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deepseekv3 cc mode fixed #719

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Merged
shihaobai merged 6 commits into from
Feb 10, 2025
Merged

deepseekv3 cc mode fixed #719

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513cc72
v3-fix
shihaobai Feb 10, 2025
72e3c75
Merge branch 'main' into v3_fix
shihaobai Feb 10, 2025
e2f02af
fix blockwise fp8 for cc
shihaobai Feb 10, 2025
71941e1
remove print
shihaobai Feb 10, 2025
c27afec
remove print
shihaobai Feb 10, 2025
270ce08
enable cc for weight
shihaobai Feb 10, 2025
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8 changes: 3 additions & 5 deletions lightllm/models/deepseek2/layer_infer/transformer_layer_infer.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -154,7 +154,7 @@ def _decompress_kv(self, kv, infer_state: Deepseek2InferStateInfo, layer_weight:
)

# CC
compressed_kv = compressed_kv.view(-1, layer_weight.kv_lora_rank)
compressed_kv = compressed_kv.view(-1, layer_weight.kv_lora_rank).contiguous()
k_nope = self.alloc_tensor(
[compressed_kv.shape[0], self.tp_q_head_num_, self.qk_nope_head_dim],
dtype=compressed_kv.dtype,
Expand All @@ -163,10 +163,8 @@ def _decompress_kv(self, kv, infer_state: Deepseek2InferStateInfo, layer_weight:
k_nope.shape,
dtype=compressed_kv.dtype,
)
wk = layer_weight.k_b_proj_.weight.view(-1, layer_weight.kv_lora_rank).T
wv = layer_weight.v_b_proj_.weight.transpose(0, 1).reshape(layer_weight.kv_lora_rank, -1)
torch.mm(compressed_kv, wk, out=k_nope.reshape(compressed_kv.shape[0], -1))
torch.mm(compressed_kv, wv, out=v.reshape(compressed_kv.shape[0], -1))
layer_weight.cc_k_b_proj_.mm(compressed_kv, out=k_nope.reshape(compressed_kv.shape[0], -1))
layer_weight.cc_v_b_proj_.mm(compressed_kv, out=v.reshape(compressed_kv.shape[0], -1))
return k_nope, k_rope, v

def _context_attention_kernel_with_CC(
Expand Down
47 changes: 42 additions & 5 deletions lightllm/models/deepseek2/layer_weights/transformer_layer_weight.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,7 @@ def fuse_vb_o(self, layer_weight):
class Deepseek2TransformerLayerWeight(TransformerLayerWeight):
def __init__(self, layer_num, tp_rank, world_size, data_type, network_config, mode=[], quant_cfg=None):
self.enable_dp = os.getenv("ENABLE_DP", "0").upper() in ["ON", "TRUE", "1"]
self.enable_cc_method = os.getenv("ENABLE_CC_METHOD", "False").upper() in ["ON", "TRUE", "1"]
super().__init__(layer_num, tp_rank, world_size, data_type, network_config, mode, quant_cfg)
return

Expand Down Expand Up @@ -132,34 +133,44 @@ def _load_kb(self, kv_b_proj_):
k_b_proj_ = kv_b_proj_.view(self.num_attention_heads, self.qk_nope_head_dim * 2, self.kv_lora_rank)[
:, : self.qk_nope_head_dim, :
]
return k_b_proj_.contiguous().to(self.data_type_)
return k_b_proj_.contiguous().to(kv_b_proj_.dtype)

def _load_kb_scale(self, kv_b_proj_, block_size):
k_b_proj_scale_ = kv_b_proj_.view(
self.num_attention_heads, self.qk_nope_head_dim * 2 // block_size, self.kv_lora_rank // block_size
)[:, : self.qk_nope_head_dim // block_size, :]
return k_b_proj_scale_.contiguous().to(self.data_type_)
return k_b_proj_scale_.contiguous().to(kv_b_proj_.dtype)

def _load_vb(self, kv_b_proj_):
v_b_proj_ = kv_b_proj_.T.view(self.kv_lora_rank, self.num_attention_heads, self.qk_nope_head_dim * 2,)[
:, :, self.qk_nope_head_dim :
].transpose(0, 1)
return v_b_proj_.contiguous().to(self.data_type_)
return v_b_proj_.contiguous().to(kv_b_proj_.dtype)

def _load_vb_scale(self, kv_b_proj_scale_, block_size):
v_b_proj_scale_ = kv_b_proj_scale_.T.view(
self.kv_lora_rank // block_size,
self.num_attention_heads,
self.qk_nope_head_dim * 2 // block_size,
)[:, :, self.qk_nope_head_dim // block_size :].transpose(0, 1)
return v_b_proj_scale_.contiguous().to(self.data_type_)
return v_b_proj_scale_.contiguous().to(kv_b_proj_scale_.dtype)

def load_hf_weights(self, weights):
if f"model.layers.{self.layer_num_}.self_attn.kv_b_proj.weight" in weights:
kv_b_proj_ = weights[f"model.layers.{self.layer_num_}.self_attn.kv_b_proj.weight"]
weights[f"model.layers.{self.layer_num_}.self_attn.k_b_proj.weight"] = self._load_kb(kv_b_proj_)
weights[f"model.layers.{self.layer_num_}.self_attn.v_b_proj.weight"] = self._load_vb(kv_b_proj_)

if self.enable_cc_method:
weights[f"model.layers.{self.layer_num_}.self_attn.cc_k_b_proj.weight"] = self._load_kb(
kv_b_proj_
).reshape(-1, self.kv_lora_rank)
weights[f"model.layers.{self.layer_num_}.self_attn.cc_v_b_proj.weight"] = (
self._load_vb(kv_b_proj_)
.transpose(0, 1)
.reshape(self.kv_lora_rank, -1)
.transpose(0, 1)
.contiguous()
)
if (
self.quant_cfg.quantized_weight
and f"model.layers.{self.layer_num_}.self_attn.kv_b_proj." + self.weight_scale_suffix in weights
Expand All @@ -177,6 +188,17 @@ def load_hf_weights(self, weights):
weights[
f"model.layers.{self.layer_num_}.self_attn.v_b_proj." + self.weight_scale_suffix
] = self._load_vb_scale(kv_b_proj_scale_, block_size)
if self.enable_cc_method:
weights[
f"model.layers.{self.layer_num_}.self_attn.cc_k_b_proj." + self.weight_scale_suffix
] = self._load_kb_scale(kv_b_proj_scale_, block_size).reshape(-1, self.kv_lora_rank // block_size)
weights[f"model.layers.{self.layer_num_}.self_attn.cc_v_b_proj." + self.weight_scale_suffix] = (
self._load_vb_scale(kv_b_proj_scale_, block_size)
.transpose(0, 1)
.reshape(self.kv_lora_rank // block_size, -1)
.transpose(0, 1)
.contiguous()
)

return super().load_hf_weights(weights)

Expand Down Expand Up @@ -236,6 +258,21 @@ def _init_qkvo(self):
weight_scale_suffix=self.weight_scale_suffix,
act_scale_suffix=self.act_scale_suffix,
)
if self.enable_cc_method:
self.cc_k_b_proj_ = ROWMMWeight(
f"model.layers.{self.layer_num_}.self_attn.cc_k_b_proj.weight",
self.data_type_,
split_n_embed=self.tp_q_head_num_ * self.qk_nope_head_dim,
weight_scale_suffix=self.weight_scale_suffix,
act_scale_suffix=self.act_scale_suffix,
)
self.cc_v_b_proj_ = ROWMMWeight(
f"model.layers.{self.layer_num_}.self_attn.cc_v_b_proj.weight",
self.data_type_,
split_n_embed=self.tp_q_head_num_ * self.qk_nope_head_dim,
weight_scale_suffix=self.weight_scale_suffix,
act_scale_suffix=self.act_scale_suffix,
)

self.o_weight_ = COLMMWeight(
f"model.layers.{self.layer_num_}.self_attn.o_proj.weight",
Expand Down