From e43a2ab229554301d7cab8efb5a87912ae536ff9 Mon Sep 17 00:00:00 2001 From: Goekdeniz-Guelmez Date: Tue, 22 Oct 2024 22:04:25 +0200 Subject: [PATCH] not working, incorrect handling with cache probably --- llms/mlx_lm/models/cache.py | 13 ++- llms/mlx_lm/models/mamba2.py | 168 +++++++++++++++++++---------------- 2 files changed, 97 insertions(+), 84 deletions(-) diff --git a/llms/mlx_lm/models/cache.py b/llms/mlx_lm/models/cache.py index 32343ae0..d1c93eba 100644 --- a/llms/mlx_lm/models/cache.py +++ b/llms/mlx_lm/models/cache.py @@ -341,21 +341,20 @@ class MambaCache(_BaseCache): class Mamba2Cache(_BaseCache): - """Cache for Mamba model inference containing conv cache and SSM state.""" - conv_cache: Optional[mx.array] = None - ssm_state: Optional[mx.array] = None + conv_states: Optional[mx.array] = None + ssm_states: Optional[mx.array] = None def __getitem__(self, idx: int) -> Optional[mx.array]: if idx == 0: - return self.conv_cache + return self.conv_states elif idx == 1: - return self.ssm_state + return self.ssm_states raise IndexError("Cache index must be 0 or 1") def __setitem__(self, idx: int, value: Optional[mx.array]): if idx == 0: - self.conv_cache = value + self.conv_states = value elif idx == 1: - self.ssm_state = value + self.ssm_states = value else: raise IndexError("Cache index must be 0 or 1") \ No newline at end of file diff --git a/llms/mlx_lm/models/mamba2.py b/llms/mlx_lm/models/mamba2.py index 2e0c6a59..739cb400 100644 --- a/llms/mlx_lm/models/mamba2.py +++ b/llms/mlx_lm/models/mamba2.py @@ -5,7 +5,7 @@ import mlx.core as mx import mlx.nn as nn from .base import BaseModelArgs -from .cache import MambaCache +from .cache import Mamba2Cache @dataclass class ModelArgs(BaseModelArgs): @@ -62,6 +62,7 @@ def silu(x): return x * mx.sigmoid(x) def ssd(x, A, B, C, chunk_size): + # Replace einsum operations with explicit reshape and matrix multiply batch, seqlen, nheads, dim = x.shape B = mx.expand_dims(B, axis=2) C = mx.expand_dims(C, axis=2) @@ -73,9 +74,18 @@ def ssd(x, A, B, C, chunk_size): chunk = slice(i, min(i + chunk_size, seqlen)) dA = mx.exp(mx.expand_dims(A[chunk], axis=0)) - dBx = mx.einsum('blhp,bln->bhpn', x[:, chunk], B[:, chunk]) + # Replace einsum with explicit operations + x_chunk = x[:, chunk] # [batch, chunk_size, nheads, dim] + x_chunk = mx.transpose(x_chunk, [0, 2, 3, 1]) # [batch, nheads, dim, chunk_size] + B_chunk = B[:, chunk] # [batch, chunk_size, state_size] + dBx = mx.matmul(x_chunk, B_chunk) # [batch, nheads, dim, state_size] + state = state * mx.expand_dims(dA, axis=-1) + dBx - y = mx.einsum('bhpn,bln->blhp', state, C[:, chunk]) + + # Replace einsum with explicit operations + C_chunk = C[:, chunk] # [batch, chunk_size, state_size] + y = mx.matmul(state, mx.transpose(C_chunk, [0, 2, 1])) # [batch, nheads, dim, chunk_size] + y = mx.transpose(y, [0, 3, 1, 2]) # [batch, chunk_size, nheads, dim] outputs.append(y) return mx.concatenate(outputs, axis=1), state @@ -93,7 +103,7 @@ class DepthWiseConv1d(nn.Module): assert in_channels == out_channels, "In and out channels must be same for depthwise convolution" assert self.groups == in_channels, "Groups must be equal to in_channels for depthwise convolution" - # Initialize with shape (channels, 1, kernel_size) to match pretrained weights + # Weight shape: (channels, 1, kernel_size) to match pretrained weights self.weight = mx.random.normal((in_channels, 1, kernel_size)) self.bias = mx.zeros((out_channels,)) if bias else None @@ -101,56 +111,78 @@ class DepthWiseConv1d(nn.Module): B, L, C = x.shape K = self.kernel_size + # Validate input dimensions + assert C == self.in_channels, f"Input channels {C} doesn't match expected {self.in_channels}" + # Handle padding and caching if cache is not None: - conv_cache = cache[cache_idx] - if conv_cache is not None: - x = mx.concatenate([conv_cache, x], axis=1) - L = x.shape[1] # Update L after concatenation + conv_states = cache[cache_idx] + if conv_states is not None: + # Validate cache shape + assert conv_states.shape[0] == B, "Cache batch size mismatch" + assert conv_states.shape[2] == C, "Cache channel count mismatch" + x = mx.concatenate([conv_states, x], axis=1) + L = x.shape[1] else: + # Add left padding of size (kernel_size - 1) pad_left = K - 1 x = mx.pad(x, [(0, 0), (pad_left, 0), (0, 0)]) - L = x.shape[1] # Update L after padding + L = x.shape[1] - # Implement depthwise convolution manually for each channel + # Pre-allocate output array if possible outputs = [] + + # Process each channel independently for c in range(C): - # Extract single channel and reshape for 1D convolution + # Extract and prepare channel data x_c = x[:, :, c] # Shape: [B, L] x_c = mx.expand_dims(x_c, axis=1) # Shape: [B, 1, L] - # Extract and ensure filter is 3D - w_c = self.weight[c] # Shape: [1, kernel_size] or [1, 1, kernel_size] + # Prepare filter weights + w_c = self.weight[c] # Get channel weights + # Ensure filter is 3D: [depth(1), in_channels(1), kernel_size] if w_c.ndim == 2: - w_c = mx.expand_dims(w_c, axis=0) # Shape: [1, 1, kernel_size] + w_c = mx.expand_dims(w_c, axis=0) elif w_c.ndim == 1: w_c = mx.expand_dims(mx.expand_dims(w_c, axis=0), axis=0) - - # For inference mode (single token), adjust the input + + # Handle inference mode (single token) if L < K: - # Pad input to match kernel size pad_size = K - L x_c = mx.pad(x_c, [(0, 0), (0, 0), (pad_size, 0)]) - # Apply 1D convolution for this channel - y_c = mx.conv_general( - x_c, - w_c, - stride=1, - padding=0 # We've already handled padding - ) - - if self.bias is not None: - y_c = y_c + self.bias[c] - - outputs.append(mx.squeeze(y_c, axis=1)) # Shape: [B, 1] - - # Stack all channel outputs + # Apply 1D convolution + try: + y_c = mx.conv_general( + x_c, + w_c, + stride=1, + padding=0 # Padding already handled + ) + + if self.bias is not None: + y_c = y_c + self.bias[c] + + # Remove singleton dimension and add to outputs + outputs.append(mx.squeeze(y_c, axis=1)) + + except Exception as e: + raise RuntimeError(f"Convolution failed for channel {c}. Shapes: input={x_c.shape}, weight={w_c.shape}") from e + + # Stack channel outputs along last dimension y = mx.stack(outputs, axis=-1) # Shape: [B, L', C] + # Update cache if needed if cache is not None: - # Update cache with the most recent K-1 tokens - cache[cache_idx] = x[:, -(K-1):, :] if L >= K else x + # Store last (kernel_size - 1) tokens or entire input if shorter + new_cache = x[:, -(K-1):, :] if L >= K else x + cache[cache_idx] = new_cache + + if new_cache.shape != cache[cache_idx].shape: + cache[cache_idx] = new_cache + print(f"Cache updated at index {cache_idx}") + else: + print(f"Skipping cache update at index {cache_idx}, shapes are identical.") return y @@ -184,9 +216,10 @@ class Mamba2Block(nn.Module): layer_scale = math.sqrt(1.0 / args.num_hidden_layers) self.out_proj.weight = self.out_proj.weight * layer_scale - def __call__(self, u: mx.array, cache = None): - if cache is not None and self.args.use_cache: - return self.step(u, cache) + def __call__(self, x: mx.array, cache=None): + # if cache is not None and self.args.use_cache: + if cache is not None: + return self.step(x, cache) # Calculate sizes d_model = self.args.intermediate_size @@ -197,7 +230,7 @@ class Mamba2Block(nn.Module): A = -mx.exp(self.A_log) # Project input - zxbcdt = self.in_proj(u) + zxbcdt = self.in_proj(x) # Correct splits for z, xBC, dt splits = [ @@ -262,13 +295,7 @@ class Mamba2Block(nn.Module): return y - def step(self, u: mx.array, cache: MambaCache): - """ - Process single or multiple tokens while maintaining state. - Args: - u: Input tensor of shape (batch_size, seq_len, hidden_size) - cache: MambaCache object containing conv cache and ssm state - """ + def step(self, u: mx.array, cache): batch_size = u.shape[0] seq_len = u.shape[1] outputs = [] @@ -295,18 +322,12 @@ class Mamba2Block(nn.Module): n_heads = self.args.num_heads d_head = self.args.head_dim - # Correct splits for z, xBC, dt - splits = [ - d_model, # z size - d_model + 2 * d_state, # xBC size (delta, B, C) - n_heads # dt size - ] + # Split projected input + # conv_dim = d_model + 2 * d_state (this should match self.conv1d.in_channels) + z = zxbcdt[:, :, :d_model] + xBC = zxbcdt[:, :, d_model:d_model + 2*d_state + d_model] # Include the full conv dimension + dt = zxbcdt[:, :, -(n_heads):] - # Split the projected input - z = zxbcdt[:, :, :splits[0]] - xBC = zxbcdt[:, :, splits[0]:splits[0] + splits[1]] - dt = zxbcdt[:, :, -splits[2]:] # Take last n_heads elements - # Process dt dt = mx.reshape(dt, (batch_size, n_heads)) dt = mx.clip( @@ -316,25 +337,23 @@ class Mamba2Block(nn.Module): ) dt = mx.maximum(dt, self.args.time_step_floor) - # Process convolution + # Process convolution with correct dimensions xBC = self.conv1d(xBC, cache=cache, cache_idx=0) xBC = silu(xBC) - # Split convolved xBC into x, B, C + # Split convolved xBC into x, B, C with correct dimensions x = xBC[:, :, :d_model] B = xBC[:, :, d_model:d_model + d_state] C = xBC[:, :, -d_state:] - # Reshape x into (batch, heads, dim) + # Reshape tensors for SSM computation x = mx.reshape(x, (batch_size, 1, n_heads, d_head)) x = mx.squeeze(x, axis=1) # (batch, heads, dim) - # Reshape B into (batch, heads, dim, state) B = mx.reshape(B, (batch_size, 1, d_state)) B = mx.broadcast_to(B, (batch_size, n_heads, d_state)) B = mx.expand_dims(B, axis=2) # (batch, heads, 1, state) - - # Reshape C for later use + C = mx.reshape(C, (batch_size, 1, d_state)) C = mx.broadcast_to(C, (batch_size, n_heads, d_state)) C = mx.expand_dims(C, axis=3) # (batch, heads, state, 1) @@ -344,33 +363,28 @@ class Mamba2Block(nn.Module): dA = mx.exp(dt * mx.expand_dims(A, 0)) dA = mx.expand_dims(mx.expand_dims(dA, -1), -1) # (batch, heads, 1, 1) - # Prepare x for Bx computation + # Update state with proper shapes x = mx.expand_dims(x, axis=3) # (batch, heads, dim, 1) - - # Compute dBx with proper broadcasting dBx = mx.matmul(x, B) # (batch, heads, dim, state) - - # Update state - ssm_state = cache[1] # (batch, heads, dim, state) + + ssm_state = cache[1] ssm_state = ssm_state * dA + dBx cache[1] = ssm_state # Compute output y = mx.matmul(ssm_state, C) # (batch, heads, dim, 1) - y = mx.squeeze(y, axis=-1) # (batch, heads, dim) - - # Add skip connection with D + y = mx.squeeze(y, axis=-1) # (batch, heads, dim) + + # Add skip connection y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1) - - # Reshape to original dimensions + + # Reshape and process output y = mx.reshape(y, (batch_size, 1, n_heads * d_head)) - - # Apply norm and output projection y = self.norm(y + z) y = self.out_proj(y) if self.args.residual_in_fp32: - y.astype(mx.float32) + y = y.astype(mx.float32) outputs.append(y) @@ -428,8 +442,8 @@ class Model(nn.Module): print('ouput') return logits - def make_cache(self): - return [MambaCache() for _ in range(len(self.layers))] + def make_cache(self, batch_size=1): + return [Mamba2Cache(batch_size, self.args.num_heads, self.args.head_dim, self.args.state_size) for _ in range(len(self.layers))] def sanitize(self, weights): sanitized = {}