mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-26 02:33:23 +08:00
315 lines
11 KiB
Python
315 lines
11 KiB
Python
import math
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from dataclasses import dataclass, field
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from typing import Tuple, Union
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import mlx.core as mx
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import mlx.nn as nn
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from .base import BaseModelArgs
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from .cache import Mamba2Cache
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@dataclass
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class ModelArgs(BaseModelArgs):
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num_heads: int
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head_dim: int
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vocab_size: int
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hidden_size: int
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state_size: int
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num_hidden_layers: int
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layer_norm_epsilon: float
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expand: int
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conv_kernel: int
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n_groups: int
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use_bias: bool
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use_conv_bias: bool
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initializer_range: float
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residual_in_fp32: bool
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time_step_min: float
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time_step_max: float
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time_step_floor: float
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rescale_prenorm_residual: bool
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rms_norm: bool
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chunk_size: int
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tie_word_embeddings: bool
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use_cache: bool = True
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time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
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time_step_rank: Union[int, str] = "auto"
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model_type: str = "mamba2"
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def __post_init__(self):
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if not hasattr(self, "intermediate_size"):
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self.intermediate_size = int(self.expand * self.hidden_size)
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if not hasattr(self, "head_dim"):
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self.head_dim = self.hidden_size // self.num_heads
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if self.time_step_rank == "auto":
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self.time_step_rank = math.ceil(self.hidden_size / 16)
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class MambaRMSNormGated(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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super().__init__()
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self.weight = mx.ones((hidden_size,))
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self.variance_epsilon = eps
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def __call__(self, hidden_states, gate=None):
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if gate is not None:
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hidden_states = hidden_states * nn.silu(gate)
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variance = mx.mean(hidden_states ** 2, axis=-1, keepdims=True)
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hidden_states = hidden_states * mx.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states
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def silu(x):
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return x * mx.sigmoid(x)
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def ssd(x, A, B, C, chunk_size):
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# Replace einsum operations with explicit reshape and matrix multiply
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batch, seqlen, nheads, dim = x.shape
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B = mx.expand_dims(B, axis=2)
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C = mx.expand_dims(C, axis=2)
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state = mx.zeros((batch, nheads, dim, B.shape[-1]))
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outputs = []
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for i in range(0, seqlen, chunk_size):
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chunk = slice(i, min(i + chunk_size, seqlen))
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dA = mx.exp(mx.expand_dims(A[chunk], axis=0))
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# Replace einsum with explicit operations
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x_chunk = x[:, chunk] # [batch, chunk_size, nheads, dim]
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x_chunk = mx.transpose(x_chunk, [0, 2, 3, 1]) # [batch, nheads, dim, chunk_size]
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B_chunk = B[:, chunk] # [batch, chunk_size, state_size]
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dBx = mx.matmul(x_chunk, B_chunk) # [batch, nheads, dim, state_size]
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state = state * mx.expand_dims(dA, axis=-1) + dBx
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# Replace einsum with explicit operations
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C_chunk = C[:, chunk] # [batch, chunk_size, state_size]
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y = mx.matmul(state, mx.transpose(C_chunk, [0, 2, 1])) # [batch, nheads, dim, chunk_size]
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y = mx.transpose(y, [0, 3, 1, 2]) # [batch, chunk_size, nheads, dim]
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outputs.append(y)
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return mx.concatenate(outputs, axis=1), state
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class DepthWiseConv1d(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.padding = padding
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self.groups = groups if groups is not None else in_channels
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assert in_channels == out_channels, "In and out channels must be same for depthwise convolution"
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assert self.groups == in_channels, "Groups must be equal to in_channels for depthwise convolution"
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self.weight = mx.random.normal((in_channels, 1, kernel_size))
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self.bias = mx.zeros((out_channels,)) if bias else None
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def __call__(self, x: mx.array, cache=None) -> mx.array:
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B, L, C = x.shape
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K = self.kernel_size
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assert C == self.in_channels, f"Input channels {C} doesn't match expected {self.in_channels}"
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if cache is not None:
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if isinstance(cache.conv_states[0], type(None)):
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cache.conv_states[0] = mx.zeros((B, K-1, C))
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x = mx.concatenate([cache.conv_states[0], x], axis=1)
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outputs = []
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for c in range(C):
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# Input prep debug
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x_c = x[:, :, c]
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x_c = mx.expand_dims(x_c, axis=1)
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# Weight prep debug
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w_c = self.weight[c]
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if w_c.ndim == 2:
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w_c = mx.expand_dims(w_c, axis=0)
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elif w_c.ndim == 1:
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w_c = mx.expand_dims(mx.expand_dims(w_c, axis=0), axis=0)
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y_c = mx.conv_general(
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x_c,
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w_c,
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stride=1,
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padding=0
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)
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if self.bias is not None:
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y_c = y_c + self.bias[c]
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y_c = mx.squeeze(y_c, axis=1)
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outputs.append(y_c)
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# Output statistics
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y = mx.stack(outputs, axis=-1)
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# Cache update debug
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if cache is not None:
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cache.conv_states[0] = x[:, -K+1:, :] if x.shape[1] >= K else x
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return y
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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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d_in_proj = 2 * args.intermediate_size + 2 * args.state_size + args.num_heads
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self.in_proj = nn.Linear(args.hidden_size, d_in_proj, bias=args.use_bias)
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conv_dim = args.intermediate_size + 2 * args.state_size
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self.conv1d = DepthWiseConv1d(
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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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bias=args.use_conv_bias,
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padding=args.conv_kernel - 1
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)
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# Initialize parameters
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self.dt_bias = mx.ones(args.num_heads)
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A = mx.arange(1, args.num_heads + 1)
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self.A_log = mx.log(A)
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self.D = mx.ones(args.num_heads)
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self.norm = MambaRMSNormGated(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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if args.rescale_prenorm_residual:
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layer_scale = math.sqrt(1.0 / args.num_hidden_layers)
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self.out_proj.weight = self.out_proj.weight * layer_scale
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def __call__(self, u: mx.array, cache=None):
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# Expect input to be shape [batch_size, 1, dim]
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batch_size, seq_len, dimension = u.shape
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assert seq_len == 1, "Input should be a single token"
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# Initialize cache if needed
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if cache.conv_states[0] is None:
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conv_dim = self.args.intermediate_size + 2 * self.args.state_size
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cache.conv_states[0] = mx.zeros((batch_size, self.args.conv_kernel - 1, conv_dim))
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if cache.ssm_states[0] is None:
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cache.ssm_states[0] = mx.zeros((
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batch_size,
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self.args.num_heads,
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self.args.head_dim,
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self.args.state_size
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))
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# Project input
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zxbcdt = self.in_proj(u)
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# Split projections
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n_heads = self.args.num_heads
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z = zxbcdt[:, :, :self.args.intermediate_size]
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xBC = zxbcdt[:, :, self.args.intermediate_size:self.args.intermediate_size + 2*self.args.state_size + self.args.intermediate_size]
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dt = zxbcdt[:, :, -(n_heads):]
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# Time steps
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dt = mx.reshape(dt, (batch_size, n_heads))
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dt = mx.clip(nn.softplus(dt + self.dt_bias), self.args.time_step_min, self.args.time_step_max)
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dt = mx.maximum(dt, self.args.time_step_floor)
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# Convolution
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xBC = self.conv1d(xBC, cache=cache)
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xBC = silu(xBC)
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# Split states
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x = xBC[:, :, :self.args.intermediate_size]
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B = xBC[:, :, self.args.intermediate_size:self.args.intermediate_size + self.args.state_size]
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C = xBC[:, :, -self.args.state_size:]
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# Reshape for SSM
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x = mx.reshape(x, (batch_size, 1, n_heads, self.args.head_dim))
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x = mx.squeeze(x, axis=1)
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B = mx.reshape(B, (batch_size, 1, self.args.state_size))
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B = mx.broadcast_to(B, (batch_size, n_heads, self.args.state_size))
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B = mx.expand_dims(B, axis=2)
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C = mx.reshape(C, (batch_size, 1, self.args.state_size))
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C = mx.broadcast_to(C, (batch_size, n_heads, self.args.state_size))
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C = mx.expand_dims(C, axis=3)
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# SSM updates
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A = -mx.exp(self.A_log)
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dA = mx.exp(dt * mx.expand_dims(A, 0))
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dA = mx.expand_dims(mx.expand_dims(dA, -1), -1)
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# Update state
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x = mx.expand_dims(x, axis=3)
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dBx = mx.matmul(x, B)
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cache.ssm_states[0] = cache.ssm_states[0] * dA + dBx
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# Compute output
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y = mx.matmul(cache.ssm_states[0], C)
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y = mx.squeeze(y, axis=-1)
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y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1)
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y = mx.reshape(y, (batch_size, 1, n_heads * self.args.head_dim))
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y = self.norm(y + z)
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return self.out_proj(y)
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class ResidualBlock(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.residual_in_fp32 = args.residual_in_fp32
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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):
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if self.residual_in_fp32:
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x = x.astype(mx.float32)
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normed = self.norm(x)
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output = self.mixer(normed, cache)
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return output + 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 = [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):
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x = self.embeddings(x)
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if cache is None:
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cache = [None] * len(self.layers)
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hidden = x
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for layer, c in zip(self.layers, cache):
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hidden = layer(hidden, c)
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return self.norm_f(hidden)
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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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hidden = self.backbone(inputs, cache)
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if self.args.tie_word_embeddings:
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logits = self.backbone.embeddings.as_linear(hidden)
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else:
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logits = self.lm_head(hidden)
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return logits
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def make_cache(self):
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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 |