mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-26 02:33:23 +08:00
imopemented multi Token inputs, but still generating Gibberish
This commit is contained in:
Goekdeniz-Guelmez
parent
2f95b361a8
commit
1a6688384d
@ -91,168 +91,6 @@ def ssd(x, A, B, C, chunk_size):
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return mx.concatenate(outputs, axis=1), state
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# class DepthWiseConv1d(nn.Module):
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# def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0):
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# super().__init__()
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# self.in_channels = in_channels
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# self.out_channels = out_channels
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# self.kernel_size = kernel_size
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# self.padding = padding
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# self.groups = groups if groups is not None else in_channels
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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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# 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) -> mx.array:
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# B, L, C = x.shape
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# K = self.kernel_size
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# assert C == self.in_channels, f"Input channels {C} doesn't match expected {self.in_channels}"
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# if cache is not None:
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# if isinstance(cache.conv_states[0], type(None)):
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# cache.conv_states[0] = mx.zeros((B, K-1, C))
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# x = mx.concatenate([cache.conv_states[0], x], axis=1)
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# outputs = []
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# for c in range(C):
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# # Input prep debug
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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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# # Weight prep debug
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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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# 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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# if self.bias is not None:
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# y_c = y_c + self.bias[c]
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# y_c = mx.squeeze(y_c, axis=1)
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# outputs.append(y_c)
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# # Output statistics
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# y = mx.stack(outputs, axis=-1)
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# # Cache update debug
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# if cache is not None:
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# cache.conv_states[0] = 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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# super().__init__()
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# self.args = args
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# d_in_proj = 2 * args.intermediate_size + 2 * args.state_size + args.num_heads
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# self.in_proj = nn.Linear(args.hidden_size, d_in_proj, bias=args.use_bias)
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# conv_dim = args.intermediate_size + 2 * args.state_size
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# self.conv1d = DepthWiseConv1d(
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# in_channels=conv_dim,
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# out_channels=conv_dim,
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# kernel_size=args.conv_kernel,
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# groups=conv_dim,
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# bias=args.use_conv_bias,
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# padding=args.conv_kernel - 1
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# )
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# self.dt_bias = mx.random.normal((args.num_heads,)) * args.initializer_range
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# self.A_log = mx.random.normal((args.num_heads,)) * args.initializer_range
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# self.D = mx.random.normal((args.num_heads,)) * args.initializer_range
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# self.norm = MambaRMSNormGated(args.intermediate_size, eps=args.layer_norm_epsilon)
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# self.out_proj = nn.Linear(args.intermediate_size, args.hidden_size, bias=args.use_bias)
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# if args.rescale_prenorm_residual:
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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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# # Expect input to be shape [batch_size, 1, dim]
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# batch_size, seq_len, dimension = u.shape
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# assert seq_len == 1, "Input should be a single token"
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# # Initialize cache if needed
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# if cache.conv_states[0] is None:
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# conv_dim = self.args.intermediate_size + 2 * self.args.state_size
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# cache.conv_states[0] = mx.zeros((batch_size, self.args.conv_kernel - 1, conv_dim))
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# if cache.ssm_states[0] is None:
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# cache.ssm_states[0] = 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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# self.args.state_size
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# ))
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# # Project input
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# zxbcdt = self.in_proj(u)
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# # Split projections
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# n_heads = self.args.num_heads
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# z = zxbcdt[:, :, :self.args.intermediate_size]
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# xBC = zxbcdt[:, :, self.args.intermediate_size:self.args.intermediate_size + 2*self.args.state_size + self.args.intermediate_size]
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# dt = zxbcdt[:, :, -(n_heads):]
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# # Time steps
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# dt = mx.reshape(dt, (batch_size, n_heads))
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# dt = mx.clip(nn.softplus(dt + self.dt_bias), self.args.time_step_min, self.args.time_step_max)
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# dt = mx.maximum(dt, self.args.time_step_floor)
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# # Convolution
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# xBC = self.conv1d(xBC, cache=cache)
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# xBC = silu(xBC)
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# # Split states
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# x = xBC[:, :, :self.args.intermediate_size]
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# B = xBC[:, :, self.args.intermediate_size:self.args.intermediate_size + self.args.state_size]
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# C = xBC[:, :, -self.args.state_size:]
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# # Reshape for SSM
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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, self.args.state_size))
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# B = mx.broadcast_to(B, (batch_size, n_heads, self.args.state_size))
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# B = mx.expand_dims(B, axis=2)
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# C = mx.reshape(C, (batch_size, 1, self.args.state_size))
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# C = mx.broadcast_to(C, (batch_size, n_heads, self.args.state_size))
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# C = mx.expand_dims(C, axis=3)
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# # 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)
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# # Update 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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# cache.ssm_states[0] = cache.ssm_states[0] * dA + dBx
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# # Compute output
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# y = mx.matmul(cache.ssm_states[0], C)
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# y = mx.squeeze(y, axis=-1)
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# y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1)
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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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# return self.out_proj(y)
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class DepthWiseConv1d(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0):
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super().__init__()
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@ -313,6 +151,97 @@ class DepthWiseConv1d(nn.Module):
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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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# super().__init__()
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# self.args = args
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# d_in_proj = 2 * args.intermediate_size + 2 * args.state_size + args.num_heads
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# self.in_proj = nn.Linear(args.hidden_size, d_in_proj, bias=args.use_bias)
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# conv_dim = args.intermediate_size + 2 * args.state_size
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# self.conv1d = DepthWiseConv1d(
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# in_channels=conv_dim,
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# out_channels=conv_dim,
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# kernel_size=args.conv_kernel,
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# groups=conv_dim,
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# bias=args.use_conv_bias,
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# padding=args.conv_kernel - 1
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# )
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# self.dt_bias = mx.random.normal((args.num_heads,)) * args.initializer_range
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# self.A_log = mx.random.normal((args.num_heads,)) * args.initializer_range
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# self.D = mx.random.normal((args.num_heads,)) * args.initializer_range
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# self.norm = MambaRMSNormGated(args.intermediate_size, eps=args.layer_norm_epsilon)
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# self.out_proj = nn.Linear(args.intermediate_size, args.hidden_size, bias=args.use_bias)
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# if args.rescale_prenorm_residual:
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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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# batch_size, seq_len, dimension = u.shape
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# assert seq_len == 1, "Input should be a single token"
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# # Initialize cache states directly using indices
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# if cache[0] is None: # conv state
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# conv_dim = self.args.intermediate_size + 2 * self.args.state_size
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# cache[0] = mx.zeros((batch_size, self.args.conv_kernel - 1, conv_dim))
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# if cache[1] is None: # ssm state
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# cache[1] = 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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# self.args.state_size
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# ))
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# zxbcdt = self.in_proj(u)
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# n_heads = self.args.num_heads
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# z = zxbcdt[:, :, :self.args.intermediate_size]
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# xBC = zxbcdt[:, :, self.args.intermediate_size:self.args.intermediate_size + 2*self.args.state_size + self.args.intermediate_size]
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# dt = zxbcdt[:, :, -(n_heads):]
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# dt = mx.reshape(dt, (batch_size, n_heads))
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# dt = mx.clip(nn.softplus(dt + self.dt_bias), self.args.time_step_min, self.args.time_step_max)
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# dt = mx.maximum(dt, self.args.time_step_floor)
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# xBC = self.conv1d(xBC, cache=cache)
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# xBC = silu(xBC)
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# x = xBC[:, :, :self.args.intermediate_size]
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# B = xBC[:, :, self.args.intermediate_size:self.args.intermediate_size + self.args.state_size]
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# C = xBC[:, :, -self.args.state_size:]
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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, self.args.state_size))
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# B = mx.broadcast_to(B, (batch_size, n_heads, self.args.state_size))
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# B = mx.expand_dims(B, axis=2)
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# C = mx.reshape(C, (batch_size, 1, self.args.state_size))
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# C = mx.broadcast_to(C, (batch_size, n_heads, self.args.state_size))
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# C = mx.expand_dims(C, axis=3)
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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)
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# x = mx.expand_dims(x, axis=3)
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# dBx = mx.matmul(x, B)
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# # Update ssm state directly using cache[1]
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# cache[1] = cache[1] * dA + dBx
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# y = mx.matmul(cache[1], C)
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# y = mx.squeeze(y, axis=-1)
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# y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1)
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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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# return self.out_proj(y)
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class Mamba2Block(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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@ -344,64 +273,81 @@ class Mamba2Block(nn.Module):
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def __call__(self, u: mx.array, cache=None):
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batch_size, seq_len, dimension = u.shape
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assert seq_len == 1, "Input should be a single token"
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# Initialize cache states directly using indices
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if cache[0] is None: # conv state
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conv_dim = self.args.intermediate_size + 2 * self.args.state_size
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cache[0] = mx.zeros((batch_size, self.args.conv_kernel - 1, conv_dim))
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if cache[1] is None: # ssm state
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cache[1] = 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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self.args.state_size
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))
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zxbcdt = self.in_proj(u)
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n_heads = self.args.num_heads
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z = zxbcdt[:, :, :self.args.intermediate_size]
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xBC = zxbcdt[:, :, self.args.intermediate_size:self.args.intermediate_size + 2*self.args.state_size + self.args.intermediate_size]
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dt = zxbcdt[:, :, -(n_heads):]
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# Process sequence in chunks if needed
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outputs = []
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current_cache = cache
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for i in range(seq_len):
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# Extract current token
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current_input = u[:, i:i+1, :]
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# Initialize cache states if needed
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if current_cache[0] is None: # conv state
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conv_dim = self.args.intermediate_size + 2 * self.args.state_size
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current_cache[0] = mx.zeros((batch_size, self.args.conv_kernel - 1, conv_dim))
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dt = mx.reshape(dt, (batch_size, n_heads))
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dt = mx.clip(nn.softplus(dt + self.dt_bias), self.args.time_step_min, self.args.time_step_max)
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dt = mx.maximum(dt, self.args.time_step_floor)
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if current_cache[1] is None: # ssm state
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current_cache[1] = 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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self.args.state_size
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))
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xBC = self.conv1d(xBC, cache=cache)
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xBC = silu(xBC)
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# Project input
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zxbcdt = self.in_proj(current_input)
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n_heads = self.args.num_heads
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z = zxbcdt[:, :, :self.args.intermediate_size]
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xBC = zxbcdt[:, :, self.args.intermediate_size:self.args.intermediate_size + 2*self.args.state_size + self.args.intermediate_size]
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dt = zxbcdt[:, :, -(n_heads):]
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x = xBC[:, :, :self.args.intermediate_size]
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B = xBC[:, :, self.args.intermediate_size:self.args.intermediate_size + self.args.state_size]
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C = xBC[:, :, -self.args.state_size:]
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# Process time steps
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dt = mx.reshape(dt, (batch_size, n_heads))
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dt = mx.clip(nn.softplus(dt + self.dt_bias), self.args.time_step_min, self.args.time_step_max)
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dt = mx.maximum(dt, self.args.time_step_floor)
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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, self.args.state_size))
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B = mx.broadcast_to(B, (batch_size, n_heads, self.args.state_size))
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B = mx.expand_dims(B, axis=2)
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C = mx.reshape(C, (batch_size, 1, self.args.state_size))
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C = mx.broadcast_to(C, (batch_size, n_heads, self.args.state_size))
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C = mx.expand_dims(C, axis=3)
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# Apply convolution
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xBC = self.conv1d(xBC, cache=current_cache)
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xBC = silu(xBC)
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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)
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# Split states
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x = xBC[:, :, :self.args.intermediate_size]
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B = xBC[:, :, self.args.intermediate_size:self.args.intermediate_size + self.args.state_size]
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C = xBC[:, :, -self.args.state_size:]
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x = mx.expand_dims(x, axis=3)
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dBx = mx.matmul(x, B)
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# Update ssm state directly using cache[1]
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cache[1] = cache[1] * dA + dBx
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# Reshape for SSM
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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, self.args.state_size))
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B = mx.broadcast_to(B, (batch_size, n_heads, self.args.state_size))
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B = mx.expand_dims(B, axis=2)
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C = mx.reshape(C, (batch_size, 1, self.args.state_size))
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C = mx.broadcast_to(C, (batch_size, n_heads, self.args.state_size))
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C = mx.expand_dims(C, axis=3)
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y = mx.matmul(cache[1], C)
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y = mx.squeeze(y, axis=-1)
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y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1)
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y = mx.reshape(y, (batch_size, 1, n_heads * self.args.head_dim))
|
||||
y = self.norm(y + z)
|
||||
# SSM updates
|
||||
A = -mx.exp(self.A_log)
|
||||
dA = mx.exp(dt * mx.expand_dims(A, 0))
|
||||
dA = mx.expand_dims(mx.expand_dims(dA, -1), -1)
|
||||
|
||||
return self.out_proj(y)
|
||||
# Update state
|
||||
x = mx.expand_dims(x, axis=3)
|
||||
dBx = mx.matmul(x, B)
|
||||
current_cache[1] = current_cache[1] * dA + dBx
|
||||
|
||||
# Compute output
|
||||
y = mx.matmul(current_cache[1], C)
|
||||
y = mx.squeeze(y, axis=-1)
|
||||
y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1)
|
||||
y = mx.reshape(y, (batch_size, 1, n_heads * self.args.head_dim))
|
||||
y = self.norm(y + z)
|
||||
|
||||
outputs.append(self.out_proj(y))
|
||||
|
||||
# Concatenate all outputs
|
||||
return mx.concatenate(outputs, axis=1)
|
||||
|
||||
|
||||
class ResidualBlock(nn.Module):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user