[FEATURE] GLM5 FP8 EP适配

vllm/model_executor/layers/fused_moe/all2all_utils.py

@@ -157,11 +157,8 @@ def maybe_make_prepare_finalize(
 
         # Note: We may want to use FP8 dispatch just to reduce
         # data movement.
-        use_fp8_dispatch = (
-            quant_config.quant_dtype == current_platform.fp8_dtype()
-            and quant_config.block_shape == DEEPEP_QUANT_BLOCK_SHAPE
-        )
-
+        use_fp8_dispatch = quant_config.quant_dtype == current_platform.fp8_dtype()
+            
         use_int8_dispatch = quant_config.quant_dtype == torch.int8
 
         prepare_finalize = DeepEPLLPrepareAndFinalize(

vllm/model_executor/layers/fused_moe/batched_deep_gemm_moe.py

@@ -38,10 +38,10 @@ from vllm.utils.math_utils import cdiv, round_up
 from vllm.utils.import_utils import has_deep_gemm
 from vllm.model_executor.layers.activation import SiluAndMul
 
-from lightop import fuse_silu_mul_quant_ep
+from lightop import fuse_silu_mul_quant_ep, fuse_silu_mul_fp8_quant_ep
 from lmslim.layers.gemm.int8_utils import per_token_quant_int8
 if has_deep_gemm():
-    from deepgemm import m_grouped_w8a8_gemm_nt_masked
+    from deepgemm import m_grouped_w8a8_gemm_nt_masked, m_grouped_fp8_gemm_nt_masked
 else:
     from lightop import m_grouped_w8a8_gemm_nt_masked
 
@@ -452,8 +452,8 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
             num_dispatchers=num_dispatchers,
         )
 
-        if quant_config.use_fp8_w8a8:
-            assert self.block_shape == get_mk_alignment_for_contiguous_layout()
+        #if quant_config.use_fp8_w8a8:
+            #assert self.block_shape == get_mk_alignment_for_contiguous_layout()
 
         self.N = N
         self.K = K
@@ -606,7 +606,7 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         expected_m = self.get_expected_m()
 
         if self.quant_config.use_fp8_w8a16 or self.quant_config.use_fp8_w8a8:
-            fp8_m_grouped_gemm_nt_masked(
+            m_grouped_fp8_gemm_nt_masked(
                 (a1q, a1q_scale),
                 (w1, self.w1_scale),
                 workspace1,
@@ -614,14 +614,13 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
                 expected_m,
             )
 
-            quant_scale_fmt = DeepGemmQuantScaleFMT.from_oracle()
-            a2q, a2q_scale = persistent_masked_m_silu_mul_quant(
-                workspace1,
-                expert_num_tokens,
-                quant_scale_fmt=quant_scale_fmt,
+            a2q, a2q_scale = fuse_silu_mul_fp8_quant_ep(
+                input=workspace1,
+                fp8type=0,
+                tokens_per_expert=expert_num_tokens,
             )
 
-            fp8_m_grouped_gemm_nt_masked(
+            m_grouped_fp8_gemm_nt_masked(
                 (a2q, a2q_scale),
                 (w2, self.w2_scale),
                 output,

vllm/model_executor/layers/fused_moe/config.py

@@ -87,14 +87,14 @@ def _quant_flags_to_group_shape(
     """
     a_shape: GroupShape | None
     w_shape: GroupShape | None
-    if block_shape is not None and quant_dtype!=torch.int8:
+    if block_shape is not None and quant_dtype!=torch.int8 and quant_dtype!=current_platform.fp8_dtype():
         assert not per_act_token_quant
         assert not per_out_ch_quant
         # TODO(bnell): this is not quite right for activations since first
         # dim should be 1.
         a_shape = GroupShape(row=block_shape[0], col=block_shape[1])
         w_shape = GroupShape(row=block_shape[0], col=block_shape[1])
-    elif block_shape is not None and quant_dtype == torch.int8:
+    elif block_shape is not None and (quant_dtype == torch.int8 or quant_dtype == current_platform.fp8_dtype()):
         a_shape = GroupShape(row=block_shape[0], col=block_shape[1])
         w_shape = GroupShape(row=block_shape[0], col=block_shape[1])
     else:
@@ -518,7 +518,7 @@ class FusedMoEQuantConfig:
                 weight_dtype, w_shape, w2_scale, g2_alphas, w2_zp, w2_bias
             ),
         )
-        if quant_dtype != torch.int8:
+        if quant_dtype != torch.int8 and quant_dtype != current_platform.fp8_dtype():
             assert quant_config.per_act_token_quant == per_act_token_quant
             assert quant_config.per_out_ch_quant == per_out_ch_quant
             assert quant_config.block_shape == block_shape

vllm/model_executor/layers/fused_moe/deepep_ll_prepare_finalize.py

@@ -22,7 +22,7 @@ from vllm.v1.worker.ubatching import (
     dbo_enabled,
     dbo_maybe_run_recv_hook,
 )
-
+from vllm.platforms import current_platform
 logger = init_logger(__name__)
 
 # DeepEP kernels quantize dispatch inputs in 128 element chunks.
@@ -179,7 +179,7 @@ class DeepEPLLPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
                 if quant_config.block_shape is not None
                 else None
             )
-            if block_k == DEEPEP_QUANT_BLOCK_SIZE:
+            if block_k == DEEPEP_QUANT_BLOCK_SIZE or (isinstance(x, tuple) and  x[0].dtype == current_platform.fp8_dtype()):
                 # DeepEP kernels did the quantization for us.
                 x, x_scales = x
                 return x, x_scales

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe_marlin.py

@@ -18,7 +18,7 @@ from vllm.model_executor.layers.fused_moe import (
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import(
     get_w8a8_int8_marlin_weights, w8a8_nt_kpack2_marlin_weight, weight8bit_nt_kpack2_marlin1)
-from vllm.model_executor.layers.fused_moe.config import (FusedMoEQuantConfig, int8_w8a8_moe_quant_config)
+from vllm.model_executor.layers.fused_moe.config import (FusedMoEQuantConfig, int8_w8a8_moe_quant_config, fp8_w8a8_moe_quant_config)
 from vllm.model_executor.layers.fused_moe import (
     FusedMoE,
     FusedMoEMethodBase,
@@ -120,14 +120,38 @@ class CompressedTensorsW8A8FP8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod):
                 "dynamic per token quantization. Found static input scales.")
         self.fused_experts = self.fused_moe_forward
 
+        vllm_config = get_current_vllm_config()
+        parallel_config = vllm_config.parallel_config
+        self.dp_size = get_dp_group().world_size
+        self.use_deepep = self.dp_size > 1 and parallel_config.enable_expert_parallel and \
+            (envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput" or \
+             envs.VLLM_ALL2ALL_BACKEND == "deepep_low_latency" or \
+             envs.VLLM_ALL2ALL_BACKEND == "deepep_auto")
+        
+        if self.use_deepep:
+            all2all_manager = get_ep_group().device_communicator.all2all_manager
+            assert all2all_manager is not None
+            self.num_dispatchers = all2all_manager.world_size
+
     def get_fused_moe_quant_config(
             self, layer: torch.nn.Module) -> Optional[FusedMoEQuantConfig]:
-        return None
+        return fp8_w8a8_moe_quant_config(
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            a1_scale=layer.w13_input_scale,
+            a2_scale=layer.w2_input_scale,
+            per_act_token_quant=True,
+            per_out_ch_quant=False,
+            block_shape=[256, 256] if self.use_deepep else None,
+        )
 
     def create_weights(self, layer: torch.nn.Module, num_experts: int,
                        hidden_size: int, intermediate_size_per_partition: int,
                        params_dtype: torch.dtype, **extra_weight_attrs):
-
+        
+        if self.use_deepep:
+            self.N = 2 * intermediate_size_per_partition
+            self.K = hidden_size
         params_dtype = torch.float8_e4m3fn
 
         # WEIGHTS
@@ -200,7 +224,10 @@ class CompressedTensorsW8A8FP8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod):
         else:
             w1_marlin_list = []
             for ii in range(layer.w13_weight.shape[0]):
-                w1_marlin_in = get_w8a8_int8_marlin_weights(layer.w13_weight[ii])
+                if not self.use_deepep:
+                    w1_marlin_in = get_w8a8_int8_marlin_weights(layer.w13_weight[ii])
+                else:
+                    w1_marlin_in = weight8bit_nt_kpack2_marlin1(layer.w13_weight[ii])
                 w1_marlin_list.append(w1_marlin_in.float() if w1_marlin_in.dtype == torch.float8_e4m3fn else w1_marlin_in)
             w1_marlin = torch.stack(w1_marlin_list, dim=0)
             w1_marlin = fp32_to_fp8_e4m3fn(w1_marlin)
@@ -208,7 +235,10 @@ class CompressedTensorsW8A8FP8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod):
             del w1_marlin_list
             w2_marlin_list = []
             for ii in range(layer.w2_weight.shape[0]):
-                w2_marlin_in = get_w8a8_int8_marlin_weights(layer.w2_weight[ii])
+                if not self.use_deepep:
+                    w2_marlin_in = get_w8a8_int8_marlin_weights(layer.w2_weight[ii])
+                else:
+                    w2_marlin_in = weight8bit_nt_kpack2_marlin1(layer.w2_weight[ii])
                 w2_marlin_list.append(w2_marlin_in.float() if w2_marlin_in.dtype == torch.float8_e4m3fn else w2_marlin_in)
             w2_marlin = torch.stack(w2_marlin_list, dim=0)
             w2_marlin = fp32_to_fp8_e4m3fn(w2_marlin)
@@ -328,6 +358,42 @@ class CompressedTensorsW8A8FP8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod):
             routed_scaling_factor=routed_scaling_factor,
             shared_output=shared_output, )
     
+    def select_gemm_impl(
+        self,
+        prepare_finalize: FusedMoEPrepareAndFinalize,
+        layer: torch.nn.Module,
+    ) -> FusedMoEPermuteExpertsUnpermute:
+        from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
+            BatchedDeepGemmExperts,
+        )
+        from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
+            DeepGemmExperts,
+        )
+
+        if (
+            prepare_finalize.activation_format
+            == FusedMoEActivationFormat.BatchedExperts
+        ):
+            max_num_tokens_per_rank = prepare_finalize.max_num_tokens_per_rank()
+            assert max_num_tokens_per_rank is not None
+
+            logger.debug("BatchedDeepGemmExperts(%s)", self.__class__.__name__)
+            return BatchedDeepGemmExperts(
+                moe_config=self.moe,
+                max_num_tokens=max_num_tokens_per_rank,
+                num_dispatchers=prepare_finalize.num_dispatchers(),
+                quant_config=self.moe_quant_config,
+                N=self.N,
+                K=self.K
+            )
+
+        else:
+            logger.debug("DeepGemmExperts(%s)", self.__class__.__name__)
+            return DeepGemmExperts(moe_config=self.moe,
+                                   quant_config=self.moe_quant_config,
+                                   N=self.N,
+                                   K=self.K)
+    
 class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod):
     def __init__(
             self,