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[NPU] GW prefill merge qkv #12410

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[NPU] GW prefill merge qkv #12410

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612bf91
merge qkv
cyita Nov 11, 2024
fb7c23e
variadic split
cyita Nov 11, 2024
744976a
temp
cyita Nov 12, 2024
f9b9a94
fix error
cyita Nov 12, 2024
a6117d2
remove
cyita Nov 15, 2024
8e5f19c
Merge remote-tracking branch 'upstream/main' into gw-prefill
cyita Nov 15, 2024
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87 changes: 51 additions & 36 deletions python/llm/src/ipex_llm/transformers/npu_models/mp_models_base.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -141,45 +141,60 @@ def attention(self,
if self.n_splits_linear != 1:
hidden_states = self.unsqueeze(hidden_states, axis=0)

query_states = self.linear(
hidden_states,
num_heads * head_dim,
hidden_size,
bias=False,
wt_dtype=self.dtype,
n_splits=self.n_splits_linear,
scale_factor=(self.group_size == 0),
is_prefill=(mode == "prefill")
)
if mode == "prefill":
concat_linear = self.linear(hidden_states,
num_key_value_heads * head_dim * 2 + num_heads * head_dim,
hidden_size,
wt_dtype=self.dtype,
n_splits=self.n_splits_linear,
scale_factor=(self.group_size == 0),
is_prefill=(mode == "prefill"))
if q_bias is not None:
concat_linear = concat_linear + q_bias
query_states, key_states, value_states = self.variadic_split(
concat_linear, 2,
[num_heads * head_dim, num_key_value_heads * head_dim, num_key_value_heads * head_dim]
)
else:
query_states = self.linear(
hidden_states,
num_heads * head_dim,
hidden_size,
bias=False,
wt_dtype=self.dtype,
n_splits=self.n_splits_linear,
scale_factor=(self.group_size == 0),
is_prefill=(mode == "prefill")
)

key_states = self.linear(
hidden_states,
num_key_value_heads * head_dim,
hidden_size,
bias=False,
wt_dtype=self.dtype,
n_splits=self.n_splits_linear,
scale_factor=(self.group_size == 0),
is_prefill=(mode == "prefill")
)
key_states = self.linear(
hidden_states,
num_key_value_heads * head_dim,
hidden_size,
bias=False,
wt_dtype=self.dtype,
n_splits=self.n_splits_linear,
scale_factor=(self.group_size == 0),
is_prefill=(mode == "prefill")
)

value_states = self.linear(
hidden_states,
num_key_value_heads * head_dim,
hidden_size,
bias=False,
wt_dtype=self.dtype,
n_splits=self.n_splits_linear,
scale_factor=(self.group_size == 0),
is_prefill=(mode == "prefill")
)
value_states = self.linear(
hidden_states,
num_key_value_heads * head_dim,
hidden_size,
bias=False,
wt_dtype=self.dtype,
n_splits=self.n_splits_linear,
scale_factor=(self.group_size == 0),
is_prefill=(mode == "prefill")
)

if q_bias is not None:
query_states = query_states + q_bias
if k_bias is not None:
key_states = key_states + k_bias
if v_bias is not None:
value_states = value_states + v_bias
if q_bias is not None:
query_states = query_states + q_bias
if k_bias is not None:
key_states = key_states + k_bias
if v_bias is not None:
value_states = value_states + v_bias

query_states = self.reshape(
query_states, [1, seq_len, num_heads, head_dim]
Expand Down
89 changes: 65 additions & 24 deletions python/llm/src/ipex_llm/transformers/npu_models/qwen2_mp.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -173,13 +173,18 @@ def __init__(
post_attn_layernorm_weights = [self.constant(w) for w in post_attn_layernorm_weights]

if q_biases is None:
q_biases = []
k_biases = []
v_biases = []
for i in range(num_layers):
q_biases.append(self.create_input_op((self.num_heads * self.head_dim,)))
k_biases.append(self.create_input_op((self.num_key_value_heads * self.head_dim,)))
v_biases.append(self.create_input_op((self.num_key_value_heads * self.head_dim,)))
if mode == "prefill":
q_biases = []
for i in range(num_layers):
q_biases.append(self.create_input_op((self.num_heads * self.head_dim + self.num_key_value_heads * self.head_dim * 2,)))
else:
q_biases = []
k_biases = []
v_biases = []
for i in range(num_layers):
q_biases.append(self.create_input_op((self.num_heads * self.head_dim,)))
k_biases.append(self.create_input_op((self.num_key_value_heads * self.head_dim,)))
v_biases.append(self.create_input_op((self.num_key_value_heads * self.head_dim,)))
else:
q_biases = [self.constant(w) for w in q_biases]
k_biases = [self.constant(w) for w in k_biases]
Expand Down Expand Up @@ -217,8 +222,8 @@ def __init__(
input_layernorm_weight=input_layernorm_weights[i],
post_attention_layernorm_weight=post_attn_layernorm_weights[i],
q_bias=q_biases[i],
k_bias=k_biases[i],
v_bias=v_biases[i],
k_bias=k_biases[i] if mode == "decode" else None,
v_bias=v_biases[i] if mode == "decode" else None,
past_key=past_keys[i],
past_value=past_values[i],
)
Expand All @@ -241,6 +246,11 @@ def __init__(
else:
self.compile()
print(f"{mode} end compiling")
qwen_size = "7b" if self.hidden_size == 3584 else "1.5b"
xml_path = f"gw/qwen-{qwen_size}-npu-qkv-split-{mode}-{num_layers}-{n_splits_linear}-{n_splits_down_proj}.xml"

if not os.path.exists(xml_path):
self.save(xml_path)

def build_decoder(
self,
Expand Down Expand Up @@ -524,8 +534,7 @@ def forward(
inputs = (hidden_states.to(torch.float16),
attention_mask.to(torch.float16),
position_ids.to(torch.int64))
inputs += (self.layer_norm_0, self.layer_norm_1)
inputs += (self.q_bias, self.k_bias, self.v_bias)
inputs += (self.layer_norm_0, self.layer_norm_1, self.q_bias)
hidden_states, past_key, past_value = run_model(
inputs, self.op_parameters, backend_cls, self.op_id, replica=2
)
Expand Down Expand Up @@ -815,23 +824,52 @@ def run_prefill(
mlp_layer = curr_layer.mlp

weights = []
for layer_list in [attn_layer.q_proj_dq_list, attn_layer.k_proj_dq_list,
attn_layer.v_proj_dq_list, attn_layer.o_proj_dq_list,
mlp_layer.gate_proj_dq_list, mlp_layer.up_proj_dq_list,
mlp_layer.down_proj_dq_list]:
l_weights = []
scales = []
for l in layer_list:
l_weights.append(l.weight)
scales.append(l.scale)
weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
if n_splits_linear == 1:
for layer_list in [attn_layer.q_proj_dq_list, attn_layer.k_proj_dq_list,
attn_layer.v_proj_dq_list, attn_layer.o_proj_dq_list,
mlp_layer.gate_proj_dq_list, mlp_layer.up_proj_dq_list,
mlp_layer.down_proj_dq_list]:
l_weights = []
scales = []
for l in layer_list:
l_weights.append(l.weight)
scales.append(l.scale)
weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
else:
qkv_weights = []
qkv_scales = []
for layer_list in [attn_layer.q_proj_dq_list, attn_layer.k_proj_dq_list,
attn_layer.v_proj_dq_list]:
l_weights = []
scales = []
for l in layer_list:
l_weights.append(l.weight)
scales.append(l.scale)
qkv_weights.append(torch.stack(l_weights, axis=0))
qkv_scales.append(torch.stack(scales, axis=0))

weights.append((torch.cat(qkv_weights, dim=1), torch.cat(qkv_scales, dim=1)))

for layer_list in [attn_layer.o_proj_dq_list,
mlp_layer.gate_proj_dq_list, mlp_layer.up_proj_dq_list,
mlp_layer.down_proj_dq_list]:
l_weights = []
scales = []
for l in layer_list:
l_weights.append(l.weight)
scales.append(l.scale)
weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))

cached_cos = curr_layer.self_attn.rotary_emb.cos_cached.to(torch.float16)
cached_sin = curr_layer.self_attn.rotary_emb.sin_cached.to(torch.float16)

layer_norm_0 = curr_layer.input_layernorm.weight.to(torch.float16)
layer_norm_1 = curr_layer.post_attention_layernorm.weight.to(torch.float16)

merge_bias = torch.cat([attn_layer.q_proj_dq_list.q_proj_dq_0.bias,
attn_layer.k_proj_dq_list.k_proj_dq_0.bias,
attn_layer.v_proj_dq_list.v_proj_dq_0.bias]).to(torch.float16)

new_decoderlayer = FusedQwenLowBitDecoderlayer(
weights,
num_heads=num_heads,
Expand All @@ -840,9 +878,12 @@ def run_prefill(
cached_sin=cached_sin,
layer_norm_0=layer_norm_0,
layer_norm_1=layer_norm_1,
q_bias=attn_layer.q_proj_dq_list.q_proj_dq_0.bias.to(torch.float16),
k_bias=attn_layer.k_proj_dq_list.k_proj_dq_0.bias.to(torch.float16),
v_bias=attn_layer.v_proj_dq_list.v_proj_dq_0.bias.to(torch.float16),
# q_bias=attn_layer.q_proj_dq_list.q_proj_dq_0.bias.to(torch.float16),
# k_bias=attn_layer.k_proj_dq_list.k_proj_dq_0.bias.to(torch.float16),
# v_bias=attn_layer.v_proj_dq_list.v_proj_dq_0.bias.to(torch.float16),
q_bias=merge_bias,
k_bias=None,
v_bias=None,
layer_idx=layer_idx,
rms_norm_eps=rms_norm_eps,
intermediate_size=intermediate_size,
Expand Down
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