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[Chore] some log info
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@ -303,6 +303,10 @@ class Step3TextDecoderLayer(nn.Module):
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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# query, key and positions must have the same number of tokens
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# /model_executor/layers/rotary_embedding/base.py
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# positions.shape=torch.Size([8192]), hidden_states.shape=torch.Size([4096, 3712])
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logger.info(f"{positions.shape=}, {hidden_states.shape=}")
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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@ -408,7 +412,9 @@ class Step3TextModel(nn.Module):
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if recv_handle is not None:
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for work in recv_handle:
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work.wait()
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logger.info(f"Step3TextModel {layer.layer_idx=}: {hidden_states.shape=}, {positions.shape=}")
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current_hidden, residual = layer(positions, hidden_states, residual)
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logger.info(f"create attn metadata: {current_hidden.shape=}")
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metadata = AFDConnectorMetadata.create_attention_metadata(
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layer_idx=layer.layer_idx,
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stage_idx=afd_metadata.afd_stage_idx,
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@ -580,7 +586,7 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
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(".gate_up_proj", ".up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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logger.info(f"{params_dict.keys()=}")
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# logger.info(f"{params_dict.keys()=}")
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loaded_params: set[str] = set()
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expert_params_mapping = [
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@ -592,10 +598,10 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
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disable_moe_stacked_params = [data[1] for data in expert_params_mapping]
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for name, loaded_weight in weights:
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logger.info(
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f"{self.afd_role=}, {name=}, is_moe: {self.is_moe_weight(name)}, "
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f"is_common: {self.is_common_weight(name)}"
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)
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# logger.info(
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# f"{self.afd_role=}, {name=}, is_moe: {self.is_moe_weight(name)}, "
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# f"is_common: {self.is_common_weight(name)}"
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# )
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if self.afd_role == "attention" and self.is_moe_weight(name):
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continue
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@ -660,7 +666,7 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
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start_idx,
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end_idx,
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) in qkv_params_mapping:
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logger.info(f"{weight_name=}, {name=}")
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# logger.info(f"{weight_name=}, {name=}")
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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