Create mamba2.py

llms/mlx_lm/models/mamba2.py

@@ -0,0 +1,264 @@
+# Copyright © 2024 Apple Inc.
+
+import math
+from dataclasses import dataclass, field
+from typing import Optional, Tuple, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str = "mamba2"
+    num_heads: int = 128
+    head_dim: int = 64
+    vocab_size: int = 32768
+    hidden_size: int = 4096
+    state_size: int = 128
+    num_hidden_layers: int = 64
+    layer_norm_epsilon: float = 1e-5
+    pad_token_id: int = 1
+    bos_token_id: int = 0
+    eos_token_id: int = 2
+    expand: int = 2
+    conv_kernel: int = 4
+    n_groups: int = 8
+    use_bias: bool = False
+    use_conv_bias: bool = True
+    hidden_act: str = "silu"
+    initializer_range: float = 0.1
+    residual_in_fp32: bool = True
+    time_step_rank: Union[int, str] = "auto"
+    time_step_min: float = 0.001
+    time_step_max: float = 0.1
+    time_step_floor: float = 1e-4
+    time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
+    rescale_prenorm_residual: bool = False
+    use_cache: bool = True
+    rms_norm: bool = True
+    chunk_size: int = 256
+    tie_word_embeddings: bool = False
+
+    def __post_init__(self):
+        if not hasattr(self, "intermediate_size"):
+            self.intermediate_size = int(self.expand * self.hidden_size)
+        if not hasattr(self, "head_dim"):
+            self.head_dim = self.hidden_size // self.num_heads
+        if self.time_step_rank == "auto":
+            self.time_step_rank = math.ceil(self.hidden_size / 16)
+
+
+class Mamba2Cache:
+    def __init__(self, num_layers):
+        self.cache = [[None, None] for _ in range(num_layers)]
+
+    def __getitem__(self, idx):
+        return self.cache[idx]
+
+    def __setitem__(self, idx, value):
+        self.cache[idx] = value
+
+
+class MambaRMSNormGated(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = mx.ones((hidden_size,))
+        self.variance_epsilon = eps
+
+    def __call__(self, hidden_states, gate=None):
+        if gate is not None:
+            hidden_states = hidden_states * nn.silu(gate)
+        variance = mx.mean(hidden_states ** 2, axis=-1, keepdims=True)
+        hidden_states = hidden_states * mx.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states
+
+
+class Mamba2Mixer(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.hidden_size = args.hidden_size
+        self.intermediate_size = args.intermediate_size
+        self.conv_kernel_size = args.conv_kernel
+        self.state_size = args.state_size
+        self.num_heads = args.num_heads
+        self.head_dim = args.head_dim
+        self.n_groups = args.n_groups
+        self.time_step_rank = args.time_step_rank
+
+        projection_size = self.intermediate_size + self.intermediate_size + 2 * self.n_groups * self.state_size + self.num_heads
+        self.in_proj = nn.Linear(
+            self.hidden_size,
+            projection_size,
+            bias=args.use_bias
+        )
+
+        self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size
+        self.conv1d = nn.Conv1d(
+            in_channels=self.conv_dim,
+            out_channels=self.conv_dim,
+            bias=args.use_conv_bias,
+            kernel_size=args.conv_kernel,
+            groups=self.conv_dim,
+            padding=args.conv_kernel - 1,
+        )
+
+        self.act = nn.SiLU()
+        self.dt_bias = mx.ones((self.num_heads,))
+        self.A_log = mx.log(mx.arange(1, self.num_heads + 1, dtype=mx.float32))
+        self.D = mx.ones((self.num_heads,))
+
+        self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
+        self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon)
+
+    def ssm_step(self, x, dt, state):
+        A = -mx.exp(self.A_log)
+        D = self.D
+
+        deltaBC = self.in_proj(x)
+        gate, conv_state, time_step = mx.split(
+            deltaBC,
+            [self.intermediate_size, self.intermediate_size + 2 * self.n_groups * self.state_size],
+            axis=-1
+        )
+
+        conv_state = conv_state.transpose(0, 2, 1)
+        conv_out = self.conv1d(conv_state)
+        conv_out = conv_out.transpose(0, 2, 1)
+        conv_out = self.act(conv_out)
+
+        x_and_conv_out, B, C = mx.split(
+            conv_out,
+            [self.intermediate_size, self.n_groups * self.state_size],
+            axis=-1
+        )
+
+        dt = nn.softplus(time_step + self.dt_bias)
+        dt = mx.clip(dt, self.args.time_step_min, self.args.time_step_max)
+
+        B = B.reshape(-1, self.num_heads, self.head_dim, self.state_size)
+        C = C.reshape(-1, self.num_heads, self.head_dim, self.state_size)
+
+        dA = mx.exp(dt[:, :, None, None] * A[None, :, None, None])
+        dB = dt[:, :, None, None] * B
+
+        new_state = state * dA + x_and_conv_out[:, :, None, None] * dB
+        y = mx.sum(new_state * C, axis=-1)
+        y = y + D[None, :, None] * x_and_conv_out
+
+        y = self.norm(y.reshape(-1, self.intermediate_size), gate)
+        output = self.out_proj(y)
+
+        return output, new_state
+
+    def __call__(
+        self,
+        x: mx.array,
+        cache = None
+    ):
+        B, L, _ = x.shape
+
+        if cache[0] is not None:  # Using cached state
+            conv_state, ssm_state = cache
+            x = x[:, -1:]
+            output, new_ssm_state = self.ssm_step(x, None, ssm_state)
+            cache[1] = new_ssm_state  # Update SSM state in cache
+        else:
+            conv_state, ssm_state = None, None
+            outputs = []
+            for t in range(L):
+                x = x[:, t:t+1]
+                output, ssm_state = self.ssm_step(x, None, ssm_state)
+                outputs.append(output)
+            output = mx.concatenate(outputs, axis=1)
+            cache[1] = ssm_state  # Store final SSM state in cache
+
+        # Update conv state in cache
+        new_conv_state = x[:, -self.conv_kernel_size:]
+        cache[0] = new_conv_state
+        
+        return output
+
+
+class Mamba2Block(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.residual_in_fp32 = args.residual_in_fp32
+        self.norm = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
+        self.mixer = Mamba2Mixer(args)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        h = self.mixer(self.norm(inputs), cache_params=cache)
+        r = inputs + h
+        return r
+
+
+class Mamba2(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.embeddings = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [Mamba2Block(args) for idx in range(args.num_hidden_layers)]
+        self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None
+    ):
+        hidden_states = self.embeddings(inputs)
+        
+        if cache is None:
+            cache = Mamba2Cache(len(self.layers))
+
+        for i, layer in enumerate(self.layers):
+            hidden_states = layer(hidden_states, cache[i])
+
+        hidden_states = self.norm_f(hidden_states)
+        return hidden_states
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+        self.backbone = Mamba2(args)
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None
+    ):
+        B, T = inputs.shape
+
+        x = self.backbone(inputs, cache)
+
+        if self.args.tie_word_embeddings:
+            logits = self.backbone.embeddings.as_linear(x)
+        else:
+            logits = self.lm_head(x)
+        return logits
+    
+    def sanitize_mabey(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, batch_size: int = 1):
+        return Mamba2Cache(len(self.backbone.layers))
+    
+    @property
+    def layers(self):
+        return self.backbone.layers