mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-28 03:41:17 +08:00
adding debug statements (somehiw generating only goes through the fist MambaMixer block pass)
This commit is contained in:
Goekdeniz-Guelmez
parent
00ba27fe6c
commit
8073cb486c
@ -2,7 +2,7 @@
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import math
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from dataclasses import dataclass, field
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from typing import Optional, Tuple, Union
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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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@ -48,14 +48,18 @@ class ModelArgs(BaseModelArgs):
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class Mamba2Cache:
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def __init__(self, num_layers):
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self.cache = [[None, None] for _ in range(num_layers)]
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def __init__(self):
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self.cache = [None, None]
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def __setitem__(self, idx, value):
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self.cache[idx] = value
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def __getitem__(self, idx):
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return self.cache[idx]
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def __setitem__(self, idx, value):
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self.cache[idx] = value
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@property
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def state(self):
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return self.cache
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class MambaRMSNormGated(nn.Module):
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@ -71,6 +75,40 @@ class MambaRMSNormGated(nn.Module):
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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: ModelArgs):
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@ -82,11 +120,11 @@ class Mamba2Mixer(nn.Module):
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self.hidden_size = args.hidden_size
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self.state_size = args.state_size
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self.num_heads = args.num_heads
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self.head_dim = args.head_dim
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self.head_dim = args.hidden_size // args.num_heads
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self.n_groups = args.n_groups
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self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size
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self.conv1d = nn.Conv1d(
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self.conv1d = DepthWiseConv1d(
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in_channels=self.conv_dim,
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out_channels=self.conv_dim,
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bias=args.use_conv_bias,
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@ -102,7 +140,6 @@ class Mamba2Mixer(nn.Module):
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bias=args.use_bias
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)
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self.act = nn.SiLU()
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self.dt_bias = mx.ones((self.num_heads,))
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self.A_log = mx.log(mx.arange(1, self.num_heads + 1))
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self.D = mx.ones((self.num_heads,))
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@ -111,105 +148,84 @@ class Mamba2Mixer(nn.Module):
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self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
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def ssm_step(self, x, dt, state):
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B, L, C = x.shape
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print(f"x shape: {x.shape}")
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projected_states = self.in_proj(x)
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print(f"deltaBC shape: {projected_states.shape}")
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def ssm_step(self, x, state, dt_proj):
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A = -mx.exp(self.A_log)
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D = self.D
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delta = nn.softplus(dt_proj + self.dt_bias)
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d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.state_size - self.num_heads) // 2
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B, C = mx.split(x, indices_or_sections=[self.state_size * self.n_groups], axis=-1)
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gate = projected_states[:, :, 2*d_mlp:2*d_mlp+self.intermediate_size]
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conv_state = projected_states[:, :, 2*d_mlp+self.intermediate_size:2*d_mlp+self.intermediate_size+self.conv_dim]
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time_step = projected_states[:, :, -self.num_heads:]
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B = B.reshape(-1, self.n_groups, self.state_size)
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C = C.reshape(-1, self.n_groups, self.state_size)
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print(f"conv_state shape before reshape: {conv_state.shape}")
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print(f"self.conv_dim: {self.conv_dim}")
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if state is None:
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new_state = mx.expand_dims(delta, -1) * B
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else:
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new_state = mx.expand_dims(delta, -1) * (B + state * mx.exp(mx.expand_dims(delta, -1) * A))
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# Reshape and handle the case where L=1
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conv_state = conv_state.reshape(B, self.conv_dim, L)
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if L == 1:
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# If sequence length is 1, we need to pad to apply convolution
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conv_state = mx.pad(conv_state, ((0, 0), (0, 0), (0, self.conv_kernel_size - 1)))
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y = mx.sum(new_state * C, axis=-1)
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y = y + D * x[:, :self.num_heads]
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return y, new_state
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conv_out = self.conv1d(conv_state)
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def __call__(self, x, cache):
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B, T, D = x.shape
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if cache is None:
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cache = [None, None]
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# If we padded, we need to remove the padding
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if L == 1:
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conv_out = conv_out[:, :, :L]
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outputs = []
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for t in range(T):
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xt = x[:, t, :]
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xz = self.in_proj(xt)
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# Reshape back to (B, L, C)
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conv_out = conv_out.transpose(0, 2, 1)
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x_and_conv_out, B, C = mx.split(
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conv_out,
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[self.intermediate_size, self.n_groups * self.state_size],
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x_t, z_t, dt_proj = mx.split(
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xz,
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indices_or_sections=[self.conv_dim, self.conv_dim + self.intermediate_size],
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axis=-1
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)
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dt = nn.softplus(time_step + self.dt_bias)
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dt = mx.clip(dt, self.args.time_step_min, self.args.time_step_max)
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conv_out, cache[0] = self.conv1d(mx.expand_dims(x_t, 1), cache[0])
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x_t = conv_out.squeeze(1)
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x_t = nn.silu(x_t)
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y_t, cache[1] = self.ssm_step(x_t, cache[1], dt_proj)
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z_t = nn.silu(z_t)
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B = B.reshape(-1, self.num_heads, self.head_dim, self.state_size)
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C = C.reshape(-1, self.num_heads, self.head_dim, self.state_size)
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# Print shapes for debugging
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print(f"y_t shape: {y_t.shape}")
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print(f"z_t shape: {z_t.shape}")
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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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# Reshape y_t to (B, num_heads, head_dim)
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y_t_reshaped = y_t.reshape(B, self.num_heads, -1)
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new_state = state * dA + x_and_conv_out[:, :, None, None] * dB
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y = mx.sum(new_state * C, axis=-1)
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y = y + C[None, :, None] * x_and_conv_out
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# Reshape z_t to (B, num_heads, intermediate_size // num_heads)
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z_t_reshaped = z_t.reshape(B, self.num_heads, -1)
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y = self.norm(y.reshape(-1, self.intermediate_size), gate)
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output = self.out_proj(y)
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print(f"y_t_reshaped shape: {y_t_reshaped.shape}")
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print(f"z_t_reshaped shape: {z_t_reshaped.shape}")
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return output, new_state
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# Element-wise multiplication (broadcasting across the last dimension)
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output_t = y_t_reshaped * z_t_reshaped
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def __call__(
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self,
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x: mx.array,
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cache = None
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):
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B, L, _ = x.shape
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# Reshape to match the expected input of out_proj
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output_t = output_t.reshape(B, -1)
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if cache[0] is not None: # Using cached state
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conv_state, ssm_state = cache
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x = x[:, -1:]
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output, new_ssm_state = self.ssm_step(x, None, ssm_state)
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cache[1] = new_ssm_state # Update SSM state in cache
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else:
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conv_state, ssm_state = None, None
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outputs = []
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for t in range(L):
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x = x[:, t:t+1]
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output, ssm_state = self.ssm_step(x, None, ssm_state)
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outputs.append(output)
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output = mx.concatenate(outputs, axis=1)
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cache[1] = ssm_state # Store final SSM state in cache
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print(f"output_t shape before out_proj: {output_t.shape}")
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print(f"out_proj weight shape: {self.out_proj.weight.shape}")
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# Update conv state in cache
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new_conv_state = x[:, -self.conv_kernel_size:]
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cache[0] = new_conv_state
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output_t = self.out_proj(output_t)
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outputs.append(output_t)
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output = mx.stack(outputs, axis=1)
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return output
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class Mamba2Block(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.args = args
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self.residual_in_fp32 = args.residual_in_fp32
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self.norm = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
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self.mixer = Mamba2Mixer(args)
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self.norm = nn.RMSNorm(args.hidden_size)
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def __call__(
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self,
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inputs: mx.array,
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cache=None,
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):
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h = self.mixer(self.norm(inputs), cache=cache)
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r = inputs + h
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return r
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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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@ -246,11 +262,7 @@ class Model(nn.Module):
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if not args.tie_word_embeddings:
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self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
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def __call__(
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self,
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inputs: mx.array,
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cache=None
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):
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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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@ -259,6 +271,7 @@ class Model(nn.Module):
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logits = self.backbone.embeddings.as_linear(x)
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else:
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logits = self.lm_head(x)
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return logits
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def sanitize(self, weights):
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@ -268,7 +281,7 @@ class Model(nn.Module):
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return weights
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def make_cache(self, batch_size: int = 1):
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return Mamba2Cache(len(self.backbone.layers))
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return [Mamba2Cache() for _ in range(len(self.layers))]
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@property
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def layers(self):
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@ -6,37 +6,37 @@ 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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# 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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model_type: str = "mamba2"
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num_heads: int = 128
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head_dim: int = 64
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vocab_size: int = 32768
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hidden_size: int = 4096
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state_size: int = 128
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num_hidden_layers: int = 64
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layer_norm_epsilon: float = 1e-5
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expand: int = 2
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conv_kernel: int = 4
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n_groups: int = 8
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use_bias: bool = False
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use_conv_bias: bool = True
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initializer_range: float = 0.1
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residual_in_fp32: bool = True
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time_step_rank: Union[int, str] = "auto"
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time_step_min: float = 0.001
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time_step_max: float = 0.1
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time_step_floor: float = 1e-4
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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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rescale_prenorm_residual: bool = False
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use_cache: bool = True
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rms_norm: bool = True
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chunk_size: int = 256
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tie_word_embeddings: bool = False
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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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@ -149,26 +149,35 @@ class Mamba2Mixer(nn.Module):
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self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
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def ssm_step(self, x, state, dt_proj):
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print(f"ssm_step input shapes - x: {x.shape}, dt_proj: {dt_proj.shape}")
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A = -mx.exp(self.A_log)
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D = self.D
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delta = nn.softplus(dt_proj + self.dt_bias)
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B, C = mx.split(x, indices_or_sections=[self.state_size * self.n_groups], axis=-1)
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print(f"ssm_step split shapes - B: {B.shape}, C: {C.shape}")
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B = B.reshape(-1, self.n_groups, self.state_size)
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C = C.reshape(-1, self.n_groups, self.state_size)
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print(f"After reshape - B: {B.shape}, C: {C.shape}")
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delta = delta.reshape(-1, self.num_heads, 1)
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A = A.reshape(1, self.num_heads, 1)
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if state is None:
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new_state = mx.expand_dims(delta, -1) * B
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new_state = delta * B
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else:
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new_state = mx.expand_dims(delta, -1) * (B + state * mx.exp(mx.expand_dims(delta, -1) * A))
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new_state = delta * (B + state * mx.exp(delta * A))
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print(f"Before final computation - new_state: {new_state.shape}, C: {C.shape}")
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y = mx.sum(new_state * C, axis=-1)
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y = y + D * x[:, :self.num_heads]
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print(f"ssm_step output shape - y: {y.shape}")
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return y, new_state
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def __call__(self, x, cache):
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B, T, D = x.shape
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print(f"__call__ input shape - x: {x.shape}")
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if cache is None:
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cache = [None, None]
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@ -176,47 +185,37 @@ class Mamba2Mixer(nn.Module):
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for t in range(T):
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xt = x[:, t, :]
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xz = self.in_proj(xt)
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print(f"After in_proj shape - xz: {xz.shape}")
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x_t, z_t, dt_proj = mx.split(
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xz,
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indices_or_sections=[self.conv_dim, self.conv_dim + self.intermediate_size],
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axis=-1
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)
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print(f"After split shapes - x_t: {x_t.shape}, z_t: {z_t.shape}, dt_proj: {dt_proj.shape}")
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conv_out, cache[0] = self.conv1d(mx.expand_dims(x_t, 1), cache[0])
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x_t = conv_out.squeeze(1)
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x_t = nn.silu(x_t)
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print(f"Before ssm_step shape - x_t: {x_t.shape}")
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y_t, cache[1] = self.ssm_step(x_t, cache[1], dt_proj)
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z_t = nn.silu(z_t)
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# Print shapes for debugging
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print(f"y_t shape: {y_t.shape}")
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print(f"z_t shape: {z_t.shape}")
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print(f"self.num_heads: {self.num_heads}")
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print(f"self.intermediate_size: {self.intermediate_size}")
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print(f"self.head_dim: {self.head_dim}")
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# Flexible reshaping
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y_t_reshaped = y_t.reshape(B, -1, 1)
|
||||
z_t_reshaped = z_t.reshape(B, y_t_reshaped.shape[1], -1)
|
||||
|
||||
# Print reshaped shapes
|
||||
print(f"y_t_reshaped shape: {y_t_reshaped.shape}")
|
||||
print(f"z_t_reshaped shape: {z_t_reshaped.shape}")
|
||||
print(f"After ssm_step shapes - y_t: {y_t.shape}, z_t: {z_t.shape}")
|
||||
|
||||
# Element-wise multiplication
|
||||
output_t = y_t_reshaped * z_t_reshaped
|
||||
output_t = y_t[:, :, None] * z_t[:, None, :]
|
||||
print(f"After multiplication shape - output_t: {output_t.shape}")
|
||||
|
||||
# Reshape to match the expected input of out_proj
|
||||
output_t = output_t.reshape(B, self.intermediate_size)
|
||||
|
||||
print(f"output_t shape before out_proj: {output_t.shape}")
|
||||
print(f"out_proj weight shape: {self.out_proj.weight.shape}")
|
||||
# Sum across the second dimension to match the intermediate_size
|
||||
output_t = output_t.sum(axis=1)
|
||||
print(f"After sum shape - output_t: {output_t.shape}")
|
||||
|
||||
output_t = self.out_proj(output_t)
|
||||
print(f"After out_proj shape - output_t: {output_t.shape}")
|
||||
outputs.append(output_t)
|
||||
|
||||
output = mx.stack(outputs, axis=1)
|
||||
print(f"Final output shape: {output.shape}")
|
||||
return output
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user