mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-12-16 02:08:55 +08:00
Fixed streaming generation and got rid of generating gibberish, but is still a litle slow: 0.222 tokens-per-sec
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Goekdeniz-Guelmez
@@ -148,202 +148,131 @@ 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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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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# Calculate dimensions
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self.d_model = args.hidden_size
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self.d_state = args.state_size
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self.d_conv = args.conv_kernel
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self.expand = args.expand
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self.d_inner = int(self.expand * self.d_model)
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self.n_heads = args.num_heads
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self.d_head = self.d_inner // self.n_heads
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# Input projection
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d_in_proj = self.d_inner * 2 + self.d_state * 2 + self.n_heads
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self.in_proj = nn.Linear(self.d_model, d_in_proj, bias=args.use_bias)
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# Convolution
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conv_dim = self.d_inner + 2 * self.d_state
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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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kernel_size=self.d_conv,
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bias=args.use_conv_bias,
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padding=args.conv_kernel - 1
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groups=conv_dim
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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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# SSM parameters
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self.dt_bias = mx.random.normal((self.n_heads,)) * args.initializer_range
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self.A_log = mx.random.normal((self.n_heads,)) * args.initializer_range
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self.D = mx.random.normal((self.n_heads,)) * args.initializer_range
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# Output projection
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self.norm = MambaRMSNormGated(self.d_inner, eps=args.layer_norm_epsilon)
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self.out_proj = nn.Linear(self.d_inner, self.d_model, 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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batch_size, seq_len, _ = u.shape
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# Process sequence in chunks if needed
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# Project input
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proj = self.in_proj(u) # [batch, seq_len, d_in_proj]
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# Calculate split indices and slice tensors
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z = proj[..., :self.d_inner]
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x_conv = proj[..., self.d_inner:self.d_inner + (self.d_inner + 2 * self.d_state)]
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dt = proj[..., -self.n_heads:]
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# Process time steps
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dt = nn.softplus(dt + self.dt_bias)
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dt = mx.clip(dt, 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 and activation
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x_conv = self.conv1d(x_conv, cache=[cache[0] if cache else None])
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x_conv = silu(x_conv)
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# Split conv output
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x = x_conv[..., :self.d_inner]
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B = x_conv[..., self.d_inner:self.d_inner + self.d_state]
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C = x_conv[..., -self.d_state:]
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# Reshape x for SSM
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x = mx.reshape(x, (batch_size, seq_len, self.n_heads, self.d_head))
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# Process B and C without reshaping heads
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B = mx.expand_dims(B, axis=2) # [batch, seq_len, 1, d_state]
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B = mx.broadcast_to(B, (batch_size, seq_len, self.n_heads, self.d_state))
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C = mx.expand_dims(C, axis=2) # [batch, seq_len, 1, d_state]
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C = mx.broadcast_to(C, (batch_size, seq_len, self.n_heads, self.d_state))
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# Initialize or get previous state
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if cache and cache[1] is not None:
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prev_state = cache[1]
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else:
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prev_state = mx.zeros((batch_size, self.n_heads, self.d_head, self.d_state))
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# Compute dA
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dA = -mx.exp(self.A_log) # [n_heads]
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dt = mx.reshape(dt, (batch_size, seq_len, self.n_heads)) # Ensure correct shape
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dA = mx.exp(mx.expand_dims(dt * mx.expand_dims(dA, 0), -1)) # [batch, seq_len, n_heads, 1]
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dA = mx.expand_dims(dA, -1) # [batch, seq_len, n_heads, 1, 1]
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# Process sequence
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next_state = prev_state
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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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for t in range(seq_len):
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# Get current step tensors
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xt = x[:, t] # [batch, n_heads, d_head]
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Bt = B[:, t] # [batch, n_heads, d_state]
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Ct = C[:, t] # [batch, n_heads, d_state]
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dAt = dA[:, t] # [batch, n_heads, 1, 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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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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# 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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# 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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# Apply convolution
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xBC = self.conv1d(xBC, cache=current_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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current_cache[1] = current_cache[1] * dA + dBx
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# Compute output
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y = mx.matmul(current_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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next_state = (
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next_state * dAt + # Broadcasting: [batch, n_heads, d_head, d_state] * [batch, n_heads, 1, 1]
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mx.matmul(
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mx.expand_dims(xt, -1), # [batch, n_heads, d_head, 1]
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mx.expand_dims(Bt, -2) # [batch, n_heads, 1, d_state]
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)
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)
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outputs.append(self.out_proj(y))
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# Concatenate all outputs
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# Compute output
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yt = mx.matmul(
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next_state, # [batch, n_heads, d_head, d_state]
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mx.expand_dims(Ct, -1) # [batch, n_heads, d_state, 1]
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)
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yt = mx.squeeze(yt, -1) # [batch, n_heads, d_head]
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yt = yt + xt * mx.expand_dims(self.D, -1)
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# Reshape and normalize
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yt = mx.reshape(yt, (batch_size, 1, self.d_inner))
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yt = self.norm(yt, z[:, t:t+1])
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outputs.append(self.out_proj(yt))
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# Update cache
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if cache is not None:
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cache[1] = next_state
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return mx.concatenate(outputs, axis=1)
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