mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-27 11:21:32 +08:00
adding multi token input and correct cache handling in ssm step
This commit is contained in:
Goekdeniz-Guelmez
parent
5326d9373a
commit
758597eaa8
@ -27,10 +27,10 @@ class ModelArgs(BaseModelArgs):
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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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use_cache: bool = True
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time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
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time_step_rank: Union[int, str] = "auto"
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model_type: str = "mamba2"
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@ -58,6 +58,29 @@ class MambaRMSNormGated(nn.Module):
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return self.weight * hidden_states
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def silu(x):
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return x * mx.sigmoid(x)
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def ssd(x, A, B, C, chunk_size):
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batch, seqlen, nheads, dim = x.shape
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B = mx.expand_dims(B, axis=2)
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C = mx.expand_dims(C, axis=2)
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state = mx.zeros((batch, nheads, dim, B.shape[-1]))
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outputs = []
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for i in range(0, seqlen, chunk_size):
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chunk = slice(i, min(i + chunk_size, seqlen))
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dA = mx.exp(mx.expand_dims(A[chunk], axis=0))
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dBx = mx.einsum('blhp,bln->bhpn', x[:, chunk], B[:, chunk])
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state = state * mx.expand_dims(dA, axis=-1) + dBx
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y = mx.einsum('bhpn,bln->blhp', state, C[:, chunk])
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outputs.append(y)
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return mx.concatenate(outputs, axis=1), state
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class DepthWiseConv1d(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0):
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super().__init__()
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@ -67,127 +90,142 @@ class DepthWiseConv1d(nn.Module):
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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 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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# 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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# Initialize with shape (channels, 1, kernel_size) to match pretrained weights
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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, cache=None):
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def __call__(self, x: mx.array, cache=None, cache_idx: int = 0) -> mx.array:
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B, L, C = x.shape
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_, K, _ = self.weight.shape
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K = self.kernel_size
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# Handle padding and caching
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if cache is not None:
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conv_cache = cache[cache_idx]
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if conv_cache is not None:
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x = mx.concatenate([conv_cache, x], axis=1)
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L = x.shape[1] # Update L after concatenation
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else:
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pad_left = K - 1
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x = mx.pad(x, [(0, 0), (pad_left, 0), (0, 0)])
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L = x.shape[1] # Update L after padding
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# Implement depthwise convolution manually for each channel
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outputs = []
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for c in range(C):
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# Extract single channel and reshape for 1D convolution
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x_c = x[:, :, c] # Shape: [B, L]
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x_c = mx.expand_dims(x_c, axis=1) # Shape: [B, 1, L]
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# Extract and ensure filter is 3D
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w_c = self.weight[c] # Shape: [1, kernel_size] or [1, 1, kernel_size]
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if w_c.ndim == 2:
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w_c = mx.expand_dims(w_c, axis=0) # Shape: [1, 1, kernel_size]
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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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# For inference mode (single token), adjust the input
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if L < K:
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# Pad input to match kernel size
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pad_size = K - L
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x_c = mx.pad(x_c, [(0, 0), (0, 0), (pad_size, 0)])
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# Apply 1D convolution for this channel
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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 # We've already handled padding
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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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outputs.append(mx.squeeze(y_c, axis=1)) # Shape: [B, 1]
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# Stack all channel outputs
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y = mx.stack(outputs, axis=-1) # Shape: [B, L', C]
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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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# Update cache with the most recent K-1 tokens
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cache[cache_idx] = x[:, -(K-1):, :] if L >= K else x
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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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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.intermediate_size = args.intermediate_size
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self.time_step_rank = args.time_step_rank
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self.conv_kernel_size = args.conv_kernel
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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.hidden_size // args.num_heads
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self.n_groups = args.n_groups
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# projection_size = 2 * args.intermediate_size + 2 * args.n_groups * args.state_size + args.num_heads
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projection_size = 2 * args.intermediate_size + 2 * args.state_size + args.num_heads
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self.in_proj = nn.Linear(
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args.hidden_size,
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projection_size,
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bias=args.use_bias
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)
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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.n_groups * args.state_size
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self.conv_dim = args.intermediate_size + 2 * args.state_size
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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=self.conv_dim,
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out_channels=self.conv_dim,
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in_channels=conv_dim,
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out_channels=conv_dim,
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kernel_size=args.conv_kernel,
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groups=conv_dim,
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bias=args.use_conv_bias,
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groups=self.conv_dim,
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padding=args.conv_kernel - 1
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)
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self.A_log = mx.zeros(args.num_heads)
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self.D = mx.ones((args.num_heads,))
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self.dt_bias = mx.zeros(args.num_heads)
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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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self.out_proj = nn.Linear(args.intermediate_size, args.hidden_size, bias=args.use_bias)
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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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def _ssd(self, x, A, B, C, chunk_size):
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batch, seq_len, nheads, head_dim = x.shape
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n_state = B.shape[-1]
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if args.rescale_prenorm_residual:
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layer_scale = math.sqrt(1.0 / args.num_hidden_layers)
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self.out_proj.weight = self.out_proj.weight * layer_scale
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h = mx.zeros((batch, nheads, head_dim, n_state))
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ys = []
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for i in range(0, seq_len, chunk_size):
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chunk_size_i = min(chunk_size, seq_len - i)
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xi = x[:, i:i + chunk_size_i]
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Bi = B[:, i:i + chunk_size_i]
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Ci = C[:, i:i + chunk_size_i]
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for t in range(chunk_size_i):
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h = h * mx.exp(A)[:, None, None]
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h = h + mx.expand_dims(Bi[:, t], -2) * mx.expand_dims(xi[:, t], -1)
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y = mx.sum(h * mx.expand_dims(Ci[:, t], -2), axis=-1)
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ys.append(y)
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y = mx.stack(ys, axis=1)
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return y, h
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def __call__(self, x: mx.array, cache) -> mx.array:
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if cache is not None:
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return self.step(x, cache)
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def __call__(self, u: mx.array, cache = None):
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if cache is not None and self.args.use_cache:
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return self.step(u, cache)
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A = -mx.exp(self.A_log)
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zxbcdt = self.in_proj(u)
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z, xBC, dt = mx.split(
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zxbcdt,
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[
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self.args.d_inner,
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self.args.d_inner + 2 * self.args.d_state,
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self.args.nheads,
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],
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axis=-1,
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splits = [
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self.args.intermediate_size,
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self.args.intermediate_size + 2 * self.args.state_size,
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self.args.num_heads,
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]
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z, xBC, dt = mx.split(zxbcdt, splits, axis=-1)
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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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dt = mx.softplus(dt + self.dt_bias)
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dt = mx.maximum(dt, self.args.time_step_floor)
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# Use the custom DepthWiseConv1d with cache
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xBC = self.conv1d(xBC, cache, cache_idx=0)
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xBC = mx.sigmoid(xBC) * xBC # SiLU activation
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xBC = silu(self.conv1d(xBC))
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x, B, C = mx.split(
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xBC_parts = mx.split(
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xBC,
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[self.args.d_inner, self.args.d_state, self.args.d_state],
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[self.args.intermediate_size, self.args.state_size, self.args.state_size],
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axis=-1
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)
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x = self._reshape_heads(x, True)
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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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x = xBC_parts[0]
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B = xBC_parts[1]
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C = xBC_parts[2]
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y, ssm_state = self._ssd(
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# Replace rearrange with reshape and transpose
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b, l, hp = x.shape
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h = self.args.num_heads
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p = hp // h
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x = mx.reshape(x, (b, l, h, p))
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y, ssm_state = ssd(
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x * mx.expand_dims(dt, -1),
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A * dt,
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B,
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@ -196,61 +234,127 @@ class Mamba2Block(nn.Module):
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)
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y = y + x * mx.expand_dims(self.D, -1)
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y = self._reshape_heads(y, False)
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y = self.norm(y, z)
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# Replace rearrange with reshape
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y = mx.reshape(y, (b, l, h * p))
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y = self.norm(y + z)
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y = self.out_proj(y)
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if cache is not None:
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if cache is not None and self.args.use_cache:
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cache[1] = ssm_state
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if self.args.residual_in_fp32:
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y = mx.cast(y, mx.float32)
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return y
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def step(self, x: mx.array, cache) -> mx.array:
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"""Single inference step"""
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assert x.shape[1] == 1, "Only one token can be decoded per inference step"
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def step(self, u: mx.array, cache: MambaCache):
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batch_size = u.shape[0]
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seq_len = u.shape[1]
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outputs = []
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zxbcdt = self.in_proj(mx.squeeze(x, 1))
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z, xBC, dt = mx.split(
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zxbcdt,
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[
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self.args.d_inner,
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self.args.d_inner + 2 * self.args.d_state,
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self.args.nheads,
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],
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axis=-1,
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)
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# Initialize SSM state if needed
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if cache[1] is None:
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cache[1] = mx.zeros((
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batch_size,
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self.args.num_heads,
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self.args.head_dim,
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self.args.state_size
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))
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# Use the custom DepthWiseConv1d with cache
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xBC = self.conv1d(xBC, cache, cache_idx=0)
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xBC = mx.sigmoid(xBC) * xBC # SiLU activation
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for pos in range(seq_len):
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# Get single token
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u_t = u[:, pos:pos+1, :]
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x, B, C = mx.split(
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xBC,
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[self.args.d_inner, self.args.d_state, self.args.d_state],
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axis=-1
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)
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A = -mx.exp(self.A_log)
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# Project input
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zxbcdt = self.in_proj(u_t)
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dt = mx.softplus(dt + self.dt_bias)
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dA = mx.exp(dt * A)
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# Calculate sizes
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d_model = self.args.intermediate_size
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d_state = self.args.state_size
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n_heads = self.args.num_heads
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d_head = self.args.head_dim
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x = mx.reshape(x, (-1, self.args.nheads, self.args.headdim))
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# Correct splits for z, xBC, dt
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splits = [
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d_model, # z size
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d_model + 2 * d_state, # xBC size (delta, B, C)
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n_heads # dt size
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]
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ssm_state = cache[1]
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dBx = mx.expand_dims(dt, -1) * mx.expand_dims(B, 1) * mx.expand_dims(x, -1)
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ssm_state = ssm_state * mx.expand_dims(mx.expand_dims(dA, -1), -1) + dBx
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# Split the projected input
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z = zxbcdt[:, :, :splits[0]]
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xBC = zxbcdt[:, :, splits[0]:splits[0] + splits[1]]
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dt = zxbcdt[:, :, -splits[2]:] # Take last n_heads elements
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y = mx.sum(ssm_state * mx.expand_dims(mx.expand_dims(C, 1), 1), axis=-1)
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y = y + mx.expand_dims(self.D, -1) * x
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y = mx.reshape(y, (-1, self.args.nheads * self.args.headdim))
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# Process dt
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dt = mx.reshape(dt, (batch_size, n_heads))
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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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dt = mx.maximum(dt, self.args.time_step_floor)
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y = self.norm(y, z)
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y = self.out_proj(y)
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# Process convolution
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xBC = self.conv1d(xBC, cache=cache, cache_idx=0)
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xBC = silu(xBC)
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# Update SSM state in cache
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cache[1] = ssm_state
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# Split convolved xBC into x, B, C
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x = xBC[:, :, :d_model]
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B = xBC[:, :, d_model:d_model + d_state]
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C = xBC[:, :, -d_state:]
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return mx.expand_dims(y, 1)
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# Reshape x into (batch, heads, dim)
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x = mx.reshape(x, (batch_size, 1, n_heads, d_head))
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x = mx.squeeze(x, axis=1) # (batch, heads, dim)
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# Reshape B into (batch, heads, dim, state)
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B = mx.reshape(B, (batch_size, 1, d_state))
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B = mx.broadcast_to(B, (batch_size, n_heads, d_state))
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B = mx.expand_dims(B, axis=2) # (batch, heads, 1, state)
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# Reshape C for later use
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C = mx.reshape(C, (batch_size, 1, d_state))
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C = mx.broadcast_to(C, (batch_size, n_heads, d_state))
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C = mx.expand_dims(C, axis=3) # (batch, heads, state, 1)
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# Compute 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) # (batch, heads, 1, 1)
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# Prepare x for Bx computation
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x = mx.expand_dims(x, axis=3) # (batch, heads, dim, 1)
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# Compute dBx with proper broadcasting
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dBx = mx.matmul(x, B) # (batch, heads, dim, state)
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# Update state
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ssm_state = cache[1] # (batch, heads, dim, state)
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ssm_state = ssm_state * dA + dBx
|
||||
cache[1] = ssm_state
|
||||
|
||||
# Compute output
|
||||
y = mx.matmul(ssm_state, C) # (batch, heads, dim, 1)
|
||||
y = mx.squeeze(y, axis=-1) # (batch, heads, dim)
|
||||
|
||||
# Add skip connection with D
|
||||
y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1)
|
||||
|
||||
# Reshape to original dimensions
|
||||
y = mx.reshape(y, (batch_size, 1, n_heads * d_head))
|
||||
|
||||
# Apply norm and output projection
|
||||
y = self.norm(y + z)
|
||||
y = self.out_proj(y)
|
||||
|
||||
if self.args.residual_in_fp32:
|
||||
y.astype(mx.float32)
|
||||
|
||||
outputs.append(y)
|
||||
|
||||
return mx.concatenate(outputs, axis=1)
|
||||
|
||||
|
||||
class ResidualBlock(nn.Module):
|
||||
@ -287,7 +391,6 @@ class Model(nn.Module):
|
||||
self.model_type = args.model_type
|
||||
|
||||
self.backbone = Mamba2(args)
|
||||
# self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
|
||||
|
||||
if not args.tie_word_embeddings:
|
||||
self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
|
||||
@ -302,17 +405,26 @@ class Model(nn.Module):
|
||||
else:
|
||||
logits = self.lm_head(x)
|
||||
|
||||
print('ouput')
|
||||
return logits
|
||||
|
||||
def sanitize(self, weights):
|
||||
for k, v in weights.items():
|
||||
if "conv1d.weight" in k and v.ndim == 3:
|
||||
weights[k] = v.moveaxis(2, 1)
|
||||
return weights
|
||||
|
||||
def make_cache(self):
|
||||
return [MambaCache() for _ in range(len(self.layers))]
|
||||
|
||||
def sanitize(self, weights):
|
||||
sanitized = {}
|
||||
for k, v in weights.items():
|
||||
if "conv1d.weight" in k:
|
||||
# Ensure weights are in correct shape (channels, 1, kernel_size)
|
||||
if v.ndim == 2:
|
||||
v = mx.expand_dims(v, axis=1)
|
||||
elif v.ndim == 1:
|
||||
v = mx.expand_dims(mx.expand_dims(v, axis=0), axis=0)
|
||||
sanitized[k] = v
|
||||
else:
|
||||
sanitized[k] = v
|
||||
return sanitized
|
||||
|
||||
@property
|
||||
def layers(self):
|
||||
return self.backbone.layers
|
||||
|
||||
@ -146,6 +146,8 @@ def linear_to_lora_layers(
|
||||
elif model.model_type == "mamba2":
|
||||
keys = set(
|
||||
[
|
||||
"mixer.in_proj",
|
||||
"mixer.out_proj",
|
||||
]
|
||||
)
|
||||
else:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user