mirror of
https://github.com/ml-explore/mlx-examples.git
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fixing loading the model
This commit is contained in:
Goekdeniz-Guelmez
parent
264ba43707
commit
4e1236cbf6
1
llms/mamba2-130m-hf
Submodule
1
llms/mamba2-130m-hf
Submodule
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Subproject commit 05e8773fc4ac1cd067e8a18a5c45372ce5178405
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256
llms/mlx_lm/models/mamba2 copy.py
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256
llms/mlx_lm/models/mamba2 copy.py
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# Copyright © 2024 Apple Inc.
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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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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str = "mamba2"
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num_heads: int = 128
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head_dim: int = 64
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vocab_size: int = 32768
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hidden_size: int = 4096
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state_size: int = 128
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num_hidden_layers: int = 64
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layer_norm_epsilon: float = 1e-5
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pad_token_id: int = 1
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bos_token_id: int = 0
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eos_token_id: int = 2
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expand: int = 2
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conv_kernel: int = 4
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n_groups: int = 8
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use_bias: bool = False
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use_conv_bias: bool = True
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hidden_act: str = "silu"
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initializer_range: float = 0.1
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residual_in_fp32: bool = True
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time_step_rank: Union[int, str] = "auto"
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time_step_min: float = 0.001
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time_step_max: float = 0.1
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time_step_floor: float = 1e-4
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time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
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rescale_prenorm_residual: bool = False
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use_cache: bool = True
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rms_norm: bool = True
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chunk_size: int = 256
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tie_word_embeddings: bool = False
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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 Mamba2Cache:
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def __init__(self):
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self.cache = [None, None]
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def __setitem__(self, idx, value):
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self.cache[idx] = value
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def __getitem__(self, idx):
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return self.cache[idx]
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@property
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def state(self):
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return self.cache
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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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class DepthWiseConv1d(nn.Module):
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def __init__(self, channels, kernel_size, bias=True, groups=1, padding=0):
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super().__init__()
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self.channels = channels
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self.kernel_size = kernel_size
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self.padding = padding
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self.groups = groups
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self.weight = mx.random.normal((self.channels, kernel_size, 1))
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self.bias = mx.zeros((channels,)) if bias else None
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def __call__(self, x, cache=None):
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B, L, C = x.shape
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_, K, _ = self.weight.shape
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if cache is not None:
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x = mx.concatenate([cache, x], axis=1)
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else:
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x = mx.pad(x, [(0, 0), (K - 1, 0), (0, 0)])
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y = mx.conv_general(x, self.weight, groups=self.groups)
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if self.bias is not None:
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y = y + self.bias
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return y, x[:, -K + 1 :, :]
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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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self.args = args
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self.intermediate_size = args.intermediate_size
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self.time_step_rank = args.time_step_rank
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self.conv_kernel_size = args.conv_kernel
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self.hidden_size = args.hidden_size
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self.state_size = args.state_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.n_groups = args.n_groups
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self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size
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self.conv1d = DepthWiseConv1d(
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channels=self.conv_dim,
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kernel_size=self.conv_kernel_size,
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bias=self.args.use_conv_bias,
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groups=self.conv_dim,
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padding=self.conv_kernel_size - 1,
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)
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projection_size = self.intermediate_size + self.conv_dim + self.num_heads
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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.act = nn.SiLU()
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self.dt_bias = mx.ones((self.num_heads,))
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self.A_log = mx.log(mx.arange(1, self.num_heads + 1))
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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 ssm_step(self, x, state=None):
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A = -mx.exp(self.A_log)
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D = self.D
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deltaBC = self.x_proj(x)
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delta, B, C = mx.split(
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deltaBC,
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indices_or_sections=[
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self.time_step_rank,
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self.time_step_rank + self.ssm_state_size,
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],
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axis=-1,
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)
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delta = nn.softplus(self.dt_proj(delta))
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new_state = mx.expand_dims(delta * x, -1) * mx.expand_dims(B, 1)
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if state is not None:
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new_state += state * mx.exp(mx.expand_dims(delta, -1) * A)
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y = (new_state @ mx.expand_dims(C, -1)).squeeze(2)
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y = y + D * x
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return y, new_state
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def __call__(self, x, cache):
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B, T, D = x.shape
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if cache is None:
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cache = [None, None]
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outputs = []
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for t in range(T):
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xt = x[:, t, :]
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xz = self.in_proj(xt)
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x_t, z_t = xz.split(indices_or_sections=2, axis=1)
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conv_out, cache[0] = self.conv1d(mx.expand_dims(x_t, 1), cache[0])
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x_t = conv_out.squeeze(1)
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x_t = nn.silu(x_t)
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y_t, cache[1] = self.ssm_step(x_t, cache[1])
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z_t = nn.silu(z_t)
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output_t = y_t * z_t
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output_t = self.out_proj(output_t)
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outputs.append(output_t)
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output = mx.stack(outputs, axis=1)
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return output
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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.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):
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return self.mixer(self.norm(x), cache) + x
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class Mamba2(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 = [Mamba2Block(args) for idx 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__(
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self,
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inputs: mx.array,
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cache=None
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):
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hidden_states = self.embeddings(inputs)
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if cache is None:
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cache = Mamba2Cache(len(self.layers))
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for i, layer in enumerate(self.layers):
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hidden_states = layer(hidden_states, cache[i])
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hidden_states = self.norm_f(hidden_states)
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return hidden_states
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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.model_type = args.model_type
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self.backbone = Mamba2(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):
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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 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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def make_cache(self, batch_size: int = 1):
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return [Mamba2Cache() for _ in range(len(self.layers))]
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@property
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def layers(self):
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return self.backbone.layers
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275
llms/mlx_lm/models/mamba2-other.py
Normal file
275
llms/mlx_lm/models/mamba2-other.py
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# Copyright © 2024 Apple Inc.
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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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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str = "mamba2"
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num_heads: int = 128
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head_dim: int = 64
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vocab_size: int = 32768
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hidden_size: int = 4096
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state_size: int = 128
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num_hidden_layers: int = 64
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layer_norm_epsilon: float = 1e-5
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expand: int = 2
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conv_kernel: int = 4
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n_groups: int = 8
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use_bias: bool = False
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use_conv_bias: bool = True
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initializer_range: float = 0.1
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residual_in_fp32: bool = True
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time_step_rank: Union[int, str] = "auto"
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time_step_min: float = 0.001
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time_step_max: float = 0.1
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time_step_floor: float = 1e-4
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time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
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rescale_prenorm_residual: bool = False
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use_cache: bool = True
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rms_norm: bool = True
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chunk_size: int = 256
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tie_word_embeddings: bool = False
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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 Mamba2Cache:
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def __init__(self, num_layers):
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self.cache = [[None, None] for _ in range(num_layers)]
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def __getitem__(self, idx):
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return self.cache[idx]
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def __setitem__(self, idx, value):
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self.cache[idx] = value
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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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class Mamba2Mixer(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.intermediate_size = args.intermediate_size
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self.time_step_rank = args.time_step_rank
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self.conv_kernel_size = args.conv_kernel
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self.hidden_size = args.hidden_size
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self.state_size = args.state_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.n_groups = args.n_groups
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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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projection_size = self.intermediate_size + self.conv_dim + self.num_heads
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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.act = nn.SiLU()
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self.dt_bias = mx.ones((self.num_heads,))
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self.A_log = mx.log(mx.arange(1, self.num_heads + 1))
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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 ssm_step(self, x, dt, state):
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B, L, C = x.shape
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print(f"x shape: {x.shape}")
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projected_states = self.in_proj(x)
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print(f"deltaBC shape: {projected_states.shape}")
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d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.state_size - self.num_heads) // 2
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gate = projected_states[:, :, 2*d_mlp:2*d_mlp+self.intermediate_size]
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conv_state = projected_states[:, :, 2*d_mlp+self.intermediate_size:2*d_mlp+self.intermediate_size+self.conv_dim]
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time_step = projected_states[:, :, -self.num_heads:]
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print(f"conv_state shape before reshape: {conv_state.shape}")
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print(f"self.conv_dim: {self.conv_dim}")
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# Reshape and handle the case where L=1
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conv_state = conv_state.reshape(B, self.conv_dim, L)
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if L == 1:
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# If sequence length is 1, we need to pad to apply convolution
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conv_state = mx.pad(conv_state, ((0, 0), (0, 0), (0, self.conv_kernel_size - 1)))
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conv_out = self.conv1d(conv_state)
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# If we padded, we need to remove the padding
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if L == 1:
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conv_out = conv_out[:, :, :L]
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# Reshape back to (B, L, C)
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conv_out = conv_out.transpose(0, 2, 1)
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x_and_conv_out, B, C = mx.split(
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conv_out,
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[self.intermediate_size, self.n_groups * self.state_size],
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axis=-1
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)
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dt = nn.softplus(time_step + self.dt_bias)
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dt = mx.clip(dt, self.args.time_step_min, self.args.time_step_max)
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B = B.reshape(-1, self.num_heads, self.head_dim, self.state_size)
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C = C.reshape(-1, self.num_heads, self.head_dim, self.state_size)
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dA = mx.exp(dt[:, :, None, None] * A[None, :, None, None])
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dB = dt[:, :, None, None] * B
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new_state = state * dA + x_and_conv_out[:, :, None, None] * dB
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y = mx.sum(new_state * C, axis=-1)
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y = y + C[None, :, None] * x_and_conv_out
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y = self.norm(y.reshape(-1, self.intermediate_size), gate)
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output = self.out_proj(y)
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return output, new_state
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def __call__(
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self,
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x: mx.array,
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cache = None
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):
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B, L, _ = x.shape
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if cache[0] is not None: # Using cached state
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conv_state, ssm_state = cache
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x = x[:, -1:]
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output, new_ssm_state = self.ssm_step(x, None, ssm_state)
|
||||
cache[1] = new_ssm_state # Update SSM state in cache
|
||||
else:
|
||||
conv_state, ssm_state = None, None
|
||||
outputs = []
|
||||
for t in range(L):
|
||||
x = x[:, t:t+1]
|
||||
output, ssm_state = self.ssm_step(x, None, ssm_state)
|
||||
outputs.append(output)
|
||||
output = mx.concatenate(outputs, axis=1)
|
||||
cache[1] = ssm_state # Store final SSM state in cache
|
||||
|
||||
# Update conv state in cache
|
||||
new_conv_state = x[:, -self.conv_kernel_size:]
|
||||
cache[0] = new_conv_state
|
||||
|
||||
return output
|
||||
|
||||
|
||||
class Mamba2Block(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
self.args = args
|
||||
self.residual_in_fp32 = args.residual_in_fp32
|
||||
self.norm = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
|
||||
self.mixer = Mamba2Mixer(args)
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
inputs: mx.array,
|
||||
cache=None,
|
||||
):
|
||||
h = self.mixer(self.norm(inputs), cache=cache)
|
||||
r = inputs + h
|
||||
return r
|
||||
|
||||
|
||||
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 = [Mamba2Block(args) for idx in range(args.num_hidden_layers)]
|
||||
self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
inputs: mx.array,
|
||||
cache=None
|
||||
):
|
||||
hidden_states = self.embeddings(inputs)
|
||||
|
||||
if cache is None:
|
||||
cache = Mamba2Cache(len(self.layers))
|
||||
|
||||
for i, layer in enumerate(self.layers):
|
||||
hidden_states = layer(hidden_states, cache[i])
|
||||
|
||||
hidden_states = self.norm_f(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
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 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
|
||||
|
||||
def make_cache(self, batch_size: int = 1):
|
||||
return Mamba2Cache(len(self.backbone.layers))
|
||||
|
||||
@property
|
||||
def layers(self):
|
||||
return self.backbone.layers
|
||||
@ -2,7 +2,7 @@
|
||||
|
||||
import math
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Optional, Tuple, Union
|
||||
from typing import Tuple, Union
|
||||
|
||||
import mlx.core as mx
|
||||
import mlx.nn as nn
|
||||
@ -20,15 +20,11 @@ class ModelArgs(BaseModelArgs):
|
||||
state_size: int = 128
|
||||
num_hidden_layers: int = 64
|
||||
layer_norm_epsilon: float = 1e-5
|
||||
pad_token_id: int = 1
|
||||
bos_token_id: int = 0
|
||||
eos_token_id: int = 2
|
||||
expand: int = 2
|
||||
conv_kernel: int = 4
|
||||
n_groups: int = 8
|
||||
use_bias: bool = False
|
||||
use_conv_bias: bool = True
|
||||
hidden_act: str = "silu"
|
||||
initializer_range: float = 0.1
|
||||
residual_in_fp32: bool = True
|
||||
time_step_rank: Union[int, str] = "auto"
|
||||
@ -52,14 +48,18 @@ class ModelArgs(BaseModelArgs):
|
||||
|
||||
|
||||
class Mamba2Cache:
|
||||
def __init__(self, num_layers):
|
||||
self.cache = [[None, None] for _ in range(num_layers)]
|
||||
def __init__(self):
|
||||
self.cache = [None, None]
|
||||
|
||||
def __setitem__(self, idx, value):
|
||||
self.cache[idx] = value
|
||||
|
||||
def __getitem__(self, idx):
|
||||
return self.cache[idx]
|
||||
|
||||
def __setitem__(self, idx, value):
|
||||
self.cache[idx] = value
|
||||
@property
|
||||
def state(self):
|
||||
return self.cache
|
||||
|
||||
|
||||
class MambaRMSNormGated(nn.Module):
|
||||
@ -74,67 +74,54 @@ class MambaRMSNormGated(nn.Module):
|
||||
variance = mx.mean(hidden_states ** 2, axis=-1, keepdims=True)
|
||||
hidden_states = hidden_states * mx.rsqrt(variance + self.variance_epsilon)
|
||||
return self.weight * hidden_states
|
||||
|
||||
|
||||
class DepthWiseConv1d(nn.Module):
|
||||
def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0):
|
||||
super().__init__()
|
||||
assert in_channels == out_channels, "For depthwise conv, in_channels must equal out_channels"
|
||||
self.channels = in_channels
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.padding = padding
|
||||
|
||||
# For depthwise conv, we use groups equal to the number of channels
|
||||
self.groups = self.channels if groups is None else groups
|
||||
assert self.groups == self.channels, "For depthwise conv, groups must equal the number of channels"
|
||||
self.groups = groups if groups is not None else in_channels
|
||||
|
||||
# Weight shape: (channels, 1, kernel_size) for depthwise conv
|
||||
self.weight = mx.random.normal((self.channels, 1, kernel_size))
|
||||
self.bias = mx.zeros((self.channels,)) if bias else None
|
||||
# Ensure in_channels and out_channels are the same for depthwise conv
|
||||
assert in_channels == out_channels, "In and out channels must be the same for depthwise convolution"
|
||||
# Ensure groups is equal to in_channels for depthwise conv
|
||||
assert self.groups == in_channels, "Groups must be equal to in_channels for depthwise convolution"
|
||||
|
||||
# Initialize weight with shape (out_channels, kernel_size, 1)
|
||||
self.weight = mx.random.normal((out_channels, kernel_size, 1))
|
||||
self.bias = mx.zeros((out_channels,)) if bias else None
|
||||
|
||||
def __call__(self, x, cache=None):
|
||||
B, L, C = x.shape
|
||||
K = self.kernel_size
|
||||
_, K, _ = self.weight.shape
|
||||
|
||||
if cache is not None:
|
||||
x = mx.concatenate([cache, x], axis=1)
|
||||
else:
|
||||
x = mx.pad(x, [(0, 0), (self.padding, 0), (0, 0)])
|
||||
x = mx.pad(x, [(0, 0), (K - 1, 0), (0, 0)])
|
||||
|
||||
# Reshape for depthwise convolution
|
||||
x = x.transpose(0, 2, 1) # (B, C, L)
|
||||
|
||||
# Perform depthwise convolution
|
||||
y = mx.conv(x, self.weight, groups=self.groups)
|
||||
|
||||
# Reshape back
|
||||
y = y.transpose(0, 2, 1) # (B, L, C)
|
||||
y = mx.conv_general(x, self.weight, groups=self.groups)
|
||||
|
||||
if self.bias is not None:
|
||||
y = y + self.bias
|
||||
|
||||
return y, x.transpose(0, 2, 1)[:, -K:, :]
|
||||
return y, x[:, -K + 1 :, :]
|
||||
|
||||
|
||||
class Mamba2Mixer(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
self.args = args
|
||||
self.hidden_size = args.hidden_size
|
||||
self.intermediate_size = args.intermediate_size
|
||||
self.time_step_rank = args.time_step_rank
|
||||
self.conv_kernel_size = args.conv_kernel
|
||||
self.hidden_size = args.hidden_size
|
||||
self.state_size = args.state_size
|
||||
self.num_heads = args.num_heads
|
||||
self.head_dim = args.head_dim
|
||||
self.n_groups = args.n_groups
|
||||
self.time_step_rank = args.time_step_rank
|
||||
|
||||
projection_size = self.intermediate_size + self.intermediate_size + 2 * self.n_groups * self.state_size + self.num_heads
|
||||
self.in_proj = nn.Linear(
|
||||
self.hidden_size,
|
||||
projection_size,
|
||||
bias=args.use_bias
|
||||
)
|
||||
|
||||
self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size
|
||||
self.conv1d = DepthWiseConv1d(
|
||||
@ -143,32 +130,74 @@ class Mamba2Mixer(nn.Module):
|
||||
bias=args.use_conv_bias,
|
||||
kernel_size=args.conv_kernel,
|
||||
groups=self.conv_dim,
|
||||
padding=args.conv_kernel - 1,
|
||||
padding=args.conv_kernel - 1
|
||||
)
|
||||
|
||||
projection_size = self.intermediate_size + self.conv_dim + self.num_heads
|
||||
self.in_proj = nn.Linear(
|
||||
self.hidden_size,
|
||||
projection_size,
|
||||
bias=args.use_bias
|
||||
)
|
||||
|
||||
self.act = nn.SiLU()
|
||||
self.dt_bias = mx.ones((self.num_heads,))
|
||||
self.A_log = mx.log(mx.arange(1, self.num_heads + 1, dtype=mx.float32))
|
||||
self.A_log = mx.log(mx.arange(1, self.num_heads + 1))
|
||||
self.D = mx.ones((self.num_heads,))
|
||||
|
||||
self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
|
||||
self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon)
|
||||
|
||||
self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
|
||||
|
||||
# def ssm_step(self, x, state=None):
|
||||
# A = -mx.exp(self.A_log)
|
||||
# D = self.D
|
||||
# deltaBC = self.x_proj(x)
|
||||
# delta, B, C = mx.split(
|
||||
# deltaBC,
|
||||
# indices_or_sections=[
|
||||
# self.time_step_rank,
|
||||
# self.time_step_rank + self.ssm_state_size,
|
||||
# ],
|
||||
# axis=-1,
|
||||
# )
|
||||
# delta = nn.softplus(self.dt_proj(delta))
|
||||
# new_state = mx.expand_dims(delta * x, -1) * mx.expand_dims(B, 1)
|
||||
# if state is not None:
|
||||
# new_state += state * mx.exp(mx.expand_dims(delta, -1) * A)
|
||||
# y = (new_state @ mx.expand_dims(C, -1)).squeeze(2)
|
||||
# y = y + D * x
|
||||
# return y, new_state
|
||||
|
||||
def ssm_step(self, x, dt, state):
|
||||
A = -mx.exp(self.A_log)
|
||||
D = self.D
|
||||
B, L, C = x.shape
|
||||
print(f"x shape: {x.shape}")
|
||||
projected_states = self.in_proj(x)
|
||||
print(f"deltaBC shape: {projected_states.shape}")
|
||||
|
||||
deltaBC = self.in_proj(x)
|
||||
gate, conv_state, time_step = mx.split(
|
||||
deltaBC,
|
||||
[self.intermediate_size, self.intermediate_size + 2 * self.n_groups * self.state_size],
|
||||
axis=-1
|
||||
)
|
||||
d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.state_size - self.num_heads) // 2
|
||||
|
||||
conv_state = conv_state.transpose(0, 2, 1)
|
||||
gate = projected_states[:, :, 2*d_mlp:2*d_mlp+self.intermediate_size]
|
||||
conv_state = projected_states[:, :, 2*d_mlp+self.intermediate_size:2*d_mlp+self.intermediate_size+self.conv_dim]
|
||||
time_step = projected_states[:, :, -self.num_heads:]
|
||||
|
||||
print(f"conv_state shape before reshape: {conv_state.shape}")
|
||||
print(f"self.conv_dim: {self.conv_dim}")
|
||||
|
||||
# Reshape and handle the case where L=1
|
||||
conv_state = conv_state.reshape(B, self.conv_dim, L)
|
||||
if L == 1:
|
||||
# If sequence length is 1, we need to pad to apply convolution
|
||||
conv_state = mx.pad(conv_state, ((0, 0), (0, 0), (0, self.conv_kernel_size - 1)))
|
||||
|
||||
conv_out = self.conv1d(conv_state)
|
||||
|
||||
# If we padded, we need to remove the padding
|
||||
if L == 1:
|
||||
conv_out = conv_out[:, :, :L]
|
||||
|
||||
# Reshape back to (B, L, C)
|
||||
conv_out = conv_out.transpose(0, 2, 1)
|
||||
conv_out = self.act(conv_out)
|
||||
|
||||
x_and_conv_out, B, C = mx.split(
|
||||
conv_out,
|
||||
@ -187,58 +216,47 @@ class Mamba2Mixer(nn.Module):
|
||||
|
||||
new_state = state * dA + x_and_conv_out[:, :, None, None] * dB
|
||||
y = mx.sum(new_state * C, axis=-1)
|
||||
y = y + D[None, :, None] * x_and_conv_out
|
||||
y = y + C[None, :, None] * x_and_conv_out
|
||||
|
||||
y = self.norm(y.reshape(-1, self.intermediate_size), gate)
|
||||
output = self.out_proj(y)
|
||||
|
||||
return output, new_state
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
x: mx.array,
|
||||
cache = None
|
||||
):
|
||||
B, L, _ = x.shape
|
||||
def __call__(self, x, cache):
|
||||
B, T, D = x.shape
|
||||
if cache is None:
|
||||
cache = [None, None]
|
||||
|
||||
if cache[0] is not None: # Using cached state
|
||||
conv_state, ssm_state = cache
|
||||
x = x[:, -1:]
|
||||
output, new_ssm_state = self.ssm_step(x, None, ssm_state)
|
||||
cache[1] = new_ssm_state # Update SSM state in cache
|
||||
else:
|
||||
conv_state, ssm_state = None, None
|
||||
outputs = []
|
||||
for t in range(L):
|
||||
x = x[:, t:t+1]
|
||||
output, ssm_state = self.ssm_step(x, None, ssm_state)
|
||||
outputs.append(output)
|
||||
output = mx.concatenate(outputs, axis=1)
|
||||
cache[1] = ssm_state # Store final SSM state in cache
|
||||
outputs = []
|
||||
for t in range(T):
|
||||
xt = x[:, t, :]
|
||||
xz = self.in_proj(xt)
|
||||
x_t, z_t = xz.split(indices_or_sections=2, axis=1)
|
||||
|
||||
# Update conv state in cache
|
||||
new_conv_state = x[:, -self.conv_kernel_size:]
|
||||
cache[0] = new_conv_state
|
||||
if x_t.shape[-1] != self.conv_dim:
|
||||
raise ValueError(f"Expected conv input dim {self.conv_dim}, got {x_t.shape[-1]}")
|
||||
|
||||
conv_out, cache[0] = self.conv1d(mx.expand_dims(x_t, 1), cache[0])
|
||||
x_t = conv_out.squeeze(1)
|
||||
x_t = nn.silu(x_t)
|
||||
y_t, cache[1] = self.ssm_step(x_t, cache[1])
|
||||
z_t = nn.silu(z_t)
|
||||
output_t = y_t * z_t
|
||||
output_t = self.out_proj(output_t)
|
||||
outputs.append(output_t)
|
||||
output = mx.stack(outputs, axis=1)
|
||||
return output
|
||||
|
||||
|
||||
class Mamba2Block(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
self.args = args
|
||||
self.residual_in_fp32 = args.residual_in_fp32
|
||||
self.norm = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
|
||||
self.mixer = Mamba2Mixer(args)
|
||||
self.norm = nn.RMSNorm(args.hidden_size)
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
inputs: mx.array,
|
||||
cache=None,
|
||||
):
|
||||
h = self.mixer(self.norm(inputs), cache_params=cache)
|
||||
r = inputs + h
|
||||
return r
|
||||
def __call__(self, x: mx.array, cache):
|
||||
return self.mixer(self.norm(x), cache) + x
|
||||
|
||||
|
||||
class Mamba2(nn.Module):
|
||||
@ -275,11 +293,7 @@ class Model(nn.Module):
|
||||
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
|
||||
):
|
||||
def __call__(self, inputs: mx.array, cache=None):
|
||||
B, T = inputs.shape
|
||||
|
||||
x = self.backbone(inputs, cache)
|
||||
@ -288,17 +302,18 @@ class Model(nn.Module):
|
||||
logits = self.backbone.embeddings.as_linear(x)
|
||||
else:
|
||||
logits = self.lm_head(x)
|
||||
|
||||
return logits
|
||||
|
||||
def sanitize_mabey(self, weights):
|
||||
|
||||
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
|
||||
|
||||
|
||||
def make_cache(self, batch_size: int = 1):
|
||||
return Mamba2Cache(len(self.backbone.layers))
|
||||
|
||||
return [Mamba2Cache() for _ in range(len(self.layers))]
|
||||
|
||||
@property
|
||||
def layers(self):
|
||||
return self.backbone.layers
|
||||
|
||||
Loading…
Reference in New Issue
Block a user