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https://github.com/ml-explore/mlx-examples.git
synced 2025-06-27 19:31:20 +08:00
inference works but is hella slow
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Goekdeniz-Guelmez
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9ab581d678
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@ -342,7 +342,7 @@ class MambaCache(_BaseCache):
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class Mamba2Cache(_BaseCache):
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conv_states: Optional[mx.array] = None
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ssm_states: Optional[mx.array] = None
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ssm_state: Optional[mx.array] = None
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def __getitem__(self, idx: int) -> Optional[mx.array]:
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if idx == 0:
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@ -103,89 +103,55 @@ class DepthWiseConv1d(nn.Module):
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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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# Weight 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: mx.array, cache=None, cache_idx: int = 0) -> mx.array:
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def __call__(self, x: mx.array, cache=None) -> mx.array:
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B, L, C = x.shape
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K = self.kernel_size
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# Validate input dimensions
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assert C == self.in_channels, f"Input channels {C} doesn't match expected {self.in_channels}"
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# Handle padding and caching
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if cache is not None:
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conv_states = cache[cache_idx]
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if cache is not None and 'conv_states' in cache:
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conv_states = cache['conv_states']
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if conv_states is not None:
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# Validate cache shape
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assert conv_states.shape[0] == B, "Cache batch size mismatch"
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assert conv_states.shape[2] == C, "Cache channel count mismatch"
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x = mx.concatenate([conv_states, x], axis=1)
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L = x.shape[1]
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else:
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# Add left padding of size (kernel_size - 1)
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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]
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# Pre-allocate output array if possible
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outputs = []
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# Process each channel independently
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for c in range(C):
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# Extract and prepare channel data
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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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# Prepare filter weights
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w_c = self.weight[c] # Get channel weights
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# Ensure filter is 3D: [depth(1), in_channels(1), kernel_size]
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# Process each channel independently
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outputs = []
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for c in range(C):
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x_c = x[:, :, c]
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x_c = mx.expand_dims(x_c, axis=1)
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w_c = self.weight[c]
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if w_c.ndim == 2:
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w_c = mx.expand_dims(w_c, axis=0)
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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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# Handle inference mode (single token)
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if L < K:
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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 convolution
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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
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)
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# Apply 1D convolution
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try:
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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 # Padding already handled
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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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# Remove singleton dimension and add to outputs
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outputs.append(mx.squeeze(y_c, axis=1))
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except Exception as e:
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raise RuntimeError(f"Convolution failed for channel {c}. Shapes: input={x_c.shape}, weight={w_c.shape}") from e
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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))
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# Stack channel outputs along last dimension
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y = mx.stack(outputs, axis=-1) # Shape: [B, L', C]
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y = mx.stack(outputs, axis=-1)
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# Update cache if needed
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# Update cache
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if cache is not None:
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# Store last (kernel_size - 1) tokens or entire input if shorter
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new_cache = x[:, -(K-1):, :] if L >= K else x
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cache[cache_idx] = new_cache
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if new_cache.shape != cache[cache_idx].shape:
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cache[cache_idx] = new_cache
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print(f"Cache updated at index {cache_idx}")
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else:
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print(f"Skipping cache update at index {cache_idx}, shapes are identical.")
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cache['conv_states'] = x[:, -K+1:, :] if x.shape[1] >= K else x
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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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@ -217,34 +183,22 @@ class Mamba2Block(nn.Module):
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self.out_proj.weight = self.out_proj.weight * layer_scale
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def __call__(self, x: mx.array, cache=None):
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# if cache is not None and self.args.use_cache:
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if cache is not None:
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return self.step(x, cache)
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# Calculate sizes
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# Regular forward pass code remains the same...
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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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# Compute A
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A = -mx.exp(self.A_log)
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# Project input
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zxbcdt = self.in_proj(x)
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# Correct splits for z, xBC, dt
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splits = [
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d_model, # z
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d_model + 2 * d_state, # xBC (delta, B, C concatenated)
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n_heads # dt
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]
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# Split using cumulative indices
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splits = [d_model, d_model + 2 * d_state, n_heads]
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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]:]
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# Process dt
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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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@ -252,46 +206,26 @@ class Mamba2Block(nn.Module):
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)
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dt = mx.maximum(dt, self.args.time_step_floor)
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# Process convolution
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xBC = silu(self.conv1d(xBC))
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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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# Reshape for SSM computation
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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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# Compute SSM
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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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C,
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self.args.chunk_size
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)
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# Add skip connection
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y, ssm_state = ssd(x * mx.expand_dims(dt, -1), A * dt, B, C, self.args.chunk_size)
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y = y + x * mx.expand_dims(self.D, -1)
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# Reshape back
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y = mx.reshape(y, (b, l, h * p))
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# Apply norm and projection
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y = self.norm(y + z)
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y = self.out_proj(y)
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# Update cache if needed
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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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# Cast if needed
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if self.args.residual_in_fp32:
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y.astype(mx.float32)
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y = y.astype(mx.float32)
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return y
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@ -300,9 +234,17 @@ class Mamba2Block(nn.Module):
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seq_len = u.shape[1]
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outputs = []
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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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# Initialize cache if needed
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if cache.conv_states is None:
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conv_dim = self.args.intermediate_size + 2 * self.args.state_size
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cache.conv_states = mx.zeros((
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batch_size,
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self.args.conv_kernel - 1,
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conv_dim
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))
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if cache.ssm_state is None:
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cache.ssm_state = 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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@ -310,26 +252,17 @@ class Mamba2Block(nn.Module):
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))
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for pos in range(seq_len):
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# Getting stuck here in last position, also cache from pos 0 is the same.
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# Get single token
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u_t = u[:, pos:pos+1, :]
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# Project input
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zxbcdt = self.in_proj(u_t)
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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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# Split projected input
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# conv_dim = d_model + 2 * d_state (this should match self.conv1d.in_channels)
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z = zxbcdt[:, :, :d_model]
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xBC = zxbcdt[:, :, d_model:d_model + 2*d_state + d_model] # Include the full conv dimension
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xBC = zxbcdt[:, :, d_model:d_model + 2*d_state + d_model]
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dt = zxbcdt[:, :, -(n_heads):]
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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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@ -338,49 +271,43 @@ class Mamba2Block(nn.Module):
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)
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dt = mx.maximum(dt, self.args.time_step_floor)
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# Process convolution with correct dimensions
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xBC = self.conv1d(xBC, cache=cache, cache_idx=0)
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# Create a temporary cache dictionary for the convolution
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conv_cache = {'conv_states': cache.conv_states}
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xBC = self.conv1d(xBC, cache=conv_cache)
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cache.conv_states = conv_cache['conv_states']
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xBC = silu(xBC)
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# Split convolved xBC into x, B, C with correct dimensions
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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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# Reshape tensors for SSM computation
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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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x = mx.reshape(x, (batch_size, 1, n_heads, self.args.head_dim))
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x = mx.squeeze(x, axis=1)
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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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B = mx.expand_dims(B, axis=2)
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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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C = mx.expand_dims(C, axis=3)
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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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dA = mx.expand_dims(mx.expand_dims(dA, -1), -1)
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# Update state with proper shapes
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x = mx.expand_dims(x, axis=3) # (batch, heads, dim, 1)
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dBx = mx.matmul(x, B) # (batch, heads, dim, state)
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x = mx.expand_dims(x, axis=3)
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dBx = mx.matmul(x, B)
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ssm_state = cache[1]
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ssm_state = ssm_state * dA + dBx
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cache[1] = ssm_state
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cache.ssm_state = cache.ssm_state * dA + dBx
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# Compute output
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y = mx.matmul(ssm_state, C) # (batch, heads, dim, 1)
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y = mx.squeeze(y, axis=-1) # (batch, heads, dim)
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y = mx.matmul(cache.ssm_state, C)
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y = mx.squeeze(y, axis=-1)
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# Add skip connection
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y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1)
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# Reshape and process output
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y = mx.reshape(y, (batch_size, 1, n_heads * d_head))
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y = mx.reshape(y, (batch_size, 1, n_heads * self.args.head_dim))
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y = self.norm(y + z)
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y = self.out_proj(y)
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@ -440,7 +367,6 @@ class Model(nn.Module):
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else:
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logits = self.lm_head(x)
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print('ouput')
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return logits
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def make_cache(self, batch_size=1):
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