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https://github.com/ml-explore/mlx-examples.git
synced 2025-06-29 12:51:12 +08:00
not working, incorrect handling with cache probably
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Goekdeniz-Guelmez
parent
55485b98e8
commit
e43a2ab229
@ -341,21 +341,20 @@ class MambaCache(_BaseCache):
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class Mamba2Cache(_BaseCache):
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"""Cache for Mamba model inference containing conv cache and SSM state."""
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conv_cache: Optional[mx.array] = None
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ssm_state: Optional[mx.array] = None
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conv_states: Optional[mx.array] = None
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ssm_states: 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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return self.conv_cache
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return self.conv_states
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elif idx == 1:
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return self.ssm_state
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return self.ssm_states
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raise IndexError("Cache index must be 0 or 1")
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def __setitem__(self, idx: int, value: Optional[mx.array]):
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if idx == 0:
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self.conv_cache = value
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self.conv_states = value
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elif idx == 1:
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self.ssm_state = value
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self.ssm_states = value
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else:
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raise IndexError("Cache index must be 0 or 1")
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@ -5,7 +5,7 @@ import mlx.core as mx
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import mlx.nn as nn
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from .base import BaseModelArgs
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from .cache import MambaCache
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from .cache import Mamba2Cache
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@dataclass
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class ModelArgs(BaseModelArgs):
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@ -62,6 +62,7 @@ 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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# Replace einsum operations with explicit reshape and matrix multiply
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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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@ -73,9 +74,18 @@ def ssd(x, A, B, C, 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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# Replace einsum with explicit operations
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x_chunk = x[:, chunk] # [batch, chunk_size, nheads, dim]
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x_chunk = mx.transpose(x_chunk, [0, 2, 3, 1]) # [batch, nheads, dim, chunk_size]
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B_chunk = B[:, chunk] # [batch, chunk_size, state_size]
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dBx = mx.matmul(x_chunk, B_chunk) # [batch, nheads, dim, state_size]
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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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# Replace einsum with explicit operations
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C_chunk = C[:, chunk] # [batch, chunk_size, state_size]
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y = mx.matmul(state, mx.transpose(C_chunk, [0, 2, 1])) # [batch, nheads, dim, chunk_size]
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y = mx.transpose(y, [0, 3, 1, 2]) # [batch, chunk_size, nheads, dim]
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outputs.append(y)
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return mx.concatenate(outputs, axis=1), state
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@ -93,7 +103,7 @@ 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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# Initialize with shape (channels, 1, kernel_size) to match pretrained weights
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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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@ -101,56 +111,78 @@ class DepthWiseConv1d(nn.Module):
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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_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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conv_states = cache[cache_idx]
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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] # Update L after padding
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L = x.shape[1]
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# Implement depthwise convolution manually for each channel
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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 single channel and reshape for 1D convolution
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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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# 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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# 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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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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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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# For inference mode (single token), adjust the input
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# Handle inference mode (single token)
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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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# 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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# Stack channel outputs along last dimension
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y = mx.stack(outputs, axis=-1) # Shape: [B, L', C]
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# Update cache if needed
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if cache is not None:
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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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# 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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return y
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@ -184,9 +216,10 @@ class Mamba2Block(nn.Module):
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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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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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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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d_model = self.args.intermediate_size
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@ -197,7 +230,7 @@ class Mamba2Block(nn.Module):
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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)
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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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@ -262,13 +295,7 @@ class Mamba2Block(nn.Module):
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return y
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def step(self, u: mx.array, cache: MambaCache):
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"""
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Process single or multiple tokens while maintaining state.
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Args:
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u: Input tensor of shape (batch_size, seq_len, hidden_size)
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cache: MambaCache object containing conv cache and ssm state
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"""
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def step(self, u: mx.array, cache):
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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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@ -295,18 +322,12 @@ class Mamba2Block(nn.Module):
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n_heads = self.args.num_heads
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d_head = self.args.head_dim
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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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# 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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dt = zxbcdt[:, :, -(n_heads):]
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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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# 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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@ -316,25 +337,23 @@ 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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# Process convolution with correct dimensions
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xBC = self.conv1d(xBC, cache=cache, cache_idx=0)
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xBC = silu(xBC)
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# Split convolved xBC into x, B, C
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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 x into (batch, heads, dim)
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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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# 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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@ -344,33 +363,28 @@ class Mamba2Block(nn.Module):
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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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# Update state with proper shapes
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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 = cache[1]
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ssm_state = ssm_state * dA + dBx
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cache[1] = ssm_state
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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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# Add skip connection with D
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y = mx.squeeze(y, axis=-1) # (batch, heads, dim)
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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 to original dimensions
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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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# Apply norm and output projection
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y = self.norm(y + z)
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y = self.out_proj(y)
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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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outputs.append(y)
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@ -428,8 +442,8 @@ class Model(nn.Module):
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print('ouput')
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return logits
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def make_cache(self):
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return [MambaCache() for _ in range(len(self.layers))]
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def make_cache(self, batch_size=1):
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return [Mamba2Cache(batch_size, self.args.num_heads, self.args.head_dim, self.args.state_size) for _ in range(len(self.layers))]
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def sanitize(self, weights):
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sanitized = {}
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