[NPU] support asym_int4 for minicpm (#12567)
This commit is contained in:
Zijie Li
GitHub
parent
6e801bc4e1
commit
1a2ab12876
2 changed files with 146 additions and 40 deletions
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@ -81,7 +81,8 @@ class LowBitMinicpmMultiDecoderlayer(LLMBaseNNFactory):
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num_hidden_layers,
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n_splits_linear: int = 1,
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n_splits_down_proj: int = 1,
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group_size: int = 0
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group_size: int = 0,
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asym: bool = False,
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):
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super().__init__(max_seq_len=max_seq_len,
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transpose_value=transpose_value,
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@ -90,7 +91,8 @@ class LowBitMinicpmMultiDecoderlayer(LLMBaseNNFactory):
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device=device,
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n_splits_linear=n_splits_linear,
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n_splits_down_proj=n_splits_down_proj,
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group_size=group_size)
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group_size=group_size,
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asym=asym)
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self.max_seq_len = max_seq_len
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self.intermediate_size = intermediate_size
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self.dtype = dtype
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@ -272,7 +274,8 @@ class FusedLlamaLowBitMultiDecoderlayer(torch.nn.Module):
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do_print: bool = False,
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n_splits_linear: int = 1,
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n_splits_down_proj: int = 1,
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group_size: int = 0
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group_size: int = 0,
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asym: bool = False,
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):
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super().__init__()
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@ -280,8 +283,10 @@ class FusedLlamaLowBitMultiDecoderlayer(torch.nn.Module):
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op_parameters = []
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for w in parameters:
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if isinstance(w, tuple): # from QuantizedLinear
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if isinstance(w, tuple) and not asym: # from QuantizedLinear
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op_parameters.append((w[0].numpy(), w[1].numpy()))
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elif isinstance(w, tuple) and asym: # from QuantizedLinear
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op_parameters.append((w[0].numpy(), w[1].numpy(), w[2].numpy()))
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elif w.dtype in [torch.int8, torch.uint8]: # QuantizedLinear weight
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op_parameters.append(w.numpy())
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elif isinstance(w, np.ndarray): # scale
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@ -336,7 +341,8 @@ class FusedLlamaLowBitMultiDecoderlayer(torch.nn.Module):
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dtype=np_dtype,
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n_splits_linear=n_splits_linear,
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n_splits_down_proj=n_splits_down_proj,
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group_size=group_size
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group_size=group_size,
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asym=asym,
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)
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self.backend_decoders.append(decoder)
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@ -414,7 +420,8 @@ class FusedLlamaLowBitDecoderlayer(torch.nn.Module):
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transpose_value: bool = False,
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n_splits_linear: int = 1,
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n_splits_down_proj: int = 1,
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group_size: int = 0
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group_size: int = 0,
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asym: bool = False,
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):
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super().__init__()
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self.op_parameters = parameters
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@ -447,7 +454,8 @@ class FusedLlamaLowBitDecoderlayer(torch.nn.Module):
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dtype=np_dtype,
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n_splits_linear=n_splits_linear,
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n_splits_down_proj=n_splits_down_proj,
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group_size=group_size
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group_size=group_size,
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asym=asym,
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)
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self.layer_norm_0 = layer_norm_0
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self.layer_norm_1 = layer_norm_1
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@ -534,6 +542,7 @@ def run_decode(
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layer_indexs = range(layer_start, layer_end)
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n_splits_linear = len(model.model.layers[0].mlp.gate_proj_dq_list)
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n_splits_down_proj = len(model.model.layers[0].mlp.down_proj_dq_list)
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asym = getattr(model.config, "asym", False)
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for layer_idx in layer_indexs:
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curr_layer = model.model.layers[layer_idx]
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attn_layer = curr_layer.self_attn
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@ -546,10 +555,17 @@ def run_decode(
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mlp_layer.down_proj_dq_list]:
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l_weights = []
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scales = []
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zeros = []
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for l in layer_list:
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l_weights.append(l.weight)
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scales.append(l.scale)
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
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if l.zero is not None:
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zeros.append(l.zero)
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if len(zeros):
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0),
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torch.stack(zeros, axis=0)))
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else:
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
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cached_cos = curr_layer.self_attn.rotary_emb.cos_cached.to(torch.float16)
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cached_sin = curr_layer.self_attn.rotary_emb.sin_cached.to(torch.float16)
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@ -580,7 +596,8 @@ def run_decode(
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do_print=False,
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n_splits_linear=n_splits_linear,
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n_splits_down_proj=n_splits_down_proj,
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group_size=group_size
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group_size=group_size,
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asym=asym,
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)
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dist.barrier()
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@ -753,6 +770,7 @@ def run_prefill(
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layer_indexs = range(layer_start, layer_end)
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n_splits_linear = len(model.model.layers[0].mlp.gate_proj_dq_list)
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n_splits_down_proj = len(model.model.layers[0].mlp.down_proj_dq_list)
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asym = getattr(model.config, "asym", False)
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for layer_idx in layer_indexs:
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curr_layer = model.model.layers[layer_idx]
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attn_layer = curr_layer.self_attn
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@ -765,10 +783,17 @@ def run_prefill(
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mlp_layer.down_proj_dq_list]:
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l_weights = []
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scales = []
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zeros = []
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for l in layer_list:
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l_weights.append(l.weight)
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scales.append(l.scale)
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
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if l.zero is not None:
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zeros.append(l.zero)
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if len(zeros):
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0),
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torch.stack(zeros, axis=0)))
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else:
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
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cached_cos = curr_layer.self_attn.rotary_emb.cos_cached.to(torch.float16)
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cached_sin = curr_layer.self_attn.rotary_emb.sin_cached.to(torch.float16)
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@ -793,7 +818,8 @@ def run_prefill(
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transpose_value=transpose_value_cache,
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n_splits_linear=n_splits_linear,
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n_splits_down_proj=n_splits_down_proj,
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group_size=group_size
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group_size=group_size,
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asym=asym
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)
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layer_weights.extend(weights)
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@ -105,6 +105,7 @@ class MiniCPMLMHead(LLMBaseNNFactory):
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profile: bool = False,
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device: str = "NPU",
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n_splits: int = 1,
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asym: bool = False
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):
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super().__init__(max_seq_len=max_seq_len,
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transpose_value=transpose_value,
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@ -134,11 +135,13 @@ class MiniCPMLMHead(LLMBaseNNFactory):
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# for MiniCPM-2B-sft-bf16
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hidden_states_1 = self.linear(
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hidden_states, 73440, self.hidden_size, bias=False, wt_dtype=self.dtype,
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n_splits=n_splits, scale_factor=(n_splits == 1)
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n_splits=n_splits, scale_factor=(n_splits == 1),
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asym=asym
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)
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hidden_states_2 = self.linear(
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hidden_states, 73440, self.hidden_size, bias=False, wt_dtype=self.dtype,
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n_splits=n_splits, scale_factor=(n_splits == 1)
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n_splits=n_splits, scale_factor=(n_splits == 1),
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asym=asym
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)
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hidden_states_2 = self.slice(hidden_states_2, begin=[0, 0, 0], end=[1, 1, 49313])
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@ -147,7 +150,8 @@ class MiniCPMLMHead(LLMBaseNNFactory):
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# for MiniCPM-1B-sft-bf16
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hidden_states = self.linear(
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hidden_states, self.vocab_size, self.hidden_size, bias=False,
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wt_dtype=self.dtype, n_splits=n_splits, scale_factor=(n_splits == 1)
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wt_dtype=self.dtype, n_splits=n_splits, scale_factor=(n_splits == 1),
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asym=asym
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)
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# define outputs
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@ -165,28 +169,48 @@ def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir,
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rms_norm_eps = model.config.rms_norm_eps
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vocab_size = model.config.vocab_size
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model_norm = model.model.norm
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asym = getattr(model.config, "asym", False)
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if n_splits_linear == 1:
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if vocab_size == 122753:
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# for MiniCPM-2B-sft-bf16
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weights = [(model.lm_head_0.weight, model.lm_head_0.scale),
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(model.lm_head_1.weight, model.lm_head_1.scale)]
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asym = model.lm_head_0.qtype == "asym_int4_rtn"
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if asym:
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weights = [(model.lm_head_0.weight, model.lm_head_0.scale, model.lm_head_0.zero),
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(model.lm_head_1.weight, model.lm_head_1.scale, model.lm_head_1.zero)]
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else:
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weights = [(model.lm_head_0.weight, model.lm_head_0.scale),
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(model.lm_head_1.weight, model.lm_head_1.scale)]
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else:
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# for MiniCPM-1B-sft-bf16
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weights = [(model.lm_head.weight, model.lm_head.scale)]
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asym = model.lm_head.qtype == "asym_int4_rtn"
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if asym:
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weights = [(model.lm_head.weight, model.lm_head.scale, model.lm_head.zero)]
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else:
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weights = [(model.lm_head.weight, model.lm_head.scale)]
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else:
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weights = []
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if vocab_size == 122753:
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asym = model.lm_head_0.lm_heads[0].qtype == "asym_int4_rtn"
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lm_head_list = [model.lm_head_0.lm_heads, model.lm_head_1.lm_heads]
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else:
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asym = model.lm_head.lm_heads[0].qtype == "asym_int4_rtn"
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lm_head_list = [model.lm_head.lm_heads]
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for lh in lm_head_list:
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lm_head_weights = []
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scales = []
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zeros = []
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for l in lh:
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lm_head_weights.append(l.weight)
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scales.append(l.scale)
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weights.append((torch.stack(lm_head_weights, axis=0),
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torch.stack(scales, axis=0)))
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if l.zero is not None:
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zeros.append(l.zero)
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if len(zeros):
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weights.append((torch.stack(lm_head_weights, axis=0),
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torch.stack(scales, axis=0),
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torch.stack(zeros, axis=0)))
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else:
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weights.append((torch.stack(lm_head_weights, axis=0),
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torch.stack(scales, axis=0)))
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if isinstance(weights[0], tuple):
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np_dtype = np.int8 if weights[0][0].dtype == torch.int8 else np.uint8
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else: # FP16 Linear
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@ -202,7 +226,8 @@ def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir,
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dtype=np_dtype,
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model_norm_weight=model_norm.weight.to(torch.float16),
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vocab_size=vocab_size,
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n_splits=n_splits_linear
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n_splits=n_splits_linear,
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asym=asym
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)
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last_blob_path = update_names_of_IR_and_export_blob(new_lm_head, "lm_head", temp_dir,
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True, True)
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@ -210,12 +235,24 @@ def convert_lm_head_and_embedding(model, n_splits_linear, temp_dir, weight_dir,
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# save weights bins files
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if n_splits_linear == 1:
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if vocab_size == 122753:
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weight_numpy = [model.lm_head_0.weight.data.numpy(),
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model.lm_head_0.scale.data.numpy(),
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model.lm_head_1.weight.data.numpy(),
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model.lm_head_1.scale.data.numpy(), ]
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if not asym:
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weight_numpy = [model.lm_head_0.weight.data.numpy(),
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model.lm_head_0.scale.data.numpy(),
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model.lm_head_1.weight.data.numpy(),
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model.lm_head_1.scale.data.numpy(), ]
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else:
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weight_numpy = [model.lm_head_0.weight.data.numpy(),
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model.lm_head_0.scale.data.numpy(),
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model.lm_head_0.zero.data.numpy(),
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model.lm_head_1.weight.data.numpy(),
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model.lm_head_1.scale.data.numpy(),
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model.lm_head_1.zero.data.numpy(), ]
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else:
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weight_numpy = [model.lm_head.weight.data.numpy(), model.lm_head.scale.data.numpy(), ]
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if not asym:
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weight_numpy = [model.lm_head.weight.data.numpy(), model.lm_head.scale.data.numpy()]
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else:
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weight_numpy = [model.lm_head.weight.data.numpy(), model.lm_head.scale.data.numpy(),
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model.lm_head.zero.data.numpy()]
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else:
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weight_numpy = [v.numpy() for v in weights[0]]
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if vocab_size == 122753:
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@ -266,6 +303,7 @@ def convert_minicpm_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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rms_norm_eps = model.config.rms_norm_eps
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num_hidden_layers = model.config.num_hidden_layers
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scale_depth = model.model.config.scale_depth
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asym = getattr(model.config, "asym", False)
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from ipex_llm.transformers.npu_models.minicpm_mp import LowBitMinicpmMultiDecoderlayer
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curr_layer = model.model.layers[layer_idx]
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@ -279,10 +317,17 @@ def convert_minicpm_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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mlp_layer.down_proj_dq_list]:
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l_weights = []
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scales = []
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zeros = []
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for l in layer_list:
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l_weights.append(l.weight)
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scales.append(l.scale)
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
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if l.zero is not None:
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zeros.append(l.zero)
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if len(zeros):
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0),
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torch.stack(zeros, axis=0)))
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else:
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weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
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cached_cos = curr_layer.self_attn.rotary_emb.cos_cached.to(torch.float16)
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cached_sin = curr_layer.self_attn.rotary_emb.sin_cached.to(torch.float16)
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@ -321,7 +366,8 @@ def convert_minicpm_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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dtype=np_dtype,
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n_splits_linear=n_splits_linear,
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n_splits_down_proj=n_splits_down_proj,
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group_size=group_size
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group_size=group_size,
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asym=asym
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)
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rest_blob_path = update_names_of_IR_and_export_blob(single_decoder,
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decoder_name,
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@ -337,11 +383,23 @@ def convert_minicpm_layer(model, layer_idx, n_splits_linear, n_splits_down_proj,
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layer_norm_0.data.numpy().tofile(input_lm_bin_file)
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layer_norm_1.data.numpy().tofile(post_lm_bin_file)
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st_idx = 7
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for idx, (weight, scale) in enumerate(weights):
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+idx*2}.bin")
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weight.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+idx*2+1}.bin")
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scale.numpy().tofile(bin_file)
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if not asym:
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for idx, (weight, scale) in enumerate(weights):
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+idx*2}.bin")
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weight.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+idx*2+1}.bin")
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scale.numpy().tofile(bin_file)
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else:
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for idx, (weight, scale, zero) in enumerate(weights):
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bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+idx*3}.bin")
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weight.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir,
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f"model_{layer_idx}_input_{st_idx+idx*3+1}.bin")
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scale.numpy().tofile(bin_file)
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bin_file = os.path.join(weight_dir,
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f"model_{layer_idx}_input_{st_idx+idx*3+2}.bin")
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zero.numpy().tofile(bin_file)
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del single_decoder
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@ -357,6 +415,7 @@ def convert_fused_minicpm_layer(model, fused_layers, n_splits_linear, n_splits_d
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scale_depth = model.model.config.scale_depth
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layer_num = len(model.model.layers)
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fused_layer_num = layer_num // fused_layers
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asym = getattr(model.config, "asym", False)
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from ipex_llm.transformers.npu_models.minicpm_mp import LowBitMinicpmMultiDecoderlayer
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for i in range(fused_layers):
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@ -380,10 +439,17 @@ def convert_fused_minicpm_layer(model, fused_layers, n_splits_linear, n_splits_d
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mlp_layer.down_proj_dq_list]:
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l_weights = []
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scales = []
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zeros = []
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for l in layer_list:
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l_weights.append(l.weight)
|
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scales.append(l.scale)
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||||
weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0)))
|
||||
if l.zero is not None:
|
||||
zeros.append(l.zero)
|
||||
if len(zeros):
|
||||
weights.append((torch.stack(l_weights, axis=0), torch.stack(scales, axis=0),
|
||||
torch.stack(zeros, axis=0)))
|
||||
else:
|
||||
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)
|
||||
|
|
@ -401,12 +467,25 @@ def convert_fused_minicpm_layer(model, fused_layers, n_splits_linear, n_splits_d
|
|||
layer_norm_1.data.numpy().tofile(post_lm_bin_file)
|
||||
st_idx = 5
|
||||
# 6, 7 are past k/v
|
||||
for idx, (weight, scale) in enumerate(weights):
|
||||
bin_file = os.path.join(weight_dir, f"model_{layer_idx}_input_{st_idx+idx*2}.bin")
|
||||
weight.numpy().tofile(bin_file)
|
||||
bin_file = os.path.join(weight_dir,
|
||||
f"model_{layer_idx}_input_{st_idx+idx*2+1}.bin")
|
||||
scale.numpy().tofile(bin_file)
|
||||
if not asym:
|
||||
for idx, (weight, scale) in enumerate(weights):
|
||||
bin_file = os.path.join(weight_dir,
|
||||
f"model_{layer_idx}_input_{st_idx+idx*2}.bin")
|
||||
weight.numpy().tofile(bin_file)
|
||||
bin_file = os.path.join(weight_dir,
|
||||
f"model_{layer_idx}_input_{st_idx+idx*2+1}.bin")
|
||||
scale.numpy().tofile(bin_file)
|
||||
else:
|
||||
for idx, (weight, scale, zero) in enumerate(weights):
|
||||
bin_file = os.path.join(weight_dir,
|
||||
f"model_{layer_idx}_input_{st_idx+idx*3}.bin")
|
||||
weight.numpy().tofile(bin_file)
|
||||
bin_file = os.path.join(weight_dir,
|
||||
f"model_{layer_idx}_input_{st_idx+idx*3+1}.bin")
|
||||
scale.numpy().tofile(bin_file)
|
||||
bin_file = os.path.join(weight_dir,
|
||||
f"model_{layer_idx}_input_{st_idx+idx*3+2}.bin")
|
||||
zero.numpy().tofile(bin_file)
|
||||
|
||||
if isinstance(weights[0], tuple):
|
||||
np_dtype = np.int8 if weights[0][0].dtype == torch.int8 else np.uint8
|
||||
|
|
@ -432,7 +511,8 @@ def convert_fused_minicpm_layer(model, fused_layers, n_splits_linear, n_splits_d
|
|||
dtype=np_dtype,
|
||||
n_splits_linear=n_splits_linear,
|
||||
n_splits_down_proj=n_splits_down_proj,
|
||||
group_size=group_size
|
||||
group_size=group_size,
|
||||
asym=asym
|
||||
)
|
||||
update_names_of_IR_and_export_blob(fused_decoder,
|
||||
f"decoder_layer_{i}",
|
||||
|
|
|
|||
Loading…
Reference in a new issue