performance optimization, usability optimization and API compatibility adjustments for deepseek with npu graph mode (#731)

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### What this PR does / why we need it?
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- Fixes #
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1. Improve inference speed and usability for deepsek models with NPU
graph mode.
2. Modify some codes to adapt to CANN 8.1.RC1.beta1.
3. Add a switch for NPU graph mode and its cache.

### Does this PR introduce _any_ user-facing change?
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This PR provides an experimental configuration to enable NPU graph mode
for Deepseek models. User can set
additional_config={'enable_graph_mode': True} to try this feature. Note
that this feature currently only supports for V0 engine.


### How was this patch tested?
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This patch was tested with the newest torch_npu 2.5.1
(https://pypi.org/project/torch-npu/#files) and CANN 8.1.RC1.beta1
toolkit&nnal&kernels
(https://www.hiascend.com/developer/download/community/result?module=cann)
released in 25/30 April.

Signed-off-by: linfeng-yuan <1102311262@qq.com>

vllm_ascend/models/deepseek_v2.py

@@ -30,6 +30,7 @@ from typing import Any, Dict, List, Optional, Union
 
 import torch
 import torch.distributed as dist
+import torch_npu
 from torch import nn
 from transformers import PretrainedConfig
 from vllm.attention import Attention, AttentionMetadata
@@ -39,10 +40,13 @@ from vllm.distributed import (get_dp_group, get_pp_group,
                               get_tensor_model_parallel_world_size,
                               get_tp_group, tensor_model_parallel_all_reduce)
 from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
                                                ReplicatedLinear,
-                                               RowParallelLinear)
+                                               RowParallelLinear,
+                                               UnquantizedLinearMethod)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
@@ -55,15 +59,84 @@ from vllm.model_executor.models.deepseek_v2 import \
     yarn_get_mscale  # ruff: noqa: E501
 from vllm.model_executor.models.deepseek_v2 import (DeepseekV2Attention,
                                                     DeepseekV2DecoderLayer,
-                                                    DeepseekV2MLAAttention,
-                                                    DeepseekV2MLP)
+                                                    DeepseekV2MLAAttention)
 from vllm.model_executor.models.utils import (
     PPMissingLayer, make_empty_intermediate_tensors_factory, make_layers,
     maybe_prefix)
-# >>>>>>> dcd5c73 (Feat: Graph mode for deepseek v2/v3.)
 from vllm.sequence import IntermediateTensors
 
 from vllm_ascend.ops.fused_moe import AscendFusedMoE
+from vllm_ascend.quantization.w8a8_dynamic import AscendW8A8DynamicLinearMethod
+
+
+class CustomDeepseekV2MLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config,
+                                           reduce_results=reduce_results,
+                                           prefix=f"{prefix}.down_proj")
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+        # NOTE: `torch_npu.npu_dequant_swiglu_quant` can only be enabled in dynamic quant
+        self.is_dynamic_quant = not isinstance(
+            self.gate_up_proj.quant_method,
+            UnquantizedLinearMethod) and isinstance(
+                self.gate_up_proj.quant_method.quant_method,
+                AscendW8A8DynamicLinearMethod)
+
+    def forward(self, x):
+        if self.is_dynamic_quant:
+            x, dynamic_scale = torch_npu.npu_dynamic_quant(x)
+            x = torch_npu.npu_quant_matmul(
+                x,
+                self.gate_up_proj.weight,
+                self.gate_up_proj.weight_scale,
+                output_dtype=torch.int32,
+            )
+            x, dynamic_scale = torch_npu.npu_dequant_swiglu_quant(
+                x=x,
+                weight_scale=self.gate_up_proj.weight_scale_fp32,
+                activation_scale=dynamic_scale,
+                bias=None,
+                quant_scale=None,
+                quant_offset=None,
+                group_index=None,
+                activate_left=True,
+                quant_mode=1)
+            x = torch_npu.npu_quant_matmul(
+                x,
+                self.down_proj.weight,
+                self.down_proj.weight_scale,
+                pertoken_scale=dynamic_scale,
+                output_dtype=torch.bfloat16,
+            )
+            if self.down_proj.reduce_results and self.down_proj.tp_size > 1:
+                x = tensor_model_parallel_all_reduce(x)
+            return x
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
 
 
 class CustomDeepseekV2MoE(nn.Module):
@@ -119,7 +192,7 @@ class CustomDeepseekV2MoE(nn.Module):
         if config.n_shared_experts is not None:
             intermediate_size = (config.moe_intermediate_size *
                                  config.n_shared_experts)
-            self.shared_experts = DeepseekV2MLP(
+            self.shared_experts = CustomDeepseekV2MLP(
                 hidden_size=config.hidden_size,
                 intermediate_size=intermediate_size,
                 hidden_act=config.hidden_act,
@@ -392,7 +465,7 @@ class CustomDeepseekV2DecoderLayer(DeepseekV2DecoderLayer):
                 prefix=f"{prefix}.mlp",
             )
         else:
-            self.mlp = DeepseekV2MLP(
+            self.mlp = CustomDeepseekV2MLP(
                 hidden_size=config.hidden_size,
                 intermediate_size=config.intermediate_size,
                 hidden_act=config.hidden_act,
@@ -442,8 +515,9 @@ class CustomDeepseekV2DecoderLayer(DeepseekV2DecoderLayer):
             hidden_states, residual)
         hidden_states = self.mlp(hidden_states)
 
-        if isinstance(self.mlp,
-                      DeepseekV2MLP) and hidden_states.dtype == torch.float16:
+        if isinstance(
+                self.mlp,
+                CustomDeepseekV2MLP) and hidden_states.dtype == torch.float16:
             # Fix FP16 overflow
             # Scaling the DeepseekV2MLP output, it is the input of
             # input_layernorm of next decoder layer.
@@ -582,4 +656,4 @@ class CustomDeepseekV2ForCausalLM(DeepseekV2ForCausalLM):
 
 
 class CustomDeepseekV3ForCausalLM(CustomDeepseekV2ForCausalLM):
-    pass
+    pass