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Goekdeniz-Guelmez
@@ -1,424 +0,0 @@
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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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def selective_scan(x, A, B, C, chunk_size):
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"""
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Selective scan implementation for training.
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Arguments
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x: (batch, seqlen, n_heads, d_head)
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A: (batch, seqlen, n_heads)
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B: (batch, seqlen, n_heads, d_state)
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C: (batch, seqlen, n_heads, d_state)
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Return
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y: (batch, seqlen, n_heads, d_head)
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"""
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assert x.shape[1] % chunk_size == 0
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# Reshape into chunks
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def chunk_reshape(m):
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shape = list(m.shape)
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shape[1:2] = [shape[1] // chunk_size, chunk_size]
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return m.reshape(shape)
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x, A, B, C = map(chunk_reshape, (x, A, B, C))
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A = mx.transpose(A, [0, 3, 1, 2])
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# Compute cumulative sums
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A_cumsum = mx.cumsum(A, axis=-1)
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# Process chunks
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L = mx.exp(selective_cumsum(A))
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Y_diag = mx.einsum('bclhn,bcshn,bhcls,bcshp->bclhp', C, B, L, x)
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decay_states = mx.exp(A_cumsum[..., -1:] - A_cumsum)
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states = mx.einsum('bclhn,bhcl,bclhp->bchpn', B, decay_states, x)
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initial_states = mx.zeros_like(states[:, :1])
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states = mx.concatenate([initial_states, states], axis=1)
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decay_chunk = mx.exp(selective_cumsum(mx.pad(A_cumsum[..., -1], ((0,0), (0,0), (1,0)))))
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new_states = mx.einsum('bhzc,bchpn->bzhpn', decay_chunk, states)
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states = new_states[:, :-1]
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state_decay_out = mx.exp(A_cumsum)
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Y_off = mx.einsum('bclhn,bchpn,bhcl->bclhp', C, states, state_decay_out)
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Y = (Y_diag + Y_off).reshape((-1, x.shape[1] * chunk_size, *Y_diag.shape[-2:]))
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return Y
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def selective_cumsum(x: mx.array) -> mx.array:
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"""Stable selective cumulative sum calculation."""
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T = x.shape[-1]
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x = mx.repeat(x[..., None], T, axis=-1)
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mask = mx.tril(mx.ones((T, T)), k=-1)
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x = x * mask
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x_cumsum = mx.cumsum(x, axis=-2)
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mask = mx.tril(mx.ones((T, T)), k=0)
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return mx.where(mask, x_cumsum, float('-inf'))
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class Mamba2Block(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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# Project input to get various components [z, x, B, C, dt]
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projection_size = (2 * args.intermediate_size + 2 * args.n_groups * args.state_size + args.num_heads)
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self.in_proj = nn.Linear(
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args.hidden_size,
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projection_size,
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bias=args.use_bias
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)
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# Convolution layer
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conv_dim = args.intermediate_size + 2 * args.n_groups * args.state_size
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self.conv1d = nn.Conv1d(
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in_channels=conv_dim,
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out_channels=conv_dim,
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kernel_size=args.conv_kernel,
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groups=conv_dim,
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padding=args.conv_kernel - 1,
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bias=args.use_conv_bias
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)
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# SSM parameters
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self.dt_bias = mx.zeros(args.num_heads)
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self.A_log = mx.zeros(args.num_heads)
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self.D = mx.ones(args.num_heads)
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# Output projections
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self.norm = nn.RMSNorm(args.intermediate_size, eps=args.layer_norm_epsilon)
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self.out_proj = nn.Linear(args.intermediate_size, args.hidden_size, bias=args.use_bias)
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def __call__(self, u: mx.array, cache=None) -> mx.array:
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# return self.forward_training(x) if x.shape[1] > 1 else self.forward_inference(x, cache)
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# def forward_training(self, u: mx.array) -> mx.array:
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# # Reset cache during training
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# self.cache = None
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# # Input projection and splitting
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# zxbcdt = self.in_proj(u)
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# z, xBC, dt = mx.split(
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# zxbcdt,
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# [
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# self.args.hidden_size,
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# self.args.hidden_size + 2 * self.args.state_size
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# ],
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# axis=-1
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# )
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# # Time step processing
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# dt = mx.clip(
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# nn.softplus(dt + self.dt_bias),
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# self.args.time_step_min,
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# self.args.time_step_max
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# )
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# # Convolution processing
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# xBC_t = mx.transpose(xBC, [0, 2, 1])
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# conv_out = self.conv1d(xBC_t)
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# xBC = mx.transpose(conv_out, [0, 2, 1])[:, :u.shape[1]]
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# xBC = mx.sigmoid(xBC) * xBC # SiLU
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# # Split states
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# x, B, C = mx.split(
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# xBC,
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# [self.args.hidden_size, self.args.state_size],
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# axis=-1
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# )
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# # Reshape for selective scan
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# x = x.reshape((-1, x.shape[1], self.args.num_heads, self.args.head_dim))
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# A = -mx.exp(self.A_log)
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# # Apply selective scan
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# y = selective_scan(
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# x * dt[..., None],
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# A * dt,
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# B[..., None, :],
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# C[..., None, :],
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# self.args.chunk_size
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# )
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# # Output processing
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# y = y + x * self.D[None, None, :, None]
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# y = y.reshape((-1, y.shape[1], self.args.hidden_size))
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# y = self.norm(y, z)
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# y = self.out_proj(y)
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# return y
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# def forward_inference(self, u: mx.array, cache=None) -> mx.array:
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# """
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# u: (B, 1, D)
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# cache: (h_cache, conv_cache)
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# """
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# """Single token processing during inference."""
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# assert u.shape[1] == 1, "Inference mode expects single token"
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# batch_size = u.shape[0]
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# # Use provided cache or create new one
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# self.cache = cache if cache is not None else Mamba2Cache.get_cache(self.args, batch_size, None)
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# # Project input
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# zxbcdt = self.in_proj(u.squeeze(1)) # (B, 2D)
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# d_mlp = (zxbcdt.shape[-1] - 2 * self.args.hidden_size - 2 * self.args.n_groups * self.args.state_size - self.args.num_heads) // 2
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# # (1, 768) (1, 0) (1, 0) (1, 256) (1, 0) (1, 3328)
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# y0, z0, x0, z, xBC, dt = mx.split(
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# zxbcdt,
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# [
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# d_mlp,
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# d_mlp,
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# self.args.hidden_size,
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# self.args.hidden_size + 2 * self.args.n_groups * self.args.state_size,
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# self.args.num_heads
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# ],
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# axis=-1
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# )
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# # Update convolution state and apply
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# conv_state = self.cache.update_conv_state(xBC)
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# xBC = mx.sum(conv_state[:, :, -1] * mx.transpose(self.conv1d.weight, [1, 0, 2]), axis=-1) # (B, D) (4, 1792)
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# if self.args.use_conv_bias:
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# xBC = xBC + self.conv1d.bias
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# xBC = mx.sigmoid(xBC) * xBC # SiLU (4, 1792)
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# # Split states and ensure proper shapes
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# a0, x, B, C = mx.split(
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# xBC, # (4, 1792)
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# [
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# self.args.hidden_size,
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# self.args.n_groups * self.args.state_size,
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# self.args.n_groups * self.args.state_size
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# ],
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# axis=-1
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# )
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# # SSM step with explicit shapes
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# A = -mx.exp(self.A_log) # (num_heads) (24,)
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# print(A.shape) # (24,)
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# print(dt.shape) # (1, 3328)
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# dA = mx.exp(dt * A[None, :]) # Shape: (batch_size, num_heads) <------- her eis the error
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# # Reshape x considering intermediate size
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# # x shape should be (batch_size * num_heads, head_dim)
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# x = mx.reshape(x, (batch_size, self.args.num_heads, -1))
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# assert x.shape[-1] == self.args.head_dim, f"Head dimension mismatch: {x.shape[-1]} vs {self.args.head_dim}"
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# B = mx.reshape(B, (batch_size, -1)) # Should be (batch_size, state_size)
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# C = mx.reshape(C, (batch_size, -1)) # Should be (batch_size, state_size)
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# # Compute dBx with explicit shapes
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# dBx = mx.einsum('bh,bs,bhd->bhds', dt, B, x)
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# ssm_state = self.cache.update_ssm_state(dA, dBx)
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# y = mx.einsum('bhds,bs->bhd', ssm_state, C)
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# y = y + x * self.D[None, :, None]
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# y = mx.reshape(y, (batch_size, self.args.hidden_size))
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# # Output processing
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# y = self.norm(y, z)
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# if d_mlp > 0:
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# y = mx.cat([nn.silu(z0) * x0, y], axis=-1)
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# y = self.out_proj(y)
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# return mx.expand_dims(y, 1)
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assert u.shape[1] == 1, "Inference mode expects single token"
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batch_size = u.shape[0]
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# Use provided cache or create new one
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self.cache = cache if cache is not None else Mamba2Cache.get_cache(self.args, batch_size, None)
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# Project input
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zxbcdt = self.in_proj(u.squeeze(1)) # (B, projection_size)
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# Calculate splits based on model dimensions
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d_mlp = self.args.intermediate_size
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d_state = self.args.state_size * self.args.n_groups
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# Split the projection into its components
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splits = [
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d_mlp, # y0
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d_mlp, # z0
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self.args.hidden_size, # x0
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self.args.hidden_size, # z
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d_state * 2, # xBC (includes both B and C)
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self.args.num_heads # dt
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]
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y0, z0, x0, z, xBC, dt = mx.split(zxbcdt, splits[:-1], axis=-1)
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# Update convolution state and apply
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conv_state = self.cache.update_conv_state(xBC)
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xBC = mx.sum(conv_state[:, :, -1] * mx.transpose(self.conv1d.weight, [1, 0, 2]), axis=-1)
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if self.args.use_conv_bias:
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xBC = xBC + self.conv1d.bias
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xBC = mx.sigmoid(xBC) * xBC # SiLU
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# Split states and reshape
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x, BC = mx.split(xBC, [self.args.intermediate_size], axis=-1)
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B, C = mx.split(BC, [d_state], axis=-1)
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# Reshape for SSM computation
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x = mx.reshape(x, (batch_size, self.args.num_heads, -1)) # (B, H, head_dim)
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B = mx.reshape(B, (batch_size, self.args.num_heads, -1)) # (B, H, state_per_head)
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C = mx.reshape(C, (batch_size, self.args.num_heads, -1)) # (B, H, state_per_head)
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# Process dt to match expected shape
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dt = mx.reshape(dt, (batch_size, self.args.num_heads)) # (B, H)
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dt = mx.clip(
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nn.softplus(dt + self.dt_bias),
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self.args.time_step_min,
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self.args.time_step_max
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)
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# SSM step
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A = -mx.exp(self.A_log) # (H,)
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dA = mx.exp(dt * A[None, :]) # (B, H)
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# Compute dBx
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dBx = mx.einsum('bh,bhs,bhd->bhds', dt, B, x)
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# Update SSM state and compute output
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ssm_state = self.cache.update_ssm_state(dA, dBx)
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y = mx.einsum('bhds,bhs->bhd', ssm_state, C)
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y = y + x * self.D[None, :, None]
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# Reshape output
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y = mx.reshape(y, (batch_size, self.args.hidden_size))
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# Final output processing
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y = self.norm(y, z)
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if d_mlp > 0:
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y = mx.concat([nn.silu(z0) * x0, y], axis=-1)
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y = self.out_proj(y)
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return mx.expand_dims(y, 1) # (B, 1, D)
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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 = Mamba2Block(args)
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self.norm = nn.RMSNorm(args.hidden_size)
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def __call__(self, x: mx.array, cache=None) -> mx.array:
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# x : (B, L, D)
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return self.mixer(self.norm(x), cache) + x # (B, L, D)
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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 : (B, L)
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x = self.embeddings(x)
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# x : (B, L, D)
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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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# inputs : (B, L)
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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 [Mamba2Cache(
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batch_size=batch_size,
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hidden_size=self.args.hidden_size,
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state_size=self.args.state_size,
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conv_kernel=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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) for _ in range(len(self.backbone.layers))]
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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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