection
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y = self.norm(y) * gate
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return self.out_proj(y)
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class ResidualBlock(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.mixer = Mamba2Mixer(args)
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self.norm = nn.RMSNorm(args.hidden_size)
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def __call__(self, x: mx.array, cache: Optional[Mamba2Cache] = None) -> mx.array:
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return self.mixer(self.norm(x), cache) + x
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class Mamba2Model(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.args = args
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self.embeddings = nn.Embedding(args.vocab_size, args.hidden_size)
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self.layers = [ResidualBlock(args) for _ in range(args.num_hidden_layers)]
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self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
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def __call__(self, x: mx.array, cache=None) -> mx.array:
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x = self.embeddings(x)
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if cache is None:
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cache = [None] * len(self.layers)
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for layer, layer_cache in zip(self.layers, cache):
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x = layer(x, layer_cache)
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return self.norm_f(x)
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class Model(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.args = args
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self.backbone = Mamba2Model(args)
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if not args.tie_word_embeddings:
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self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
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def __call__(self, inputs: mx.array, cache=None) -> mx.array:
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B, T = inputs.shape
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x = self.backbone(inputs, cache)
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if self.args.tie_word_embeddings:
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logits = self.backbone.embeddings.as_linear(x)
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else:
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logits = self.lm_head(x)
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return logits
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def make_cache(self, batch_size=1):
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return [
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Mamba2Cache(
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batch_size=batch_size,
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conv_dim=self.args.intermediate_size + 2 * self.args.n_groups * self.args.state_size,
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kernel_size=self.args.conv_kernel,
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num_heads=self.args.num_heads,
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head_dim=self.args.head_dim,
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state_size=self.args.state_size
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)
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for _ in range(len(self.backbone.layers))
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]
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def sanitize(self, weights):
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for k, v in weights.items():
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if "conv1d.weight" in k and v.ndim == 3:
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weights[k] = v.moveaxis(2, 1)
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return weights
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@property
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def layers(self):
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return self.backbone.layers
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