mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-12-16 02:08:55 +08:00
updates
This commit is contained in:
Goekdeniz-Guelmez
@@ -32,259 +32,272 @@ class ModelArgs(BaseModelArgs):
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rms_norm: bool
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chunk_size: int
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tie_word_embeddings: bool
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intermediate_size: int = None
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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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self.intermediate_size = int(self.expand * self.hidden_size) # E*D = ED
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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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def selective_scan(x, A, B, C, chunk_size):
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"""
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Selective scan implementation for training.
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Arguments
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x: (batch, seqlen, n_heads, d_head)
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A: (batch, seqlen, n_heads)
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B: (batch, seqlen, n_heads, d_state)
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C: (batch, seqlen, n_heads, d_state)
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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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Return
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y: (batch, seqlen, n_heads, d_head)
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"""
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assert x.shape[1] % chunk_size == 0
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# Reshape into chunks
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def chunk_reshape(m):
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shape = list(m.shape)
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shape[1:2] = [shape[1] // chunk_size, chunk_size]
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return m.reshape(shape)
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x, A, B, C = map(chunk_reshape, (x, A, B, C))
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A = mx.transpose(A, [0, 3, 1, 2])
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# Compute cumulative sums
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A_cumsum = mx.cumsum(A, axis=-1)
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# Process chunks
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L = mx.exp(selective_cumsum(A))
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Y_diag = mx.einsum('bclhn,bcshn,bhcls,bcshp->bclhp', C, B, L, x)
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decay_states = mx.exp(A_cumsum[..., -1:] - A_cumsum)
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states = mx.einsum('bclhn,bhcl,bclhp->bchpn', B, decay_states, x)
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initial_states = mx.zeros_like(states[:, :1])
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states = mx.concatenate([initial_states, states], axis=1)
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decay_chunk = mx.exp(selective_cumsum(mx.pad(A_cumsum[..., -1], ((0,0), (0,0), (1,0)))))
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new_states = mx.einsum('bhzc,bchpn->bzhpn', decay_chunk, states)
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states = new_states[:, :-1]
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state_decay_out = mx.exp(A_cumsum)
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Y_off = mx.einsum('bclhn,bchpn,bhcl->bclhp', C, states, state_decay_out)
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Y = (Y_diag + Y_off).reshape((-1, x.shape[1] * chunk_size, *Y_diag.shape[-2:]))
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return Y
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def forward(self, hidden_states, gate=None):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(mx.float32)
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def selective_cumsum(x: mx.array) -> mx.array:
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"""Stable selective cumulative sum calculation."""
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T = x.shape[-1]
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x = mx.repeat(x[..., None], T, axis=-1)
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mask = mx.tril(mx.ones((T, T)), k=-1)
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x = x * mask
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x_cumsum = mx.cumsum(x, axis=-2)
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mask = mx.tril(mx.ones((T, T)), k=0)
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return mx.where(mask, x_cumsum, float('-inf'))
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if gate is not None:
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hidden_states = hidden_states * nn.functional.silu(gate.to(mx.float32))
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * math.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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class Mamba2Block(nn.Module):
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class Mamba2Mixer(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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# Model dimensions
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self.hidden_size = args.hidden_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.intermediate_size = int(args.expand * args.hidden_size)
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# Internal cache state
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self.conv_state = None
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self.ssm_state = None
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# Convolution parameters
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self.conv_kernel = args.conv_kernel
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self.use_conv_bias = args.use_conv_bias
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# Project input to get various components
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d_in_proj = (2 * args.intermediate_size + 2 * self.args.n_groups * args.state_size + args.num_heads)
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# Time step parameters
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self.time_step_rank = int(args.time_step_rank)
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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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# Processing parameters
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self.chunk_size = args.chunk_size
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self.layer_norm_epsilon = args.layer_norm_epsilon
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# Calculate dimensions
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self.conv_dim = (self.intermediate_size +
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2 * self.n_groups * self.ssm_state_size)
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projection_size = (self.intermediate_size +
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self.conv_dim +
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self.num_heads)
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# Initialize layers
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self.in_proj = nn.Linear(
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args.hidden_size,
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d_in_proj,
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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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in_channels=self.conv_dim,
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out_channels=self.conv_dim,
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kernel_size=self.conv_kernel,
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groups=self.conv_dim,
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padding=self.conv_kernel - 1,
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bias=self.use_conv_bias
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)
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# Initialize parameters
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self.dt_bias = mx.ones(self.num_heads)
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A = mx.arange(1, self.num_heads + 1)
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self.A_log = mx.log(A)
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self.D = mx.ones(self.num_heads)
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# Output layers
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self.norm = MambaRMSNormGated(
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self.intermediate_size,
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eps=self.layer_norm_epsilon
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)
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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=args.use_bias
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)
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# Convolution layer
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conv_dim = args.intermediate_size + 2 * self.args.n_groups * args.state_size
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self.conv1d = nn.Conv1d(
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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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padding=args.conv_kernel - 1,
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bias=args.use_conv_bias
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)
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# SSM parameters
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dt_init_floor = math.log(args.time_step_floor)
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self.dt_bias = mx.zeros((args.num_heads,)) * args.initializer_range
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self.A_log = mx.zeros((args.num_heads,)) * args.initializer_range
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self.D = mx.zeros((args.num_heads,)) * args.initializer_range
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# Output projections
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self.norm = nn.RMSNorm(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 __call__(self, x: mx.array, cache=None) -> mx.array:
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return self.forward_training(x) if x.shape[1] > 1 else self.forward_inference(x, cache)
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def forward_training(self, u: mx.array) -> mx.array:
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# Reset cache during training
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self.cache = None
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def reshape_into_chunks(self, tensor, pad_size, chunk_size):
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if pad_size > 0:
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pad_shape = list(tensor.shape)
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pad_shape[1] = pad_size
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padding = mx.zeros(pad_shape, dtype=tensor.dtype)
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tensor = mx.concatenate([tensor, padding], axis=1)
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# Input projection and splitting
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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.intermediate_size,
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self.args.intermediate_size + 2 * self.args.state_size
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],
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axis=-1
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)
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chunk_shape = list(tensor.shape)
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chunk_shape[1] = -1
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chunk_shape.insert(2, chunk_size)
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return tensor.reshape(chunk_shape)
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# Time step processing
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def segment_sum(self, x):
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return mx.cumsum(x, axis=-1)
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def process_single_token(self, hidden_states, B, C, dt, cache):
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batch_size = hidden_states.shape[0]
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# Process convolution state
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if cache is not None:
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conv_state = cache.conv_states
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# Roll the conv state and update the last position
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conv_state = mx.roll(conv_state, shift=-1, axis=-1)
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# Create new conv state with updated last position
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new_conv_state = mx.array(conv_state)
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new_conv_state = new_conv_state.at[:, :, -1].add(hidden_states)
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conv_state = new_conv_state
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# Compute convolution
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conv_out = mx.sum(conv_state * self.conv1d.weight[:, 0, :], axis=-1)
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if self.use_conv_bias:
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conv_out = conv_out + self.conv1d.bias
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# Apply SiLU activation
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conv_out = mx.sigmoid(conv_out) * conv_out
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else:
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# Initialize new cache
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conv_state = mx.zeros((batch_size, self.conv_dim, self.conv_kernel - 1))
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conv_out = self.conv1d(hidden_states)
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conv_out = mx.sigmoid(conv_out) * conv_out
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# Process SSM
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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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self.time_step_min,
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self.time_step_max
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)
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# Convolution processing
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xBC_t = mx.transpose(xBC, [0, 2, 1])
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conv_out = self.conv1d(xBC_t)
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xBC = mx.transpose(conv_out, [0, 2, 1])[:, :u.shape[1]]
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xBC = mx.sigmoid(xBC) * xBC # SiLU
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# Split states
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x, B, C = mx.split(
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xBC,
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[self.args.intermediate_size, self.args.state_size],
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axis=-1
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)
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# Reshape for selective scan
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x = x.reshape((-1, x.shape[1], self.args.num_heads, self.args.head_dim))
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A = -mx.exp(self.A_log)
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dA = mx.exp(dt * A[None, :])
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if cache is not None:
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ssm_state = cache.ssm_states
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else:
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ssm_state = mx.zeros(
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(batch_size, self.num_heads, self.head_dim, self.ssm_state_size)
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)
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# Compute SSM updates
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dBx = mx.einsum('bh,bhs,bhd->bhds', dt, B, hidden_states)
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next_state = ssm_state * dA[:, :, None, None] + dBx
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y = mx.einsum('bhds,bhs->bhd', next_state, C)
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# Add skip connection
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y = y + hidden_states * self.D[None, :, None]
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return y, conv_state, next_state
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# Apply selective scan
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y = selective_scan(
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x * dt[..., None],
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A * dt,
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B[..., None, :],
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C[..., None, :],
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self.args.chunk_size
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def process_long_sequence(self, hidden_states, B, C, dt, ssm_state):
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batch_size, seq_len = hidden_states.shape[:2]
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pad_size = self.chunk_size - (seq_len % self.chunk_size)
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# Reshape into chunks
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x_chunks = self.reshape_into_chunks(hidden_states, pad_size, self.chunk_size)
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B_chunks = self.reshape_into_chunks(B, pad_size, self.chunk_size)
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C_chunks = self.reshape_into_chunks(C, pad_size, self.chunk_size)
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# Process time steps
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dt = nn.softplus(dt + self.dt_bias)
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dt = mx.clip(dt, self.time_step_min)
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# Prepare matrices
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A = -mx.exp(self.A_log)
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A = A * dt[:, None]
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# Process chunks
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A_chunks = self.reshape_into_chunks(
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mx.broadcast_to(A, (batch_size, seq_len + pad_size, self.num_heads)),
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pad_size,
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self.chunk_size
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)
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# Output processing
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y = y + x * self.D[None, None, :, None]
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y = y.reshape((-1, y.shape[1], self.args.intermediate_size))
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y = self.norm(y, z)
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y = self.out_proj(y)
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return y
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def forward_inference(self, u: mx.array, cache=None) -> mx.array:
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"""Single token processing during inference."""
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assert u.shape[1] == 1, "Inference mode expects single token"
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# Compute cumulative sums
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A_cumsum = mx.cumsum(A_chunks, axis=-1)
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L = mx.exp(self.segment_sum(A_chunks))
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batch_size = u.shape[0]
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# Use provided cache or create new one
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self.cache = cache if cache is not None else Mamba2Cache.get_cache(self.args, batch_size, None)
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# Process diagonal blocks
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G = mx.einsum('...lhn,...shn->...lsh', C_chunks, B_chunks)
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M = G * L[..., None, :]
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Y_diag = mx.einsum('...lsh,...sh->...lh', M, x_chunks)
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# Process off-diagonal blocks
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decay_states = mx.exp(A_cumsum[..., -1:] - A_cumsum)
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B_decay = B_chunks * decay_states[..., None]
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states = mx.einsum('...shn,...sh->...hn', B_decay, x_chunks)
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# Combine results
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y = Y_diag + states
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# Remove padding if necessary
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if pad_size > 0:
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y = y[:, :seq_len]
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return y, ssm_state
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def __call__(self, x: mx.array, cache: Optional[Mamba2Cache] = None) -> mx.array:
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batch_size, seq_len, _ = x.shape
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# Project input
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zxbcdt = self.in_proj(mx.squeeze(u, 1))
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parts = mx.split(
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zxbcdt,
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[
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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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],
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axis=-1
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)
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z, xBC = parts[0], parts[1]
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dt = zxbcdt[:, -self.args.num_heads:] # Extract dt separately
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# Update convolution state and apply
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conv_state = self.cache.update_conv_state(xBC)
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xBC = mx.sum(
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conv_state * mx.transpose(self.conv1d.weight, [1, 0, 2]),
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axis=-1
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)
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if self.args.use_conv_bias:
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xBC = xBC + self.conv1d.bias
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xBC = mx.sigmoid(xBC) * xBC # SiLU
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# Split states and ensure proper shapes
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x_splits = mx.split(
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xBC,
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[self.args.intermediate_size, self.args.state_size],
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axis=-1
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)
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x, B, C = x_splits[0], x_splits[1], x_splits[2]
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projected_states = self.in_proj(x.squeeze(1))
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# Process time steps - ensure proper broadcasting
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dt = mx.reshape(dt, (batch_size, self.args.num_heads))
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dt = mx.clip(
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nn.softplus(dt + self.dt_bias[None, :]),
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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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# Calculate d_mlp based on projection size
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d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 *
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self.n_groups * self.ssm_state_size - self.num_heads) // 2
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# SSM step with explicit shapes
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A = -mx.exp(self.A_log)
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dA = mx.exp(dt * A[None, :]) # Shape: (batch_size, num_heads)
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# Split projections with corrected dimensions
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splits = [
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d_mlp, # z0
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d_mlp, # x0
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self.intermediate_size, # gate
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self.conv_dim, # hidden_states
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self.num_heads # dt
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]
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# Reshape x considering intermediate size
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# x shape should be (batch_size * num_heads, head_dim)
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x = mx.reshape(x, (batch_size, self.args.num_heads, -1))
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assert x.shape[-1] == self.args.head_dim, f"Head dimension mismatch: {x.shape[-1]} vs {self.args.head_dim}"
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z0, x0, x1, gate, hidden_states, dt = projected_states.split(splits, axis=-1)
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# Reshape B and C for ssm computation
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B = mx.reshape(B, (batch_size, -1)) # Should be (batch_size, state_size)
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C = mx.reshape(C, (batch_size, -1)) # Should be (batch_size, state_size)
|
||||
# Split hidden states into components
|
||||
x_conv, BC = mx.split(hidden_states, [self.intermediate_size], axis=-1)
|
||||
B, C = mx.split(BC, [self.n_groups * self.ssm_state_size], axis=-1)
|
||||
|
||||
# Compute dBx with explicit shapes
|
||||
dBx = mx.einsum('bh,bs,bhd->bhds', dt, B, x)
|
||||
# Process based on sequence length
|
||||
if seq_len > 1 and cache is None:
|
||||
y, next_state = self.process_long_sequence(
|
||||
x_conv, B, C, dt,
|
||||
mx.zeros((batch_size, self.num_heads, self.head_dim, self.ssm_state_size))
|
||||
)
|
||||
else:
|
||||
# Reshape for single token processing
|
||||
x_conv = x_conv.reshape(batch_size, -1, self.head_dim)
|
||||
B = B.reshape(batch_size, self.num_heads, -1)
|
||||
C = C.reshape(batch_size, self.num_heads, -1)
|
||||
y, conv_state, next_state = self.process_single_token(x_conv, B, C, dt, cache)
|
||||
|
||||
if cache is not None:
|
||||
cache.update(conv_state, next_state)
|
||||
|
||||
ssm_state = self.cache.update_ssm_state(dA, dBx)
|
||||
|
||||
y = mx.einsum('bhds,bs->bhd', ssm_state, C)
|
||||
y = y + x * self.D[None, :, None]
|
||||
y = mx.reshape(y, (batch_size, self.args.intermediate_size))
|
||||
|
||||
# Output processing
|
||||
y = self.norm(y, z)
|
||||
y = self.out_proj(y)
|
||||
|
||||
return mx.expand_dims(y, 1)
|
||||
|
||||
# Apply normalization and final projection
|
||||
y = self.norm(y) * gate
|
||||
return self.out_proj(y)
|
||||
|
||||
|
||||
class ResidualBlock(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
self.mixer = Mamba2Block(args)
|
||||
self.mixer = Mamba2Mixer(args)
|
||||
self.norm = nn.RMSNorm(args.hidden_size)
|
||||
|
||||
def __call__(self, x: mx.array, cache=None) -> mx.array:
|
||||
def __call__(self, x: mx.array, cache: Optional[Mamba2Cache] = None) -> mx.array:
|
||||
return self.mixer(self.norm(x), cache) + x
|
||||
|
||||
|
||||
class Mamba2Model(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
@@ -295,19 +308,20 @@ class Mamba2Model(nn.Module):
|
||||
|
||||
def __call__(self, x: mx.array, cache=None) -> mx.array:
|
||||
x = self.embeddings(x)
|
||||
|
||||
if cache is None:
|
||||
cache = [None] * len(self.layers)
|
||||
|
||||
for layer, layer_cache in zip(self.layers, cache):
|
||||
x = layer(x, layer_cache)
|
||||
return self.norm_f(x)
|
||||
|
||||
return self.norm_f(x)
|
||||
|
||||
class Model(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
self.args = args
|
||||
self.backbone = Mamba2Model(args)
|
||||
|
||||
if not args.tie_word_embeddings:
|
||||
self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
|
||||
|
||||
@@ -324,17 +338,24 @@ class Model(nn.Module):
|
||||
return logits
|
||||
|
||||
def make_cache(self, batch_size=1):
|
||||
return [Mamba2Cache(
|
||||
batch_size=batch_size,
|
||||
intermediate_size=self.args.intermediate_size,
|
||||
state_size=self.args.state_size,
|
||||
conv_kernel=self.args.conv_kernel,
|
||||
num_heads=self.args.num_heads,
|
||||
head_dim=self.args.head_dim
|
||||
) for _ in range(len(self.backbone.layers))]
|
||||
return [
|
||||
Mamba2Cache(
|
||||
batch_size=batch_size,
|
||||
conv_dim=self.args.intermediate_size + 2 * self.args.n_groups * self.args.state_size,
|
||||
kernel_size=self.args.conv_kernel,
|
||||
num_heads=self.args.num_heads,
|
||||
head_dim=self.args.head_dim,
|
||||
state_size=self.args.state_size
|
||||
)
|
||||
for _ in range(len(self.backbone.layers))
|
||||
]
|
||||
|
||||
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
|
||||
return weights
|
||||
|
||||
@property
|
||||
def layers(self):
|
||||
return self.backbone.layers
|
||||
|
||||
Reference in New Issue
Block a user