Revert "perf: optimize PCG inductor path for FP8 models (#21734)" (#23159)

Feel free to PR again.

python/sglang/srt/layers/quantization/fp8_utils.py

@@ -18,7 +18,6 @@ if TYPE_CHECKING:
 from sglang.srt.layers.quantization.fp8_kernel import (
     fp8_dtype,
     fp8_max,
-    fp8_min,
     is_fp8_fnuz,
     mxfp8_block_scaled_matmul_triton,
     per_token_group_quant_fp8,
@@ -29,7 +28,6 @@ from sglang.srt.layers.quantization.fp8_kernel import (
     w8a8_block_fp8_matmul_deepgemm,
     w8a8_block_fp8_matmul_triton,
 )
-from sglang.srt.server_args import get_global_server_args
 from sglang.srt.utils import (
     ceil_align,
     ceil_div,
@@ -1457,32 +1455,12 @@ def apply_fp8_linear(
         num_token_padding = output_padding
         if cutlass_fp8_supported and weight_scale.numel() == weight.shape[1]:
             num_token_padding = None
-        # For static per-tensor activation scales when using inductor compiler,
-        # use pure PyTorch ops instead of the opaque sgl_kernel quant kernel.
-        # Inductor fuses these with surrounding ops (RMSNorm, residual add),
-        # eliminating a separate kernel launch per linear layer.
-        # weight_scale shape does not matter here -- it is only used in the
-        # GEMM epilogue, not in the activation quant fusion. Only activates when
-        # piecewise_cuda_graph_compiler=inductor; eager PCG and decode both
-        # use the faster custom kernel.
-        if (
-            input_scale is not None
-            and input_scale.numel() == 1
-            and get_global_server_args().piecewise_cuda_graph_compiler == "inductor"
-        ):
-            qinput = (
-                (input_2d * input_scale.reciprocal())
-                .clamp(min=fp8_min, max=fp8_max)
-                .to(fp8_dtype)
-            )
-            x_scale = input_scale
-        else:
-            qinput, x_scale = scaled_fp8_quant(
-                input_2d,
-                input_scale,
-                num_token_padding=num_token_padding,
-                use_per_token_if_dynamic=use_per_token_if_dynamic,
-            )
+        qinput, x_scale = scaled_fp8_quant(
+            input_2d,
+            input_scale,
+            num_token_padding=num_token_padding,
+            use_per_token_if_dynamic=use_per_token_if_dynamic,
+        )
     else:
         # cutlass w8a8 fp8 sgl-kernel only supports per-token scale
         if input_scale is not None:

python/sglang/srt/models/utils.py

@@ -30,7 +30,6 @@ from sglang.srt.mem_cache.swa_memory_pool import SWAKVPool
 from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
-from sglang.srt.server_args import get_global_server_args
 from sglang.srt.utils import get_current_device_stream_fast, is_cuda, is_hip
 from sglang.srt.utils.custom_op import register_custom_op
 
@@ -423,8 +422,6 @@ def apply_qk_norm(
         and allow_inplace  # TODO(dark): this can be relaxed if needed
         and (q_eps == k_eps)  # TODO(dark): this can also be relaxed
         and not envs.SGLANG_ENABLE_DETERMINISTIC_INFERENCE.get()
-        and get_global_server_args().piecewise_cuda_graph_compiler
-        != "inductor"  # let inductor fuse QK norm
         and can_use_fused_inplace_qknorm(head_dim, q.dtype)
     ):
         fused_inplace_qknorm(
@@ -440,16 +437,16 @@ def apply_qk_norm(
     if alt_stream is not None and get_is_capture_mode():
         current_stream = get_current_device_stream_fast()
         alt_stream.wait_stream(current_stream)
-        q_by_head = q.view(*q.shape[:-1], -1, head_dim)
+        q_by_head = q.reshape(-1, head_dim)
         q_by_head = q_norm(q_by_head)
         with torch.cuda.stream(alt_stream):
-            k_by_head = k.view(*k.shape[:-1], -1, head_dim)
+            k_by_head = k.reshape(-1, head_dim)
             k_by_head = k_norm(k_by_head)
         current_stream.wait_stream(alt_stream)
     else:
-        q_by_head = q.view(*q.shape[:-1], -1, head_dim)
+        q_by_head = q.reshape(-1, head_dim)
         q_by_head = q_norm(q_by_head)
-        k_by_head = k.view(*k.shape[:-1], -1, head_dim)
+        k_by_head = k.reshape(-1, head_dim)
         k_by_head = k_norm(k_by_head)
     q = q_by_head.view(q.shape)
     k = k_by_head.view(k.shape)