mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-25 18:11:17 +08:00
fixed inference slowness but it cant handle multible Token inputs and is generateing gibberish
This commit is contained in:
Goekdeniz-Guelmez
parent
800b60239c
commit
3a499f9735
@ -350,28 +350,11 @@ class MambaCache:
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return [self.cache[1]]
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class Mamba2Cache:
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def __init__(self):
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self.conv_states = [None] # Initialize as None, will be set on first use
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self.ssm_states = [None] # Initialize as None, will be set on first use
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class Mamba2Cache(_BaseCache):
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def __init__(
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self,
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batch_size,
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conv_kernel
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):
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self.conv_kernel: mx.array = conv_kernel
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self.conv_states: mx.array = [None]
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self.ssm_states = [None]
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self.seqlen_offset = 0
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def reset(self):
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self.conv_states = None
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self.ssm_state = None
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def update(self, layer_idx: int, new_conv_state: mx.array, cache_position: mx.array) -> mx.array:
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conv_state = self.conv_states[layer_idx]
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cache_position = cache_position.clamp(0, self.conv_kernel - 1)
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conv_state = conv_state.roll(shifts=-1, dims=-1)
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conv_state[:, :, cache_position] = new_conv_state
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self.conv_states[layer_idx].zero_()
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self.conv_states[layer_idx] += conv_state
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return self.conv_states[layer_idx]
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@property
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def state(self):
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return [self.conv_states[0], self.ssm_states[0]]
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@ -1,11 +1,11 @@
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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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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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from .cache import Mamba2Cache
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@dataclass
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class ModelArgs(BaseModelArgs):
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@ -61,9 +61,8 @@ class MambaRMSNormGated(nn.Module):
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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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# Not getting used
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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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@ -92,30 +91,91 @@ def ssd(x, A, B, C, chunk_size):
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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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self.chunk_size = args.chunk_size
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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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self.conv_dim = args.intermediate_size + 2 * args.state_size
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# Replace DepthWiseConv1d with grouped nn.Conv1d
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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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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=self.conv_dim, # Makes it depthwise
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groups=conv_dim,
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bias=args.use_conv_bias,
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padding=0 # We'll handle padding via cache
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padding=args.conv_kernel - 1
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)
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self.dt_bias = mx.random.normal((args.num_heads,)) * args.initializer_range
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self.A_log = mx.random.normal((args.num_heads,)) * args.initializer_range
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self.D = mx.random.normal((args.num_heads,)) * args.initializer_range
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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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@ -124,24 +184,18 @@ class Mamba2Block(nn.Module):
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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: Optional[MambaCache] = None):
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batch_size, seq_len, _ = u.shape
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pad_size = self.chunk_size - (seq_len % self.chunk_size)
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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 is None:
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cache = MambaCache()
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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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# Initialize states if needed
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if cache[0] is None: # conv state
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cache[0] = mx.zeros((
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batch_size,
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self.args.conv_kernel - 1,
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self.conv_dim
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))
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if cache[1] is None: # ssm state
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cache[1] = mx.zeros((
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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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@ -152,90 +206,67 @@ class Mamba2Block(nn.Module):
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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[:, :, -(self.args.num_heads):]
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dt = zxbcdt[:, :, -(n_heads):]
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# Process delta time
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dt = mx.reshape(dt, (batch_size, seq_len, self.args.num_heads))
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dt = mx.squeeze(dt, axis=0)
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dt = mx.clip(
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nn.softplus(dt + self.dt_bias),
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self.args.time_step_min,
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self.args.time_step_max
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)
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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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# Handle convolution caching and padding
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conv_state = cache[0]
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if conv_state is not None:
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xBC = mx.concatenate([conv_state, xBC], axis=1)
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# Prepare input for conv1d: [B, C, L]
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xBC = mx.transpose(xBC, [0, 2, 1])
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# Apply convolution
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xBC = self.conv1d(xBC)
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# Update cache state
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cache[0] = mx.transpose(xBC, [0, 2, 1])[:, -self.args.conv_kernel+1:, :]
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# Return to [B, L, C] format
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xBC = mx.transpose(xBC, [0, 2, 1])
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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 conv output
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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, seq_len, self.args.num_heads, self.args.head_dim))
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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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B = mx.reshape(B, (batch_size, seq_len, self.args.state_size))
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B = mx.broadcast_to(B, (batch_size, self.args.num_heads, self.args.state_size))
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C = mx.reshape(C, (batch_size, seq_len, self.args.state_size))
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C = mx.broadcast_to(C, (batch_size, self.args.num_heads, self.args.state_size))
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# SSM state update
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ssm_state = cache[1]
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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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x = mx.expand_dims(x, axis=-1)
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dBx = mx.matmul(x, mx.expand_dims(B, axis=-2))
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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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new_ssm_state = ssm_state * mx.expand_dims(dA, -1) + dBx
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cache[1] = new_ssm_state
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# Output computation
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y = mx.matmul(new_ssm_state, mx.expand_dims(C, axis=-1))
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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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if pad_size > 0:
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y = y[:, :seq_len, :, :]
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# Final reshape and projections
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y = mx.reshape(y, (batch_size, seq_len, -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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return self.mixer(self.norm(x), cache) + x
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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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@ -249,9 +280,11 @@ class Mamba2(nn.Module):
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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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x = layer(x, c)
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return self.norm_f(x)
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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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@ -259,32 +292,23 @@ class Model(nn.Module):
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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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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(x)
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logits = self.backbone.embeddings.as_linear(hidden)
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else:
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logits = self.lm_head(x)
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logits = self.lm_head(hidden)
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return logits
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def make_cache(self, batch_size=1):
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return [MambaCache() for _ in range(len(self.backbone.layers))]
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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 [Mamba2Cache() for _ in range(len(self.layers))]
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@property
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def layers(self):
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