forked from tinygrad/tinygrad
refactor delta attention
This commit is contained in:
+46
-13
@@ -1,6 +1,6 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
from tinygrad import Tensor, dtypes
|
||||
from tinygrad import Tensor, dtypes, nn
|
||||
from tinygrad.llm.model import (
|
||||
GatedDeltaNetBlock, SSMConfig, TransformerBlock, TransformerConfig,
|
||||
apply_rope as apply_rope_new, precompute_freqs_cis, pairwise_topk,
|
||||
@@ -45,10 +45,10 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
|
||||
return Tensor.linspace(start, stop, int(np.prod(shape)), dtype=dtypes.float32).reshape(*shape)
|
||||
|
||||
def _make_config(self, **kwargs):
|
||||
return TransformerConfig(**({"num_blocks":1, "dim":4, "hidden_dim":8, "n_heads":1, "n_kv_heads":1,
|
||||
"norm_eps":1e-5, "vocab_size":32, "head_dim":4, "rope_theta":10000.0,
|
||||
"rope_dim":4, "v_head_dim":4, "max_context":4, "ssm_layers":(True,),
|
||||
"ssm":SSMConfig(conv_kernel=2, state_size=2, group_count=1, time_step_rank=1, inner_size=2)} | kwargs))
|
||||
return TransformerConfig(**({"num_blocks":1, "dim":32, "hidden_dim":64, "n_heads":1, "n_kv_heads":1,
|
||||
"norm_eps":1e-5, "vocab_size":32, "head_dim":32, "rope_theta":10000.0,
|
||||
"rope_dim":32, "v_head_dim":32, "max_context":4, "ssm_layers":(True,),
|
||||
"ssm":SSMConfig(conv_kernel=2, state_size=32, group_count=1, time_step_rank=1, inner_size=32)} | kwargs))
|
||||
|
||||
def _make_block(self, config:TransformerConfig) -> GatedDeltaNetBlock:
|
||||
block = GatedDeltaNetBlock(config, config.ssm)
|
||||
@@ -79,6 +79,10 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
|
||||
recurrent_state = cache[:, conv_flat:].reshape(cache.shape[0], block.num_v_heads, block.head_v_dim, block.head_v_dim)
|
||||
return conv_state, recurrent_state
|
||||
|
||||
def _reset_state(self, block:GatedDeltaNetBlock):
|
||||
Tensor.realize(block.conv_state.assign(block.conv_state.const_like(0)),
|
||||
block.recurrent_state.assign(block.recurrent_state.const_like(0)))
|
||||
|
||||
def _linear_np(self, x:np.ndarray, weight:np.ndarray) -> np.ndarray:
|
||||
return x.astype(np.float32) @ weight.T.astype(np.float32)
|
||||
|
||||
@@ -86,7 +90,7 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
|
||||
x_float = x.astype(np.float32)
|
||||
return (x_float / np.sqrt((x_float * x_float).mean(axis=-1, keepdims=True) + eps)) * weight.astype(np.float32)
|
||||
|
||||
def _normalize_np(self, x:np.ndarray, eps:float=1e-12) -> np.ndarray:
|
||||
def _normalize_np(self, x:np.ndarray, eps:float=1e-6) -> np.ndarray:
|
||||
return x / np.maximum(np.sqrt((x * x).sum(axis=-1, keepdims=True)), eps)
|
||||
|
||||
def _softplus_np(self, x:np.ndarray) -> np.ndarray:
|
||||
@@ -148,6 +152,12 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
|
||||
x = Tensor.linspace(-1.0, 1.0, 3 * config.dim, dtype=dtypes.float32).reshape(1, 3, config.dim)
|
||||
|
||||
expected_outs, expected_conv, expected_recurrent = self._naive_attention(block, x)
|
||||
out = self._run_attention(block, x, 0)
|
||||
conv_state, recurrent_state = self._cache_views(block)
|
||||
np.testing.assert_allclose(out, np.concatenate(expected_outs, axis=1), rtol=1e-3, atol=1e-3)
|
||||
np.testing.assert_allclose(conv_state, expected_conv[-1], rtol=1e-3, atol=1e-3)
|
||||
np.testing.assert_allclose(recurrent_state, expected_recurrent[-1], rtol=1e-3, atol=1e-3)
|
||||
self._reset_state(block)
|
||||
|
||||
for step in range(x.shape[1]):
|
||||
out = self._run_attention(block, x[:, step:step+1], step)
|
||||
@@ -163,7 +173,7 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
|
||||
prompt = Tensor.linspace(0.75, -0.75, 2 * config.dim, dtype=dtypes.float32).reshape(1, 2, config.dim)
|
||||
|
||||
for i in range(warmup.shape[1]): self._run_attention(block, warmup[:, i:i+1], i)
|
||||
Tensor.realize(*block._state_reset_ops())
|
||||
self._reset_state(block)
|
||||
expected_outs, expected_conv, expected_recurrent = self._naive_attention(block, prompt)
|
||||
|
||||
for step in range(prompt.shape[1]):
|
||||
@@ -177,18 +187,41 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
|
||||
err_msg=f"GatedDeltaNet reset recurrent cache mismatch at step {step}")
|
||||
|
||||
def test_kda_channel_decay(self):
|
||||
config = self._make_config(n_heads=2, ssm=SSMConfig(conv_kernel=2, state_size=2, group_count=2, time_step_rank=2, inner_size=4, kda=True))
|
||||
block, x = GatedDeltaNetBlock(config, config.ssm), Tensor([[[1., 2., 0., 0.]]])
|
||||
# f_b(f_a(x)) = [1, 2, 3, 4]
|
||||
config = self._make_config(dim=4, hidden_dim=8, n_heads=2, head_dim=4, rope_dim=4, v_head_dim=4,
|
||||
ssm=SSMConfig(conv_kernel=2, state_size=2, group_count=2, time_step_rank=2, inner_size=4, kda=True))
|
||||
block, x = GatedDeltaNetBlock(config, config.ssm), Tensor([[[1., 2., 0., 0.], [2., 1., 0., 0.]]])
|
||||
block.ssm_f_a.weight = Tensor([[1., 0., 0., 0.], [0., 1., 0., 0.]])
|
||||
block.ssm_f_b.weight = Tensor([[1., 0.], [0., 1.], [1., 1.], [2., 1.]])
|
||||
block._init_state(x)
|
||||
initial_state = Tensor.arange(8, dtype=dtypes.float32).reshape(1, 2, 2, 2)
|
||||
block.recurrent_state.assign(initial_state).realize()
|
||||
block.ssm_a = Tensor([[-1.], [-1.]])
|
||||
block._attention(x, 0).realize()
|
||||
alpha = np.exp(-self._softplus_np(np.arange(1, 5)).reshape(1, 2, 1, 2))
|
||||
np.testing.assert_allclose(block.recurrent_state.numpy(), initial_state.numpy() * alpha, rtol=1e-5, atol=1e-5)
|
||||
block._attention(x, x.shape[1]).realize()
|
||||
alpha = np.exp(-self._softplus_np(np.array([[1, 2, 3, 4], [2, 1, 3, 5]])).reshape(2, 2, 2)).prod(0)
|
||||
np.testing.assert_allclose(block.recurrent_state.numpy(), initial_state.numpy() * alpha[..., None], rtol=1e-5, atol=1e-5)
|
||||
|
||||
def test_kda_prefill_matches_decode(self):
|
||||
config = self._make_config(ssm=SSMConfig(conv_kernel=2, state_size=32, group_count=1, time_step_rank=1, inner_size=32, kda=True))
|
||||
block = GatedDeltaNetBlock(config, config.ssm)
|
||||
for p in nn.state.get_parameters(block):
|
||||
p.replace(self._tensor_linspace(-0.05, 0.05, p.shape) if len(p.shape) > 1 else self._tensor_linspace(0.05, 0.1, p.shape))
|
||||
x = self._tensor_linspace(-0.5, 0.5, (1, 3, config.dim))
|
||||
prefill = self._run_attention(block, x, 0)
|
||||
prefill_conv, prefill_recurrent = self._cache_views(block)
|
||||
self._reset_state(block)
|
||||
decode = np.concatenate([self._run_attention(block, x[:, i:i+1], i) for i in range(3)], axis=1)
|
||||
decode_conv, decode_recurrent = self._cache_views(block)
|
||||
np.testing.assert_allclose(prefill, decode, rtol=1e-3, atol=1e-3)
|
||||
np.testing.assert_allclose(prefill_conv, decode_conv, rtol=1e-3, atol=1e-3)
|
||||
np.testing.assert_allclose(prefill_recurrent, decode_recurrent, rtol=1e-3, atol=1e-3)
|
||||
|
||||
def test_start_zero_resets_realized_state(self):
|
||||
config, x = self._make_config(max_context=3), self._tensor_linspace(-1, 1, (1, 3, 32))
|
||||
block = self._make_block(config)
|
||||
self._run_attention(block, x, 0)
|
||||
restarted = self._run_attention(block, x[:, :2], 0)
|
||||
fresh = self._run_attention(self._make_block(config), x[:, :2], 0)
|
||||
np.testing.assert_allclose(restarted, fresh, rtol=1e-3, atol=1e-3)
|
||||
|
||||
class TestPairwiseTopk(unittest.TestCase):
|
||||
def test_basic_topk(self):
|
||||
|
||||
@@ -42,7 +42,8 @@ class TestTransformerGenerate(unittest.TestCase):
|
||||
return Tensor([[42]])
|
||||
with patch.object(Transformer, '__call__', mock_call):
|
||||
next(model.generate([1, 2, 3, 4, 5, 42, 10]))
|
||||
self.assertEqual(calls, [((1, 1), V_START_POS.bind(5)), ((1, 1), V_START_POS.bind(6))])
|
||||
# recurrent blocks prefill chunks like attention blocks: the 2 new tokens go through one chunked call
|
||||
self.assertEqual(calls, [((1, V_TOKS.bind(2)), V_START_POS.bind(5))])
|
||||
|
||||
def test_recurrent_divergent_prompt_restarts(self):
|
||||
model, calls = Transformer(TEST_CONFIG), []
|
||||
|
||||
+51
-32
@@ -124,8 +124,6 @@ class FFNBlock:
|
||||
|
||||
# given the token-prefix match, return how much cached state this block can still reuse
|
||||
def _reusable_prefix_len(self, prefix_len:int, cached_len:int) -> int: return prefix_len
|
||||
# return writes that reset this block's state after a cache mismatch
|
||||
def _state_reset_ops(self) -> list[Tensor]: return []
|
||||
def _init_state(self, x:Tensor): raise NotImplementedError
|
||||
def _attention(self, x:Tensor, start_pos:int|UOp) -> Tensor: raise NotImplementedError
|
||||
|
||||
@@ -260,45 +258,68 @@ class GatedDeltaNetBlock(FFNBlock):
|
||||
|
||||
def _attention(self, x:Tensor, start_pos:int|UOp) -> Tensor:
|
||||
B, T, _ = x.shape
|
||||
assert T == 1, "GatedDeltaNetBlock currently only supports T=1"
|
||||
# bind ints to a variable so the reset flag stays a runtime value (it toggles when generation restarts at position 0)
|
||||
start_pos = start_pos if isinstance(start_pos, UOp) else UOp.variable("start_pos", 0, self.config.max_context-1).bind(start_pos)
|
||||
initial = Tensor(start_pos).eq(0)
|
||||
is_kda = hasattr(self, "ssm_g_a")
|
||||
symbolic = isinstance(T, UOp)
|
||||
T_pad = x.max_shape[1] # symbolic chunks are padded to their max size: one graph serves every size
|
||||
|
||||
# input processing
|
||||
x = x.half()
|
||||
out_gate = self.ssm_g_b(self.ssm_g_a(x)) if is_kda else self.attn_gate(x)
|
||||
out_gate = out_gate.reshape(B, 1, self.num_v_heads, self.head_v_dim)
|
||||
beta = self.ssm_beta(x).sigmoid().reshape(B, self.num_v_heads, 1, 1)
|
||||
out_gate = out_gate.reshape(B, T, self.num_v_heads, self.head_v_dim)
|
||||
beta = self.ssm_beta(x).sigmoid().reshape(B, T, self.num_v_heads)
|
||||
alpha = self.ssm_f_b(self.ssm_f_a(x)) if is_kda else self.ssm_alpha(x)
|
||||
alpha = ((alpha.float() + self.ssm_dt["bias"]).softplus().reshape(B, self.num_v_heads, -1) *
|
||||
self.ssm_a.reshape(1, self.num_v_heads, -1)).exp().unsqueeze(-2)
|
||||
log_alpha = ((alpha.float() + self.ssm_dt["bias"]).softplus().reshape(B, T, self.num_v_heads, -1) * self.ssm_a).squeeze(-1) \
|
||||
if is_kda else ((alpha.float() + self.ssm_dt["bias"]).softplus() * self.ssm_a).reshape(B, T, self.num_v_heads)
|
||||
|
||||
# qkv conv
|
||||
conv_window = self.conv_state.cat(self.attn_qkv(x), dim=1)
|
||||
conv_out = (conv_window * self.ssm_conv1d["weight"].T.unsqueeze(0)).sum(1).silu()
|
||||
# qkv conv, conv_state is reset when starting from position 0
|
||||
conv_state = initial.where(0, self.conv_state)
|
||||
# assemble the conv window in a static-size buffer: [conv_state | qkv rows | zero-pad].
|
||||
# padded steps are exact no-ops: beta=0 (delta rule off), log_alpha=0 (decay 1 after exp)
|
||||
conv_window = Tensor.zeros(B, self.ssm_conv_kernel-1 + T_pad, self.conv_channels)
|
||||
win = conv_window.uop
|
||||
win = win.after(win[:, :self.ssm_conv_kernel-1].store(conv_state.cast(win.dtype).uop))
|
||||
win = win.after(win[:, self.ssm_conv_kernel-1:self.ssm_conv_kernel-1+T].store(self.attn_qkv(x).cast(win.dtype).uop))
|
||||
conv_window = Tensor(win)
|
||||
# the last conv_kernel-1 columns of the window become the next conv state
|
||||
conv_state_store = self.conv_state.uop.store(conv_window[:, T:T+self.ssm_conv_kernel-1].cast(self.conv_state.dtype).uop)
|
||||
|
||||
conv_out = functools.reduce(lambda a,b: a+b,
|
||||
(conv_window[:, i:i+T_pad] * self.ssm_conv1d["weight"][:, i] for i in range(self.ssm_conv_kernel))).silu()
|
||||
if symbolic:
|
||||
out_gate = out_gate.pad_to((B, T_pad, self.num_v_heads, self.head_v_dim))
|
||||
beta, log_alpha = beta.pad_to((B, T_pad, self.num_v_heads)), log_alpha.pad_to((B, T_pad, self.num_v_heads))
|
||||
q, k, v = conv_out.split([self.q_dim, self.q_dim, self.conv_channels - 2*self.q_dim], dim=-1)
|
||||
q = q.reshape(B, self.num_k_heads, self.head_k_dim).normalize(dim=-1).repeat(1, self.num_v_heads//self.num_k_heads, 1)
|
||||
k = k.reshape(B, self.num_k_heads, self.head_k_dim).normalize(dim=-1).repeat(1, self.num_v_heads//self.num_k_heads, 1)
|
||||
v = v.reshape(B, self.num_v_heads, self.head_v_dim)
|
||||
q, k, v = q.mul(self.head_k_dim**-0.5).unsqueeze(-1), k.unsqueeze(-1), v.unsqueeze(-1)
|
||||
qk_eps = 1e-12 if is_kda else 1e-6
|
||||
q, k = (z.reshape(B, T_pad, self.num_k_heads, self.head_k_dim).normalize(dim=-1, eps=qk_eps)
|
||||
.repeat(1, 1, self.num_v_heads//self.num_k_heads, 1) for z in (q, k))
|
||||
v = v.reshape(B, T_pad, self.num_v_heads, self.head_v_dim)
|
||||
q, k, v, beta = [z.transpose(1, 2).float() for z in (q, k, v, beta)]
|
||||
alpha = log_alpha.transpose(1, 2).exp()
|
||||
|
||||
# recurrent
|
||||
recurrent_state = self.recurrent_state * alpha
|
||||
recurrent_state = recurrent_state + ((v - recurrent_state@k) * beta)@k.transpose(-1, -2)
|
||||
# recurrent: scan over the (padded) tokens, updating the recurrent state. the output rows go to a static-size buffer
|
||||
state = Tensor(self.recurrent_state.uop.after(conv_state_store)).float() # carry the conv write into this graph
|
||||
state = initial.where(0, state)
|
||||
core = Tensor.zeros(B, self.num_v_heads, T_pad, self.head_v_dim)
|
||||
out = core.uop
|
||||
for t in range(T_pad):
|
||||
key = k[:, :, t] # (B, H, K)
|
||||
decay = alpha[:, :, t].unsqueeze(-1) if len(alpha.shape) == 4 else alpha[:, :, t].reshape(B, self.num_v_heads, 1, 1)
|
||||
s1 = state * decay
|
||||
delta = (v[:, :, t].unsqueeze(-1) - (s1 * key.unsqueeze(-2)).sum(-1, keepdim=True)) * beta[:, :, t].reshape(B, self.num_v_heads, 1, 1)
|
||||
state = s1 + delta * key.unsqueeze(-2)
|
||||
out = out.after(out[:, :, t].store(((state * q[:, :, t].unsqueeze(-2)).sum(-1) * self.head_k_dim**-0.5).uop))
|
||||
|
||||
# store the updated state
|
||||
conv_state_store = self.conv_state.uop.store(conv_window[:, 1:, :].cast(self.conv_state.dtype).uop)
|
||||
recurrent_state_store = self.recurrent_state.uop.store(recurrent_state.cast(self.recurrent_state.dtype).uop)
|
||||
recurrent_state = Tensor(self.recurrent_state.uop.after(recurrent_state_store, conv_state_store))
|
||||
# store the updated recurrent state in place, then read the output buffer after the write
|
||||
recurrent_state_store = self.recurrent_state.uop.store(state.cast(self.recurrent_state.dtype).uop)
|
||||
core = Tensor(out.after(recurrent_state_store)).transpose(1, 2)
|
||||
|
||||
# output
|
||||
core_attn_out = self.ssm_norm((recurrent_state@q).squeeze(-1).reshape(B, 1, self.num_v_heads, self.head_v_dim))
|
||||
out_gate = out_gate.sigmoid() if is_kda else out_gate.silu()
|
||||
return self.ssm_out((core_attn_out * out_gate).reshape(B, 1, -1).cast(x.dtype))
|
||||
|
||||
# recurrent state can't be partially reused after divergence, force a full rebuild
|
||||
def _state_reset_ops(self):
|
||||
return [self.conv_state.assign(self.conv_state.const_like(0)),
|
||||
self.recurrent_state.assign(self.recurrent_state.const_like(0))] if hasattr(self, "conv_state") else []
|
||||
# output; undo the padding before the output projection
|
||||
z = (self.ssm_norm(core) * (out_gate.sigmoid() if is_kda else out_gate.silu())).cast(x.dtype).contiguous()
|
||||
if symbolic: z = z[:, :T]
|
||||
return self.ssm_out(z.reshape(B, T, -1))
|
||||
|
||||
def _init_state(self, x):
|
||||
if not hasattr(self, "conv_state"):
|
||||
@@ -429,7 +450,6 @@ class Transformer:
|
||||
return min(block._reusable_prefix_len(prefix_len, len(self._cached_tokens)) for block in self.blk)
|
||||
|
||||
def generate(self, tokens:list[int], chunk_size:int=32, temperature:float=0.0):
|
||||
if self.has_recurrent_block: chunk_size = 1
|
||||
v_start_pos = UOp.variable("start_pos", 0, self.max_context-1)
|
||||
v_toks = UOp.variable("toks", 1, chunk_size)
|
||||
# TODO: use UOp.variable for temperature once float variables are supported
|
||||
@@ -438,7 +458,6 @@ class Transformer:
|
||||
t = Tensor(tokens + [0] * (self.max_context - len(tokens)), dtype="int32").reshape(1, self.max_context)
|
||||
# recompute start_pos from what's currently valid in the caches
|
||||
start_pos = self.get_start_pos(tokens)
|
||||
if start_pos < len(self._cached_tokens) and (resets := [r for b in self.blk for r in b._state_reset_ops()]): Tensor.realize(*resets)
|
||||
out, prompt_len = None, len(tokens)
|
||||
while len(tokens) < self.max_context:
|
||||
n_toks = min(chunk_size, len(tokens) - start_pos)
|
||||
|
||||
Reference in New Issue
Block a user