[torch.compile] Fix RMSNorm + quant fusion in the non-cutlass-fp8 case, rename RedundantReshapesPass to NoopEliminationPass (#10902)

Signed-off-by: luka <luka@neuralmagic.com>

tests/compile/backend.py

@@ -13,21 +13,26 @@ class TestBackend:
     This class provides a simple Inductor backend that can be used for testing.
     It takes a list of custom passes and runs them after Inductor's passes.
     It also saves the graph before and after the custom passes for inspection.
+
+    Inductor config can be modified directly by editing the inductor_config
+    property. This can be helpful for adding passes like the
+    'pre_grad_custom_pass' and the 'post_grad_custom_pre_pass'.
     """
 
     def __init__(self, *passes: Union[InductorPass, Callable[[fx.Graph],
                                                              None]]):
         self.custom_passes = list(passes)
         from torch._inductor import config
-        self.current_config = config.shallow_copy_dict()
-        self.current_config['force_disable_caches'] = True
-        self.current_config['post_grad_custom_post_pass'] = self.post_pass
+        self.inductor_config = config.shallow_copy_dict()
+        self.inductor_config['force_disable_caches'] = True
+        self.inductor_config['post_grad_custom_post_pass'] = self.post_pass
 
     def __call__(self, graph: fx.GraphModule, example_inputs):
+        self.graph_pre_compile = deepcopy(graph)
         from torch._inductor.compile_fx import compile_fx
         return compile_fx(graph,
                           example_inputs,
-                          config_patches=self.current_config)
+                          config_patches=self.inductor_config)
 
     def post_pass(self, graph: fx.Graph):
         self.graph_pre_pass = deepcopy(graph)

tests/compile/test_functionalization.py

@@ -9,7 +9,7 @@ from vllm.compilation.fix_functionalization import FixFunctionalizationPass
 from vllm.compilation.fusion import (FUSED_OPS, FusionPass, QuantKey,
                                      kFp8DynamicTokenSym, kFp8StaticTensorSym)
 from vllm.compilation.fx_utils import find_auto_fn, find_auto_fn_maybe, is_func
-from vllm.compilation.reshapes import RedundantReshapesPass
+from vllm.compilation.noop_elimination import NoOpEliminationPass
 from vllm.config import CompilationConfig
 
 from .backend import TestBackend
@@ -50,11 +50,11 @@ def test_fix_functionalization(model: str, quant_key: QuantKey,
     torch.set_default_device("cuda")
 
     config = CompilationConfig.PassConfig(enable_fusion=do_fusion,
-                                          enable_reshape=True)
-    reshape_pass = RedundantReshapesPass(config)
+                                          enable_noop=True)
+    noop_pass = NoOpEliminationPass(config)
     fusion_pass = FusionPass.instance(config)
 
-    passes = [reshape_pass, fusion_pass] if do_fusion else [reshape_pass]
+    passes = [noop_pass, fusion_pass] if do_fusion else [noop_pass]
     func_pass = FixFunctionalizationPass(config)
     backend_func = TestBackend(*passes, func_pass)
     backend_no_func = TestBackend(*passes)

tests/compile/test_fusion.py

@@ -5,23 +5,25 @@ import torch
 from compressed_tensors.quantization import FP8_DTYPE
 
 import vllm.envs as envs
+import vllm.plugins
 from vllm.compilation.fusion import (FUSED_OPS, QUANT_OPS, FusedRMSQuantKey,
                                      FusionPass, QuantKey)
 from vllm.compilation.fx_utils import find_auto_fn, find_auto_fn_maybe
-from vllm.compilation.reshapes import RedundantReshapesPass
-from vllm.config import CompilationConfig
+from vllm.compilation.noop_elimination import NoOpEliminationPass
+from vllm.config import CompilationConfig, CompilationLevel, VllmConfig
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
-    apply_fp8_linear)
+    CUTLASS_FP8_SUPPORTED, apply_fp8_linear, maybe_create_device_identity)
 
 from .backend import TestBackend
 
 
 class TestModel(torch.nn.Module):
 
-    def __init__(self, hidden_size: int, eps: float, static: bool, *args,
-                 **kwargs):
+    def __init__(self, hidden_size: int, eps: float, static: bool,
+                 cutlass_fp8_enabled: bool, *args, **kwargs):
         super().__init__(*args, **kwargs)
+        self.cutlass_fp8_enabled = cutlass_fp8_enabled
         self.norm = [RMSNorm(hidden_size, eps) for _ in range(3)]
         self.wscale = [torch.rand(1, dtype=torch.float32) for _ in range(2)]
         if static:
@@ -41,7 +43,8 @@ class TestModel(torch.nn.Module):
                               self.w[0],
                               self.wscale[0],
                               self.scale[0],
-                              use_per_token_if_dynamic=True)
+                              use_per_token_if_dynamic=True,
+                              cutlass_fp8_supported=self.cutlass_fp8_enabled)
         # make sure resid is used for replacement to work
         y2, resid = self.norm[1](x2, resid)
 
@@ -49,7 +52,8 @@ class TestModel(torch.nn.Module):
                               self.w[1],
                               self.wscale[1],
                               self.scale[1],
-                              use_per_token_if_dynamic=True)
+                              use_per_token_if_dynamic=True,
+                              cutlass_fp8_supported=self.cutlass_fp8_enabled)
         y3, resid = self.norm[2](x3, resid)  # use resid here
         return y3
 
@@ -59,60 +63,67 @@ class TestModel(torch.nn.Module):
 @pytest.mark.parametrize("num_tokens", [7, 256, 533, 2048, 2049])
 @pytest.mark.parametrize("eps", [1e-5, 1e-6])
 @pytest.mark.parametrize("static", [True, False])
+@pytest.mark.parametrize("cutlass_fp8_enabled",
+                         [True, False] if CUTLASS_FP8_SUPPORTED else [False])
 @pytest.mark.skipif(envs.VLLM_TARGET_DEVICE != "cuda",
                     reason="Only test on CUDA")
-def test_fusion_rmsnorm_quant(dtype, hidden_size, num_tokens, eps, static):
+def test_fusion_rmsnorm_quant(dtype, hidden_size, num_tokens, eps, static,
+                              cutlass_fp8_enabled):
     torch.set_default_device("cuda")
     torch.set_default_dtype(dtype)
     torch.manual_seed(1)
+    maybe_create_device_identity()  # needed for certain non-cutlass fp8 paths
 
-    # Reshape pass is needed for the fusion pass to work
-    config = CompilationConfig.PassConfig(enable_fusion=True,
-                                          enable_reshape=True)
-    reshape_pass = RedundantReshapesPass(config)
-    fusion_pass = FusionPass.instance(config)
+    vllm_config = VllmConfig(compilation_config=CompilationConfig(
+        level=CompilationLevel.PIECEWISE, custom_ops=["+rms_norm"]))
+    with vllm.config.set_current_vllm_config(vllm_config):
+        # Reshape pass is needed for the fusion pass to work
+        config = CompilationConfig.PassConfig(enable_fusion=True,
+                                              enable_noop=True)
+        noop_pass = NoOpEliminationPass(config)
+        fusion_pass = FusionPass.instance(config)
 
-    backend = TestBackend(reshape_pass, fusion_pass)
-    model = TestModel(hidden_size, eps, static)
+        backend = TestBackend(noop_pass, fusion_pass)
+        model = TestModel(hidden_size, eps, static, cutlass_fp8_enabled)
 
-    # First dimension dynamic
-    x = torch.rand(num_tokens, hidden_size)
-    torch._dynamo.mark_dynamic(x, 0)
+        # First dimension dynamic
+        x = torch.rand(num_tokens, hidden_size)
+        torch._dynamo.mark_dynamic(x, 0)
 
-    result = model(x)
+        result = model(x)
 
-    model2 = torch.compile(model, backend=backend)
-    result2 = model2(x)
+        model2 = torch.compile(model, backend=backend)
+        result2 = model2(x)
 
-    # Higher tol for dynamic, even higher for bfloat16
-    if static:
-        ATOL, RTOL = (1e-3, 1e-3)
-    elif dtype == torch.float16:
-        ATOL, RTOL = (2e-3, 2e-3)
-    else:
-        ATOL, RTOL = (1e-2, 1e-2)
+        # Higher tol for dynamic, even higher for bfloat16
+        if static:
+            ATOL, RTOL = (1e-3, 1e-3)
+        elif dtype == torch.float16:
+            ATOL, RTOL = (2e-3, 2e-3)
+        else:
+            ATOL, RTOL = (1e-2, 1e-2)
 
-    torch.testing.assert_close(result, result2, atol=ATOL, rtol=RTOL)
+        torch.testing.assert_close(result, result2, atol=ATOL, rtol=RTOL)
 
-    # Check substitution worked
-    pre_nodes = backend.graph_pre_pass.nodes
-    post_nodes = backend.graph_post_pass.nodes
+        # Check substitution worked
+        pre_nodes = backend.graph_pre_pass.nodes
+        post_nodes = backend.graph_post_pass.nodes
 
-    # static is per-tensor, dynamic is per-token
-    key = QuantKey(dtype=FP8_DTYPE,
-                   static=static,
-                   per_tensor=static,
-                   symmetric=True)
-    rms_quant = FUSED_OPS[FusedRMSQuantKey(key, False)]
-    add_rms_quant = FUSED_OPS[FusedRMSQuantKey(key, True)]
-    fp8_quant = QUANT_OPS[key]
+        # static is per-tensor, dynamic is per-token
+        key = QuantKey(dtype=FP8_DTYPE,
+                       static=static,
+                       per_tensor=static,
+                       symmetric=True)
+        rms_quant = FUSED_OPS[FusedRMSQuantKey(key, False)]
+        add_rms_quant = FUSED_OPS[FusedRMSQuantKey(key, True)]
+        fp8_quant = QUANT_OPS[key]
 
-    # In pre-nodes, fp8 quant should be present and fused kernels should not
-    assert find_auto_fn_maybe(pre_nodes, rms_quant) is None
-    assert find_auto_fn_maybe(pre_nodes, add_rms_quant) is None
-    find_auto_fn(pre_nodes, fp8_quant)
+        # In pre-nodes, fp8 quant should be there and fused kernels should not
+        assert find_auto_fn_maybe(pre_nodes, rms_quant) is None
+        assert find_auto_fn_maybe(pre_nodes, add_rms_quant) is None
+        find_auto_fn(pre_nodes, fp8_quant)
 
-    # In post-nodes, fused kernels should be present and fp8 quant should not
-    find_auto_fn(post_nodes, rms_quant)
-    find_auto_fn(post_nodes, add_rms_quant)
-    assert find_auto_fn_maybe(post_nodes, fp8_quant) is None
+        # In post-nodes, fused kernels should be there and fp8 quant should not
+        find_auto_fn(post_nodes, rms_quant)
+        find_auto_fn(post_nodes, add_rms_quant)
+        assert find_auto_fn_maybe(post_nodes, fp8_quant) is None

vllm/compilation/noop_elimination.py

@@ -0,0 +1,135 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Iterable, Union
+
+import torch.fx
+from torch import SymInt
+
+from vllm.logger import init_logger
+
+from .fx_utils import is_func
+from .vllm_inductor_pass import VllmInductorPass
+
+logger = init_logger(__name__)
+
+
+class NoOpEliminationPass(VllmInductorPass):
+    """
+    This is an inductor pass that removes redundant reshape/slice operations.
+    It is required for RMSNorm-quant fusion to work properly.
+    That's because apply_fp8_linear adds a reshape, which is redundant
+    in the 2D-case. Additionally, torch internal no-op elimination pass does
+    not handle certain slice variants.
+
+    Example graph 1:
+    getitem_1: "f16[s0, 4096]" = ...
+    view_1: "f16[s0, 4096]" = torch.reshape(getitem_1, [-1, 4096])
+    at = auto_functionalized(static_scaled_fp8_quant, input = view_1, ...)
+    out: "f8e4m3fn[s0, 4096]" = at[1]
+
+    Can be replaced with:
+    getitem_1: "f16[s0, 4096]" = ...
+    at = auto_functionalized(static_scaled_fp8_quant, input = getitem_1, ...)
+    out: "f8e4m3fn[s0, 4096]" = at[1]
+
+    Example graph 2:
+    arg0: "s0" = SymInt(s0)
+    scaled_mm: "f16[s0, 4096]" = ...
+    slice_1: "f16[s0, 4096]" = torch.slice(scaled_mm, -1, 0, arg0)
+    at = auto_functionalized(fused_add_rms_norm, input = slice_1, ...)
+    out: "f16[s0, 4096]" = torch.slice_scatter(scaled_mm, at[1], 0, 0, arg0)
+
+    Can be replaced with:
+    arg0: "s0" = SymInt(s0)
+    scaled_mm: "f16[s0, 4096]" = ...
+    at = auto_functionalized(fused_add_rms_norm, input = scaled_mm, ...)
+    out: "f16[s0, 4096]" = at[1]
+
+    TODO(luka): This is currently tested in test_fusion,
+     but separate tests could be good.
+    """
+
+    def __call__(self, graph: torch.fx.Graph):
+        self.begin()
+        self.dump_graph(graph, "before_noop_elimination")
+        count = 0
+        # Remove no-op reshapes/views:
+        for node in graph.nodes:
+            if is_func(node, torch.ops.aten.reshape.default):
+                input, shape = node.args[:2]
+                input_shape = input.meta["val"].shape
+                if len(shape) != len(input_shape):
+                    # Reshape changing rank, skip
+                    continue
+
+                if shape.count(-1) > 1:
+                    # Invalid reshape args, skip
+                    continue
+
+                if self.all_dims_equivalent(shape, input_shape):
+                    node.replace_all_uses_with(input)
+                    graph.erase_node(node)
+                    count += 1
+
+            elif is_func(node, torch.ops.aten.slice.Tensor):
+                input, dim_index, start, end = node.args[:4]
+                input_shape = input.meta["val"].shape
+                i_dim = input_shape[dim_index]
+
+                if start == 0 and self.dims_equivalent(end, i_dim):
+                    node.replace_all_uses_with(input)
+                    graph.erase_node(node)
+                    count += 1
+
+            elif is_func(node, torch.ops.aten.slice_scatter.default):
+                base, view, dim_index, start, end = node.args[:5]
+                base_shape = base.meta["val"].shape
+                view_shape = view.meta["val"].shape
+
+                view_dim = view_shape[dim_index]
+
+                # Check that view fully covers base and the full view is used
+                # (if the view fully covered the base after slicing but was not
+                # fully used, we could replace slice_scatter with a simple slice
+                # but that's a niche case).
+                if (base_shape == view_shape and start == 0
+                        and self.dims_equivalent(end, view_dim)):
+                    node.replace_all_uses_with(view)
+                    graph.erase_node(node)
+                    count += 1
+
+        logger.debug("Removed %s no-op reshapes and slices", count)
+        self.dump_graph(graph, "after_noop_elimination")
+        self.end_and_log()
+
+    def all_dims_equivalent(self, dims: Iterable[Union[int, torch.fx.Node]],
+                            i_dims: Iterable[Union[int, SymInt]]):
+        return all(
+            self.dims_equivalent(s, i_s) for s, i_s in zip(dims, i_dims))
+
+    def dims_equivalent(self, dim: Union[int, torch.fx.Node],
+                        i_dim: Union[int, SymInt]) -> bool:
+        """
+        This function checks if two dimensions are equivalent.
+        :param dim: The dimension arg to reshape/slice
+        :param i_dim: The corresponding dimension in the input tensor
+        :return: Are the dimensions equivalent?
+
+        There are three cases in which the dimensions are equivalent:
+        1. The dimensions are equal (both integers)
+        2. The reshape dimension is -1 (i.e. inferred)
+        3. The dimensions both correspond to the same SymInt
+
+        While case 2 does not guarantee the dimensions are equal,
+        they are equal if all other dimensions are equal.
+
+        In case 3, the reshape dimension is a torch.fx.Node,
+        and its value is a SymInt. That value is equal to the
+        input dimension.
+
+        """
+        # Case 1 and 2
+        if dim == i_dim or dim == -1:
+            return True
+        # Case 3
+        return isinstance(dim, torch.fx.Node) and dim.meta["val"] == i_dim

vllm/compilation/pass_manager.py

@@ -11,7 +11,7 @@ from vllm.logger import init_logger
 from .fix_functionalization import FixFunctionalizationPass
 from .fusion import FusionPass
 from .inductor_pass import InductorPass
-from .reshapes import RedundantReshapesPass
+from .noop_elimination import NoOpEliminationPass
 
 logger = init_logger(__name__)
 
@@ -36,7 +36,7 @@ class PostGradPassManager(Parent):
 
     The order of the post-grad post-passes is:
     1. passes (constructor parameter)
-    2. default passes (RedundantReshapesPass, FusionPass)
+    2. default passes (NoopEliminationPass, FusionPass)
     3. config["post_grad_custom_post_pass"] (if it exists)
     4. fix_functionalization
     This way, all passes operate on a functionalized graph.
@@ -54,8 +54,8 @@ class PostGradPassManager(Parent):
 
     def configure(self, pass_config: CompilationConfig.PassConfig):
         self.pass_config = pass_config
-        if pass_config.enable_reshape:
-            self.passes += [RedundantReshapesPass(pass_config)]
+        if pass_config.enable_noop:
+            self.passes += [NoOpEliminationPass(pass_config)]
 
         if pass_config.enable_fusion:
             self.passes += [FusionPass.instance(pass_config)]

vllm/compilation/reshapes.py

@@ -1,90 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-from typing import Union
-
-import torch.fx
-from torch import SymInt
-
-from vllm.logger import init_logger
-
-from .fx_utils import is_func
-from .vllm_inductor_pass import VllmInductorPass
-
-logger = init_logger(__name__)
-
-
-class RedundantReshapesPass(VllmInductorPass):
-    """
-    This is an inductor pass that removes redundant reshape operations.
-    It is required for RMSNorm-quant fusion to work properly.
-    That's because apply_fp8_linear adds a reshape, which is redundant
-    in the 2D-case.
-
-    Example graph:
-
-    getitem_1: "f16[s0, 4096]" = ...
-    view_1: "f16[s0, 4096]" = torch.reshape(getitem_1, [-1, 4096])
-    at = auto_functionalized(static_scaled_fp8_quant, input = view_1, ...)
-    out: "f8e4m3fn[s0, 4096]" = at[1]
-
-    Can be replaced with:
-    getitem_1: "f16[s0, 4096]" = ...
-    at = auto_functionalized(static_scaled_fp8_quant, input = getitem_1, ...)
-    out: "f8e4m3fn[s0, 4096]" = at[1]
-    """
-
-    def __call__(self, graph: torch.fx.Graph):
-        self.begin()
-        self.dump_graph(graph, "before_reshapes")
-        count = 0
-        # Remove no-op reshapes/views:
-        for node in graph.nodes:
-            if is_func(node, torch.ops.aten.reshape.default):
-                input, shape = node.args[:2]
-                input_shape = input.meta["val"].shape
-                if len(shape) != len(input_shape):
-                    # Reshape changing rank, skip
-                    continue
-
-                if shape.count(-1) > 1:
-                    # Invalid reshape args, skip
-                    continue
-
-                if all(
-                        self.dims_equivalent(s, i_s)
-                        for s, i_s in zip(shape, input_shape)):
-                    node.replace_all_uses_with(input)
-                    graph.erase_node(node)
-                    count += 1
-
-        logger.debug("Removed %s no-op reshapes", count)
-
-        self.dump_graph(graph, "after_reshapes")
-        self.end_and_log()
-
-    def dims_equivalent(self, dim: Union[int, torch.fx.Node],
-                        i_dim: Union[int, SymInt]) -> bool:
-        """
-        This function checks if two dimensions are equivalent.
-        :param dim: The dimension arg to reshape
-        :param i_dim: The corresponding dimension in the input tensor
-        :return: Are the dimensions equivalent?
-
-        There are three cases in which the dimensions are equivalent:
-        1. The dimensions are equal (both integers)
-        2. The reshape dimension is -1 (i.e. inferred)
-        3. The dimensions both correspond to the same SymInt
-
-        While case 2 does not guarantee the dimensions are equal,
-        they are equal if all other dimensions are equal.
-
-        In case 3, the reshape dimension is a torch.fx.Node,
-        and its value is a SymInt. That value is equal to the
-        input dimension.
-
-        """
-        # Case 1 and 2
-        if dim == i_dim or dim == -1:
-            return True
-        # Case 3
-        return isinstance(dim, torch.fx.Node) and dim.meta["val"] == i_dim

vllm/compilation/vllm_inductor_pass.py

@@ -28,8 +28,8 @@ class VllmInductorPass(InductorPass):
         self.config = config
         self.pass_name = self.__class__.__name__
 
-    def dump_graph(self, graph: torch.fx.Graph, stage: str):
-        if stage in self.config.dump_graph_stages:
+    def dump_graph(self, graph: torch.fx.Graph, stage: str, always=False):
+        if stage in self.config.dump_graph_stages or always:
             # Make sure filename includes rank in the distributed setting
             parallel = p_is_init() and get_tp_world_size() > 1
             rank = f"-{get_tp_rank()}" if parallel else ""
@@ -49,3 +49,17 @@ class VllmInductorPass(InductorPass):
         self._end_time = time.perf_counter_ns()
         duration_ms = float(self._end_time - self._start_time) / 1.0e6
         logger.debug("%s completed in %.1f ms", self.pass_name, duration_ms)
+
+
+class PrinterInductorPass(VllmInductorPass):
+
+    def __init__(self,
+                 name: str,
+                 config: CompilationConfig.PassConfig,
+                 always=False):
+        super().__init__(config)
+        self.name = name
+        self.always = always
+
+    def __call__(self, graph: torch.fx.Graph):
+        self.dump_graph(graph, self.name, always=self.always)

vllm/config.py

@@ -2993,13 +2993,13 @@ class CompilationConfig(BaseModel):
             Each pass defines its own stages (before, after, maybe in-between).
         - dump_graph_dir: directory to dump the graphs. Default is .
         - enable_fusion: whether to enable the custom fusion pass.
-        - enable_reshape: whether to enable the custom reshape elimination pass.
-            TODO better pass enabling system.
+        - enable_noop: whether to enable the custom no-op elimination pass.
+            TODO(luka) better pass enabling system.
         """
         dump_graph_stages: List[str] = Field(default_factory=list)
         dump_graph_dir: Path = Field(default=Path("."))
         enable_fusion: bool = True
-        enable_reshape: bool = True
+        enable_noop: bool = True
 
         def uuid(self):
             """
@@ -3008,13 +3008,12 @@ class CompilationConfig(BaseModel):
             Do not include dump_graph_* in the hash - they don't affect
             compilation.
             """
-            dict_ = self.model_dump(
-                include={"enable_fusion", "enable_reshape"})
+            dict_ = self.model_dump(include={"enable_fusion", "enable_noop"})
             encoded = json.dumps(dict_, sort_keys=True).encode("utf-8")
             return hashlib.sha256(encoded).digest()
 
         def model_post_init(self, __context: Any) -> None:
-            if not self.enable_reshape and self.enable_fusion:
+            if not self.enable_noop and self.enable_fusion:
                 logger.warning_once(
                     "Fusion enabled but reshape elimination disabled. "
                     "RMSNorm + quant (fp8) fusion might not work")
@@ -3411,7 +3410,7 @@ class VllmConfig:
             self.compilation_config.use_inductor = True
             self.compilation_config.cudagraph_num_of_warmups = 1
             self.compilation_config.pass_config.enable_fusion = False
-            self.compilation_config.pass_config.enable_reshape = False
+            self.compilation_config.pass_config.enable_noop = False
             self.compilation_config.level = CompilationLevel.PIECEWISE
 
         self._set_cudagraph_sizes()

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -5,6 +5,7 @@ from typing import List, Optional, Tuple, Union
 import torch
 
 from vllm import _custom_ops as ops
+from vllm.config import CompilationLevel, get_current_vllm_config
 from vllm.platforms import current_platform
 
 # Input scaling factors are no longer optional in _scaled_mm starting
@@ -161,10 +162,14 @@ def apply_fp8_linear(
         # Note: we pad the input because torch._scaled_mm is more performant
         # for matrices with batch dimension > 16.
         # This could change in the future.
+        # We also don't pad when using torch.compile,
+        # as it breaks with dynamic shapes.
+        config = get_current_vllm_config().compilation_config
+        do_pad = config.level < CompilationLevel.PIECEWISE
         qinput, x_scale = ops.scaled_fp8_quant(
             input_2d,
             input_scale,
-            num_token_padding=17,
+            num_token_padding=17 if do_pad else None,
             use_per_token_if_dynamic=use_per_token_if_dynamic)
 
         per_tensor_weights = (weight_scale.numel() == 1)