diff --git a/llms/mlx_lm/models/cache.py b/llms/mlx_lm/models/cache.py index a6a56e0a..4d04dac0 100644 --- a/llms/mlx_lm/models/cache.py +++ b/llms/mlx_lm/models/cache.py @@ -338,3 +338,30 @@ class MambaCache(_BaseCache): @state.setter def state(self, v): self.cache = v + + +class Mamba2Cache: + def __init__(self, num_layers): + self.conv_states = [None] * num_layers + self.ssm_states = [None] * num_layers + self.seqlen_offset = 0 + + def __getitem__(self, idx): + return (self.conv_states[idx], self.ssm_states[idx]) + + def __setitem__(self, idx, value): + self.conv_states[idx], self.ssm_states[idx] = value + + @property + def state(self): + return { + 'conv_states': self.conv_states, + 'ssm_states': self.ssm_states, + 'seqlen_offset': self.seqlen_offset + } + + @state.setter + def state(self, v): + self.conv_states = v['conv_states'] + self.ssm_states = v['ssm_states'] + self.seqlen_offset = v['seqlen_offset'] \ No newline at end of file diff --git a/llms/mlx_lm/models/mamba2 copy.py b/llms/mlx_lm/models/mamba2 copy.py index 5ecbb3d2..2d2f44fb 100644 --- a/llms/mlx_lm/models/mamba2 copy.py +++ b/llms/mlx_lm/models/mamba2 copy.py @@ -6,41 +6,37 @@ from typing import Tuple, Union import mlx.core as mx import mlx.nn as nn - from .base import BaseModelArgs +# python -m mlx_lm.generate --model rokyang/mamba2-130m-hf --prompt "hello how are you." @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 + num_heads: int + head_dim: int + vocab_size: int + hidden_size: int + state_size: int + num_hidden_layers: int + layer_norm_epsilon: float + expand: int + conv_kernel: int + n_groups: int + use_bias: bool + use_conv_bias: bool + initializer_range: float + residual_in_fp32: bool + time_step_min: float + time_step_max: float + time_step_floor: float + rescale_prenorm_residual: bool + use_cache: bool + rms_norm: bool + chunk_size: int + tie_word_embeddings: bool 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 + time_step_rank: Union[int, str] = "auto" + model_type: str = "mamba2" def __post_init__(self): if not hasattr(self, "intermediate_size"): @@ -79,15 +75,24 @@ class MambaRMSNormGated(nn.Module): hidden_states = hidden_states * mx.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states + class DepthWiseConv1d(nn.Module): - def __init__(self, channels, kernel_size, bias=True, groups=1, padding=0): + def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0): super().__init__() - self.channels = channels + self.in_channels = in_channels + self.out_channels = out_channels self.kernel_size = kernel_size self.padding = padding - self.groups = groups - self.weight = mx.random.normal((self.channels, kernel_size, 1)) - self.bias = mx.zeros((channels,)) if bias else None + self.groups = groups if groups is not None else in_channels + + # Ensure in_channels and out_channels are the same for depthwise conv + assert in_channels == out_channels, "In and out channels must be the same for depthwise convolution" + # Ensure groups is equal to in_channels for depthwise conv + assert self.groups == in_channels, "Groups must be equal to in_channels for depthwise convolution" + + # Initialize weight with shape (out_channels, kernel_size, 1) + self.weight = mx.random.normal((out_channels, kernel_size, 1)) + self.bias = mx.zeros((out_channels,)) if bias else None def __call__(self, x, cache=None): B, L, C = x.shape @@ -116,16 +121,17 @@ class Mamba2Mixer(nn.Module): self.hidden_size = args.hidden_size self.state_size = args.state_size self.num_heads = args.num_heads - self.head_dim = args.head_dim + self.head_dim = args.hidden_size // args.num_heads self.n_groups = args.n_groups self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size self.conv1d = DepthWiseConv1d( - channels=self.conv_dim, - kernel_size=self.conv_kernel_size, - bias=self.args.use_conv_bias, + 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=self.conv_kernel_size - 1, + padding=args.conv_kernel - 1 ) projection_size = self.intermediate_size + self.conv_dim + self.num_heads @@ -135,33 +141,35 @@ class Mamba2Mixer(nn.Module): bias=args.use_bias ) - self.act = nn.SiLU() - self.dt_bias = mx.ones((self.num_heads,)) - self.A_log = mx.log(mx.arange(1, self.num_heads + 1)) - self.D = mx.ones((self.num_heads,)) + self.A_log = mx.zeros(self.num_heads) + self.D = mx.ones(self.num_heads) + self.dt_bias = mx.zeros(self.num_heads) self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon) self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias) - def ssm_step(self, x, state=None): + def ssm_step(self, x, state, dt_proj): A = -mx.exp(self.A_log) D = self.D - deltaBC = self.x_proj(x) - delta, B, C = mx.split( - deltaBC, - indices_or_sections=[ - self.time_step_rank, - self.time_step_rank + self.ssm_state_size, - ], - axis=-1, - ) - delta = nn.softplus(self.dt_proj(delta)) - new_state = mx.expand_dims(delta * x, -1) * mx.expand_dims(B, 1) - if state is not None: - new_state += state * mx.exp(mx.expand_dims(delta, -1) * A) - y = (new_state @ mx.expand_dims(C, -1)).squeeze(2) - y = y + D * x + delta = nn.softplus(dt_proj + self.dt_bias) + + B, C = mx.split(x, indices_or_sections=[self.state_size * self.n_groups], axis=-1) + + batch_size = B.shape[0] + B = B.reshape(batch_size, self.n_groups, self.state_size) + C = C.reshape(batch_size, -1, self.state_size) + + delta = delta.reshape(batch_size, self.num_heads, 1) + A = A.reshape(1, self.num_heads, 1) + + if state is None: + new_state = delta * B + else: + new_state = delta * (B + state * mx.exp(delta * A)) + + y = mx.sum(new_state[:, :, None, :] * C[:, None, :, :], axis=(-1, -2)) + y = y + D * x[:, :self.num_heads] return y, new_state def __call__(self, x, cache): @@ -173,15 +181,28 @@ class Mamba2Mixer(nn.Module): for t in range(T): xt = x[:, t, :] xz = self.in_proj(xt) - x_t, z_t = xz.split(indices_or_sections=2, axis=1) + + x_t, z_t, dt_proj = mx.split( + xz, + indices_or_sections=[self.conv_dim, self.conv_dim + self.intermediate_size], + axis=-1 + ) + conv_out, cache[0] = self.conv1d(mx.expand_dims(x_t, 1), cache[0]) x_t = conv_out.squeeze(1) x_t = nn.silu(x_t) - y_t, cache[1] = self.ssm_step(x_t, cache[1]) + y_t, cache[1] = self.ssm_step(x_t, cache[1], dt_proj) z_t = nn.silu(z_t) - output_t = y_t * z_t + + # Element-wise multiplication + output_t = y_t[:, :, None] * z_t[:, None, :] + + # Sum across the second dimension to match the intermediate_size + output_t = output_t.sum(axis=1) + output_t = self.out_proj(output_t) outputs.append(output_t) + output = mx.stack(outputs, axis=1) return output @@ -240,6 +261,9 @@ class Model(nn.Module): else: logits = self.lm_head(x) + print(logits) + print(logits.shape) + return logits def sanitize(self, weights): diff --git a/llms/mlx_lm/models/mamba2.py b/llms/mlx_lm/models/mamba2.py index 7182fb69..7ac6ecc8 100644 --- a/llms/mlx_lm/models/mamba2.py +++ b/llms/mlx_lm/models/mamba2.py @@ -2,11 +2,13 @@ import math from dataclasses import dataclass, field -from typing import Tuple, Union +from typing import Tuple, Union, Optional -import mlx.core as mx import mlx.nn as nn +import mlx.core as mx + from .base import BaseModelArgs +from .cache import Mamba2Cache # python -m mlx_lm.generate --model rokyang/mamba2-130m-hf --prompt "hello how are you." @@ -46,22 +48,6 @@ class ModelArgs(BaseModelArgs): if self.time_step_rank == "auto": self.time_step_rank = math.ceil(self.hidden_size / 16) - -class Mamba2Cache: - def __init__(self): - self.cache = [None, None] - - def __setitem__(self, idx, value): - self.cache[idx] = value - - def __getitem__(self, idx): - return self.cache[idx] - - @property - def state(self): - return self.cache - - class MambaRMSNormGated(nn.Module): def __init__(self, hidden_size, eps=1e-6): super().__init__() @@ -75,6 +61,7 @@ class MambaRMSNormGated(nn.Module): hidden_states = hidden_states * mx.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states + class DepthWiseConv1d(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0): super().__init__() @@ -111,27 +98,22 @@ class DepthWiseConv1d(nn.Module): class Mamba2Mixer(nn.Module): - def __init__(self, args: ModelArgs): + def __init__(self, args, layer_idx): super().__init__() - self.args = args - self.intermediate_size = args.intermediate_size - self.time_step_rank = args.time_step_rank - self.conv_kernel_size = args.conv_kernel + self.layer_idx = layer_idx self.hidden_size = args.hidden_size - self.state_size = args.state_size + self.intermediate_size = args.intermediate_size self.num_heads = args.num_heads - self.head_dim = args.hidden_size // args.num_heads + self.head_dim = args.head_dim + self.ssm_state_size = args.state_size self.n_groups = args.n_groups - - self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size - self.conv1d = DepthWiseConv1d( - 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.conv_kernel_size = args.conv_kernel + self.use_conv_bias = args.use_conv_bias + self.use_bias = args.use_bias + self.time_step_min = args.time_step_min + self.time_step_max = args.time_step_max + self.chunk_size = args.chunk_size + self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size projection_size = self.intermediate_size + self.conv_dim + self.num_heads self.in_proj = nn.Linear( @@ -139,91 +121,151 @@ class Mamba2Mixer(nn.Module): projection_size, bias=args.use_bias ) - - self.dt_bias = mx.ones((self.num_heads,)) - self.A_log = mx.log(mx.arange(1, self.num_heads + 1)) - self.D = mx.ones((self.num_heads,)) - + self.conv1d = nn.Conv1d( + self.conv_dim, + self.conv_dim, + self.conv_kernel_size, + groups=self.conv_dim, + bias=self.use_conv_bias + ) + self.act = nn.SiLU() self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon) + self.out_proj = nn.Linear( + self.intermediate_size, + self.hidden_size, + bias=self.use_bias + ) - self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias) + self.A_log = mx.zeros(self.num_heads) + self.D = mx.ones(self.num_heads) + self.dt_bias = mx.zeros(self.num_heads) + + def __call__(self, input_states, cache): + batch_size, seq_len, _ = input_states.shape + dtype = input_states.dtype - def ssm_step(self, x, state, dt_proj): - print(f"ssm_step input shapes - x: {x.shape}, dt_proj: {dt_proj.shape}") - A = -mx.exp(self.A_log) - D = self.D - delta = nn.softplus(dt_proj + self.dt_bias) + projected_states = self.in_proj(input_states) - B, C = mx.split(x, indices_or_sections=[self.state_size * self.n_groups], axis=-1) - print(f"ssm_step split shapes - B: {B.shape}, C: {C.shape}") + # Calculate the sizes of each split + total_size = projected_states.shape[-1] + remaining_size = total_size - self.intermediate_size - self.conv_dim - self.num_heads + d_mlp = remaining_size // 2 + sizes = [ + d_mlp, + d_mlp, + self.intermediate_size, + self.conv_dim, + self.num_heads + ] - batch_size = B.shape[0] - B = B.reshape(batch_size, self.n_groups, self.state_size) - C = C.reshape(batch_size, -1, self.state_size) - print(f"After reshape - B: {B.shape}, C: {C.shape}") + # Perform the split operation + split_result = mx.split(projected_states, sizes, axis=-1) - delta = delta.reshape(batch_size, self.num_heads, 1) - A = A.reshape(1, self.num_heads, 1) + # Print debug information + print(f"Number of split parts: {len(split_result)}") + print(f"Shapes of split parts: {[part.shape for part in split_result]}") - if state is None: - new_state = delta * B + # Flexibly handle the split result + _, _, _, gate, hidden_states, dt = split_result + + if cache is not None: + conv_state = cache.conv_states[self.layer_idx] + if conv_state is None: + # Initialize conv_state if it's None + conv_state = mx.zeros((batch_size, 1, self.conv_kernel_size, hidden_states.shape[-1])) + + conv_state = mx.roll(conv_state, -1, -2) # Roll along the kernel dimension + + # Reshape hidden_states to match conv_state dimensions + hidden_states_reshaped = hidden_states[:, None, None, :] + + conv_state = mx.concat([conv_state[:, :, :-1, :], hidden_states_reshaped], axis=-2) + cache.conv_states[self.layer_idx] = conv_state + + # Adjust the convolution operation + hidden_states = mx.sum(conv_state * self.conv1d.weight[:, :, None, :], axis=(-2, -1)) + + if self.use_conv_bias: + hidden_states += self.conv1d.bias + hidden_states = self.act(hidden_states)[:, None, :] else: - new_state = delta * (B + state * mx.exp(delta * A)) - - print(f"Before final computation - new_state: {new_state.shape}, C: {C.shape}") - y = mx.sum(new_state[:, :, None, :] * C[:, None, :, :], axis=(-1, -2)) - y = y + D * x[:, :self.num_heads] - print(f"ssm_step output shape - y: {y.shape}") - return y, new_state + hidden_states = hidden_states.transpose(0, 2, 1) + hidden_states = self.act(self.conv1d(hidden_states)).transpose(0, 2, 1) - def __call__(self, x, cache): - B, T, D = x.shape - print(f"__call__ input shape - x: {x.shape}") - if cache is None: - cache = [None, None] + hidden_states, B, C = mx.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], axis=-1) - outputs = [] - for t in range(T): - xt = x[:, t, :] - xz = self.in_proj(xt) - print(f"After in_proj shape - xz: {xz.shape}") - - x_t, z_t, dt_proj = mx.split( - xz, - indices_or_sections=[self.conv_dim, self.conv_dim + self.intermediate_size], - axis=-1 - ) - print(f"After split shapes - x_t: {x_t.shape}, z_t: {z_t.shape}, dt_proj: {dt_proj.shape}") + A = -mx.exp(self.A_log.astype(mx.float32)) + dt = nn.softplus(dt + self.dt_bias) + dt = mx.clip(dt, self.time_step_min, self.time_step_max) - conv_out, cache[0] = self.conv1d(mx.expand_dims(x_t, 1), cache[0]) - x_t = conv_out.squeeze(1) - x_t = nn.silu(x_t) - print(f"Before ssm_step shape - x_t: {x_t.shape}") - y_t, cache[1] = self.ssm_step(x_t, cache[1], dt_proj) - z_t = nn.silu(z_t) - print(f"After ssm_step shapes - y_t: {y_t.shape}, z_t: {z_t.shape}") - - # Element-wise multiplication - output_t = y_t[:, :, None] * z_t[:, None, :] - print(f"After multiplication shape - output_t: {output_t.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 + hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).astype(mx.float32) + B = B.reshape(batch_size, seq_len, -1, self.ssm_state_size).astype(mx.float32) + C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).astype(mx.float32) + + B = mx.repeat(B, repeats=self.num_heads // self.n_groups, axis=2) + C = mx.repeat(C, repeats=self.num_heads // self.n_groups, axis=2) + + if cache is not None and cache.seqlen_offset > 0: + ssm_state = cache.ssm_states[self.layer_idx] + dA = mx.exp(dt[:, None, :, None] * A[None, :, None, None]) + dB = dt[:, None, :, None] * B + dBx = dB * hidden_states[:, :, :, None] + ssm_state = ssm_state * dA + dBx + cache.ssm_states[self.layer_idx] = ssm_state + + y = mx.sum(ssm_state * C[:, None, :, :], axis=-1) + D = self.D[None, :, None].expand(self.D.shape[0], self.head_dim) + y = y + hidden_states * D + + y = y.reshape(batch_size, -1)[:, None, :] + else: + # Implement chunked computation here (simplified version) + pad_size = self.chunk_size - (seq_len % self.chunk_size) + hidden_states_padded = mx.pad(hidden_states, [(0, 0), (0, pad_size), (0, 0), (0, 0)]) + B_padded = mx.pad(B, [(0, 0), (0, pad_size), (0, 0), (0, 0)]) + C_padded = mx.pad(C, [(0, 0), (0, pad_size), (0, 0), (0, 0)]) + + chunks = seq_len // self.chunk_size + (1 if pad_size > 0 else 0) + y_list = [] + ssm_state = mx.zeros((batch_size, self.num_heads, self.head_dim, self.ssm_state_size)) + + for i in range(chunks): + chunk_start = i * self.chunk_size + chunk_end = (i + 1) * self.chunk_size + chunk_h = hidden_states_padded[:, chunk_start:chunk_end] + chunk_B = B_padded[:, chunk_start:chunk_end] + chunk_C = C_padded[:, chunk_start:chunk_end] + + chunk_dt = dt[:, chunk_start:chunk_end] + dA = mx.exp(chunk_dt[:, :, None, None] * A[None, None, :, None]) + dB = chunk_dt[:, :, None, None] * chunk_B + dBx = dB * chunk_h[:, :, :, None] + + chunk_y = mx.zeros_like(chunk_h) + for j in range(self.chunk_size): + ssm_state = ssm_state * dA[:, j] + dBx[:, j] + chunk_y[:, j] = mx.sum(ssm_state * chunk_C[:, j], axis=-1) + + y_list.append(chunk_y) + + y = mx.concat(y_list, axis=1) + if pad_size > 0: + y = y[:, :seq_len] + + D = self.D[None, :, None].expand(self.D.shape[0], self.head_dim) + y = y + hidden_states * D + y = y.reshape(batch_size, seq_len, -1) + + y = self.norm(y, gate) + contextualized_states = self.out_proj(y.astype(dtype)) + + return contextualized_states class Mamba2Block(nn.Module): - def __init__(self, args: ModelArgs): + def __init__(self, args: ModelArgs, layer_idx: int): super().__init__() - self.mixer = Mamba2Mixer(args) + self.mixer = Mamba2Mixer(args, layer_idx) self.norm = nn.RMSNorm(args.hidden_size) def __call__(self, x: mx.array, cache): @@ -235,7 +277,7 @@ class Mamba2(nn.Module): 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.layers = [Mamba2Block(args, idx) for idx in range(args.num_hidden_layers)] self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon) def __call__( @@ -274,6 +316,9 @@ class Model(nn.Module): else: logits = self.lm_head(x) + print(logits) + print(logits.shape) + return logits def sanitize(self, weights): @@ -282,8 +327,8 @@ class Model(nn.Module): weights[k] = v.moveaxis(2, 1) return weights - def make_cache(self, batch_size: int = 1): - return [Mamba2Cache() for _ in range(len(self.layers))] + def make_cache(self): + return [Mamba2Cache(self.args.num_hidden_layers) for _ in range(len(self.layers))] @property def layers(self):