mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-27 03:05:20 +08:00
276 lines
8.8 KiB
Python
276 lines
8.8 KiB
Python
# 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)
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cache[1] = new_ssm_state # Update SSM state in cache
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else:
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conv_state, ssm_state = None, None
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outputs = []
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for t in range(L):
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x = x[:, t:t+1]
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output, ssm_state = self.ssm_step(x, None, ssm_state)
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outputs.append(output)
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output = mx.concatenate(outputs, axis=1)
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cache[1] = ssm_state # Store final SSM state in cache
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# Update conv state in cache
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new_conv_state = x[:, -self.conv_kernel_size:]
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cache[0] = new_conv_state
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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.args = args
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self.residual_in_fp32 = args.residual_in_fp32
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self.norm = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
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self.mixer = Mamba2Mixer(args)
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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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h = self.mixer(self.norm(inputs), cache=cache)
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r = inputs + h
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return r
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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__(
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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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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(len(self.backbone.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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