mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-26 10:41:18 +08:00
308 lines
9.9 KiB
Python
308 lines
9.9 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 MambaCache
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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str
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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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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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intermediate_size: int
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time_step_limit: Tuple[float, float]
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time_step_rank: Union[int, str]
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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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A_init_min: float = 1.0
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A_init_max: float = 16.0
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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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class DepthWiseConv1d(nn.Module):
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def __init__(self, channels, kernel_size, bias=True, 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.weight = mx.random.normal((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=C)
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y = y + self.bias
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return y, x[:, -K + 1:, :]
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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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# Same dimensions as before
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self.d_model = args.hidden_size
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self.d_state = args.state_size
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self.d_conv = args.conv_kernel
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self.expand = args.expand
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self.d_inner = int(self.expand * self.d_model)
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self.n_groups = args.n_groups
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self.n_heads = args.num_heads
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self.d_head = self.d_inner // self.n_heads
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self.chunk_size = args.chunk_size
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# Input projection
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d_in_proj = 2 * self.d_inner + 2 * self.n_groups * self.d_state + self.n_heads
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self.in_proj = nn.Linear(self.d_model, d_in_proj, bias=args.use_bias)
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# Improved initialization of dt
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dt = mx.exp(
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mx.random.uniform(
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low=math.log(args.time_step_min),
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high=math.log(args.time_step_max),
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shape=(self.n_heads,)
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)
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)
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self.dt_bias = mx.random.normal((self.n_heads,)) * args.initializer_range
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self.A_log = mx.random.normal((self.n_heads,)) * args.initializer_range
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self.D = mx.random.normal((self.n_heads,)) * args.initializer_range
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# Same D initialization
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self.D = mx.random.normal((self.n_heads,)) * args.initializer_range
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# Convolution with proper initialization
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self.conv1d = DepthWiseConv1d(
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channels=self.d_inner + 2 * self.n_groups * self.d_state,
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kernel_size=self.d_conv,
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bias=args.use_conv_bias,
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padding=self.d_conv-1
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)
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# Output projections
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self.norm = MambaRMSNormGated(self.d_inner, eps=args.layer_norm_epsilon)
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self.out_proj = nn.Linear(self.d_inner, self.d_model, bias=args.use_bias)
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def __call__(self, u: mx.array, cache=None):
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batch_size, seq_len, _ = u.shape
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if cache is None:
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cache = [None, None]
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# Project input
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zxbcdt = self.in_proj(u) # (B, L, d_in_proj)
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A = -mx.exp(self.A_log) # (nheads) or (d_inner, d_state)
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z, xBC, dt = mx.split(
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zxbcdt,
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indices_or_sections=[
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self.d_inner,
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self.d_inner + (2 * self.n_groups * self.d_state + self.d_inner)
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],
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axis=-1
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)
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# Process dt
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dt = nn.softplus(dt + self.dt_bias) # (B, L, nheads)
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# Conv1d and activation
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xBC, conv_state = self.conv1d(xBC, cache[0] if cache else None)
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xBC = silu(xBC)
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if cache is not None:
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cache[0] = conv_state
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xBC = xBC[:, :seq_len, :]
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# Split conv output and reshape
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x, B, C = mx.split(
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xBC,
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indices_or_sections=[
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self.d_inner,
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self.d_inner + self.n_groups * self.d_state
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],
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axis=-1
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)
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# Reshape tensors
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B = mx.reshape(B, (batch_size, seq_len, self.n_groups, -1))
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C = mx.reshape(C, (batch_size, seq_len, self.n_groups, -1))
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x = mx.reshape(x, (batch_size, seq_len, self.n_heads, -1))
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# Initialize state
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if cache and cache[1] is not None:
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prev_state = cache[1]
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else:
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prev_state = mx.zeros((batch_size, self.n_heads, self.d_head, self.d_state))
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# Compute dA
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dt = mx.reshape(dt, (batch_size, seq_len, self.n_heads))
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dA = mx.exp(dt * mx.expand_dims(A, axis=(0, 1)))
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# Process sequence in chunks
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chunk_size = self.chunk_size
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outputs = []
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next_state = prev_state
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# Process in chunks
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for chunk_start in range(0, seq_len, chunk_size):
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chunk_end = min(chunk_start + chunk_size, seq_len)
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# Get current chunk
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x_chunk = x[:, chunk_start:chunk_end]
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B_chunk = B[:, chunk_start:chunk_end]
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C_chunk = C[:, chunk_start:chunk_end]
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dA_chunk = dA[:, chunk_start:chunk_end]
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z_chunk = z[:, chunk_start:chunk_end]
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# Process the chunk in batches
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chunk_outputs = []
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chunk_state = next_state
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for t in range(chunk_end - chunk_start):
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xt = x_chunk[:, t]
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Bt = B_chunk[:, t]
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Ct = C_chunk[:, t]
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dAt = dA_chunk[:, t]
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# Update state
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dBx = mx.einsum('bh,bgd,bhp->bhpd', dAt, Bt, xt)
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chunk_state = chunk_state * mx.expand_dims(dAt, axis=(-1, -2)) + dBx
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# Compute output
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yt = mx.einsum('bhpd,bgd->bhp', chunk_state, Ct)
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yt = yt + xt * mx.expand_dims(self.D, -1)
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# Reshape and normalize
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yt = mx.reshape(yt, (batch_size, 1, self.d_inner))
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yt = self.norm(yt, z_chunk[:, t:t+1])
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chunk_outputs.append(self.out_proj(yt))
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# Update state for next chunk
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next_state = chunk_state
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outputs.extend(chunk_outputs)
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# Update cache with final state
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if cache is not None:
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cache[1] = next_state
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return mx.concatenate(outputs, axis=1)
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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 sanitize(self, weights):
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for k, v in weights.items():
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if "conv1d.weight" in k and v.shape[-1] != 1:
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weights[k] = v.moveaxis(2, 1)
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return weights
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def make_cache(self):
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return [MambaCache() 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 |