mirror of
https://github.com/ml-explore/mlx-examples.git
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336 lines
12 KiB
Python
336 lines
12 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 Tuple, Union, Optional
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import mlx.nn as nn
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import mlx.core as mx
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from .base import BaseModelArgs
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from .cache import Mamba2Cache
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# python -m mlx_lm.generate --model rokyang/mamba2-130m-hf --prompt "hello how are you."
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@dataclass
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class ModelArgs(BaseModelArgs):
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num_heads: int
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head_dim: int
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vocab_size: int
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hidden_size: int
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state_size: int
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num_hidden_layers: int
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layer_norm_epsilon: float
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expand: int
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conv_kernel: int
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n_groups: int
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use_bias: bool
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use_conv_bias: bool
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initializer_range: float
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residual_in_fp32: bool
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time_step_min: float
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time_step_max: float
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time_step_floor: float
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rescale_prenorm_residual: bool
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use_cache: bool
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rms_norm: bool
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chunk_size: int
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tie_word_embeddings: bool
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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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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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# Ensure in_channels and out_channels are the same for depthwise conv
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assert in_channels == out_channels, "In and out channels must be the same for depthwise convolution"
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# Ensure groups is equal to in_channels for depthwise conv
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assert self.groups == in_channels, "Groups must be equal to in_channels for depthwise convolution"
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# Initialize weight with shape (out_channels, kernel_size, 1)
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self.weight = mx.random.normal((out_channels, kernel_size, 1))
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self.bias = mx.zeros((out_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, layer_idx):
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super().__init__()
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self.layer_idx = layer_idx
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self.hidden_size = args.hidden_size
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self.intermediate_size = args.intermediate_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.ssm_state_size = args.state_size
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self.n_groups = args.n_groups
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self.conv_kernel_size = args.conv_kernel
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self.use_conv_bias = args.use_conv_bias
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self.use_bias = args.use_bias
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self.time_step_min = args.time_step_min
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self.time_step_max = args.time_step_max
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self.chunk_size = args.chunk_size
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self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size
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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.conv1d = nn.Conv1d(
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self.conv_dim,
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self.conv_dim,
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self.conv_kernel_size,
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groups=self.conv_dim,
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bias=self.use_conv_bias
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)
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self.act = nn.SiLU()
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self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon)
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self.out_proj = nn.Linear(
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self.intermediate_size,
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self.hidden_size,
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bias=self.use_bias
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)
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self.A_log = mx.zeros(self.num_heads)
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self.D = mx.ones(self.num_heads)
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self.dt_bias = mx.zeros(self.num_heads)
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def __call__(self, input_states, cache):
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batch_size, seq_len, _ = input_states.shape
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dtype = input_states.dtype
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projected_states = self.in_proj(input_states)
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# Calculate the sizes of each split
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total_size = projected_states.shape[-1]
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remaining_size = total_size - self.intermediate_size - self.conv_dim - self.num_heads
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d_mlp = remaining_size // 2
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sizes = [
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d_mlp,
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d_mlp,
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self.intermediate_size,
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self.conv_dim,
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self.num_heads
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]
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# Perform the split operation
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split_result = mx.split(projected_states, sizes, axis=-1)
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# Print debug information
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print(f"Number of split parts: {len(split_result)}")
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print(f"Shapes of split parts: {[part.shape for part in split_result]}")
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# Flexibly handle the split result
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_, _, _, gate, hidden_states, dt = split_result
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if cache is not None:
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conv_state = cache.conv_states[self.layer_idx]
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if conv_state is None:
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# Initialize conv_state if it's None
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conv_state = mx.zeros((batch_size, 1, self.conv_kernel_size, hidden_states.shape[-1]))
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conv_state = mx.roll(conv_state, -1, -2) # Roll along the kernel dimension
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# Reshape hidden_states to match conv_state dimensions
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hidden_states_reshaped = hidden_states[:, None, None, :]
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conv_state = mx.concat([conv_state[:, :, :-1, :], hidden_states_reshaped], axis=-2)
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cache.conv_states[self.layer_idx] = conv_state
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# Adjust the convolution operation
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hidden_states = mx.sum(conv_state * self.conv1d.weight[:, :, None, :], axis=(-2, -1))
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if self.use_conv_bias:
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hidden_states += self.conv1d.bias
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hidden_states = self.act(hidden_states)[:, None, :]
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else:
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hidden_states = hidden_states.transpose(0, 2, 1)
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hidden_states = self.act(self.conv1d(hidden_states)).transpose(0, 2, 1)
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hidden_states, B, C = mx.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], axis=-1)
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A = -mx.exp(self.A_log.astype(mx.float32))
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dt = nn.softplus(dt + self.dt_bias)
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dt = mx.clip(dt, self.time_step_min, self.time_step_max)
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hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).astype(mx.float32)
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B = B.reshape(batch_size, seq_len, -1, self.ssm_state_size).astype(mx.float32)
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C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).astype(mx.float32)
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B = mx.repeat(B, repeats=self.num_heads // self.n_groups, axis=2)
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C = mx.repeat(C, repeats=self.num_heads // self.n_groups, axis=2)
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if cache is not None and cache.seqlen_offset > 0:
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ssm_state = cache.ssm_states[self.layer_idx]
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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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dBx = dB * hidden_states[:, :, :, None]
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ssm_state = ssm_state * dA + dBx
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cache.ssm_states[self.layer_idx] = ssm_state
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y = mx.sum(ssm_state * C[:, None, :, :], axis=-1)
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D = self.D[None, :, None].expand(self.D.shape[0], self.head_dim)
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y = y + hidden_states * D
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y = y.reshape(batch_size, -1)[:, None, :]
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else:
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# Implement chunked computation here (simplified version)
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pad_size = self.chunk_size - (seq_len % self.chunk_size)
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hidden_states_padded = mx.pad(hidden_states, [(0, 0), (0, pad_size), (0, 0), (0, 0)])
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B_padded = mx.pad(B, [(0, 0), (0, pad_size), (0, 0), (0, 0)])
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C_padded = mx.pad(C, [(0, 0), (0, pad_size), (0, 0), (0, 0)])
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chunks = seq_len // self.chunk_size + (1 if pad_size > 0 else 0)
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y_list = []
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ssm_state = mx.zeros((batch_size, self.num_heads, self.head_dim, self.ssm_state_size))
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for i in range(chunks):
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chunk_start = i * self.chunk_size
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chunk_end = (i + 1) * self.chunk_size
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chunk_h = hidden_states_padded[:, chunk_start:chunk_end]
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chunk_B = B_padded[:, chunk_start:chunk_end]
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chunk_C = C_padded[:, chunk_start:chunk_end]
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chunk_dt = dt[:, chunk_start:chunk_end]
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dA = mx.exp(chunk_dt[:, :, None, None] * A[None, None, :, None])
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dB = chunk_dt[:, :, None, None] * chunk_B
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dBx = dB * chunk_h[:, :, :, None]
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chunk_y = mx.zeros_like(chunk_h)
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for j in range(self.chunk_size):
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ssm_state = ssm_state * dA[:, j] + dBx[:, j]
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chunk_y[:, j] = mx.sum(ssm_state * chunk_C[:, j], axis=-1)
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y_list.append(chunk_y)
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y = mx.concat(y_list, axis=1)
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if pad_size > 0:
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y = y[:, :seq_len]
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D = self.D[None, :, None].expand(self.D.shape[0], self.head_dim)
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y = y + hidden_states * D
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y = y.reshape(batch_size, seq_len, -1)
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y = self.norm(y, gate)
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contextualized_states = self.out_proj(y.astype(dtype))
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return contextualized_states
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class Mamba2Block(nn.Module):
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def __init__(self, args: ModelArgs, layer_idx: int):
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super().__init__()
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self.mixer = Mamba2Mixer(args, layer_idx)
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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, idx) 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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print(logits)
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print(logits.shape)
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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):
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return [Mamba2Cache(self.args.num_hidden_layers) 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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