Sync from v0.13

vllm/model_executor/layers/quantization/utils/mxfp4_utils.py

@@ -0,0 +1,189 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Callable
+from typing import Any
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+from vllm.triton_utils import triton
+from vllm.utils.import_utils import has_triton_kernels
+from vllm.utils.torch_utils import direct_register_custom_op, is_torch_equal_or_newer
+
+logger = init_logger(__name__)
+
+
+def _swizzle_mxfp4(quant_tensor, scale, num_warps):
+    """weight swizzle for mxfp4 moe, used for OAI mxfp4 kernel"""
+    assert has_triton_kernels()
+    import triton_kernels.matmul_ogs_details.opt_flags as opt_flags
+    from triton_kernels.numerics import InFlexData
+    from triton_kernels.tensor import FP4, convert_layout, wrap_torch_tensor
+    from triton_kernels.tensor_details import layout
+    from triton_kernels.tensor_details.layout import StridedLayout
+
+    value_layout_opts: dict[str, Any] = {}
+    scale_layout_opts: dict[str, Any] = {}
+
+    if (
+        current_platform.is_cuda()
+        and current_platform.is_device_capability(90)
+        and not is_torch_equal_or_newer("2.8.1")
+    ):
+        logger.warning_once(
+            "Mxfp4 on hopper is running on torch < 2.8.1, "
+            "this cause swizling to be disabled, which may "
+            "cause performance degradation. Please upgrade to torch nightly"
+        )
+        value_layout = StridedLayout
+        scale_layout = StridedLayout
+    elif current_platform.is_rocm():
+        from vllm.platforms.rocm import on_gfx950
+
+        value_layout = StridedLayout
+        if on_gfx950():
+            from triton_kernels.tensor_details.layout import GFX950MXScaleLayout
+
+            scale_layout = GFX950MXScaleLayout
+        else:
+            scale_layout = StridedLayout
+    else:
+        value_layout, value_layout_opts = layout.make_default_matmul_mxfp4_w_layout(
+            mx_axis=1
+        )
+        scale_layout, scale_layout_opts = (
+            layout.make_default_matmul_mxfp4_w_scale_layout(
+                mx_axis=1, num_warps=num_warps
+            )
+        )
+    if current_platform.is_cuda():
+        if current_platform.is_device_capability(90):
+            constraints = {
+                "split_k": 1,
+            }
+            opt_flags.update_opt_flags_constraints(constraints)
+        elif current_platform.is_device_capability_family(100):
+            constraints = {
+                "is_persistent": True,
+                "epilogue_subtile": 1,
+            }
+            opt_flags.update_opt_flags_constraints(constraints)
+    # transpose the tensor so that the quantization axis is on dim1
+    quant_tensor = quant_tensor.transpose(-2, -1)
+    scale = scale.transpose(-2, -1)
+    quant_tensor = convert_layout(
+        wrap_torch_tensor(quant_tensor, dtype=FP4), value_layout, **value_layout_opts
+    )
+    scale = convert_layout(wrap_torch_tensor(scale), scale_layout, **scale_layout_opts)
+    return quant_tensor, InFlexData(), scale
+
+
+def _can_support_mxfp4(
+    use_grouped_topk: bool = False,
+    topk_group: int | None = None,
+    num_expert_group: int | None = None,
+    expert_map: torch.Tensor | None = None,
+    custom_routing_function: Callable | None = None,
+    e_score_correction_bias: torch.Tensor | None = None,
+    apply_router_weight_on_input: bool = False,
+    scoring_func: str = "softmax",
+    activation: str = "swigluoai",
+    expert_load_view: torch.Tensor | None = None,
+    logical_to_physical_map: torch.Tensor | None = None,
+    logical_replica_count: torch.Tensor | None = None,
+):
+    return not (
+        use_grouped_topk
+        or topk_group
+        or num_expert_group
+        or custom_routing_function
+        or e_score_correction_bias
+        or apply_router_weight_on_input
+        or scoring_func != "softmax"
+        or activation != "swigluoai"
+        or expert_load_view
+        or logical_to_physical_map
+        or logical_replica_count
+    )
+
+
+def get_padding_alignment():
+    return (
+        256
+        if triton.runtime.driver.active.get_current_target().arch in ("gfx950",)
+        else 128
+    )
+
+
+def _dequant_mxfp4(
+    x: torch.Tensor, scale: torch.Tensor, float_dtype: torch.dtype
+) -> torch.Tensor:
+    try:
+        from quark.torch.kernel import mx
+    except ImportError as err:
+        raise ImportError(
+            "The package `amd-quark` is required to use "
+            "MX-FP4 models. Please install it with `pip install "
+            "amd-quark`."
+        ) from err
+
+    return mx.dq_mxfp4(x, scale, float_dtype)
+
+
+def _dequant_mxfp4_fake(
+    x: torch.Tensor, scale: torch.Tensor, float_dtype: torch.dtype
+) -> torch.Tensor:
+    return torch.empty(
+        (*x.shape[:-1], x.shape[-1] * 2), dtype=float_dtype, device=x.device
+    )
+
+
+def _quant_dequant_mxfp4(
+    x: torch.Tensor, scale_calculation_mode: str = "even"
+) -> torch.Tensor:
+    try:
+        from quark.torch.kernel import mx
+    except ImportError as err:
+        raise ImportError(
+            "The package `amd-quark` is required to use "
+            "MX-FP4 models. Please install it with `pip install "
+            "amd-quark`."
+        ) from err
+
+    return mx.qdq_mxfp4(x, scale_calculation_mode)
+
+
+def _quant_dequant_mxfp4_fake(
+    x: torch.Tensor, scale_calculation_mode: str = "even"
+) -> torch.Tensor:
+    return torch.empty_like(x)
+
+
+# Protect these operations into a torch custom op to avoid errors as
+# torch._dynamo.exc.Unsupported: Attempted to call function marked as skipped
+# Explanation: Dynamo does not know how to trace the builtin
+# `kernel_ext.PyCapsule.dq_uint8_mxfp4_to_half.` This function is either a
+# Python builtin (e.g. _warnings.warn) or a third-party C/C++ Python
+# extension (perhaps created with pybind).
+# TODO: Make sure there is no way to avoid having these functions
+# marked as skipped by dynamo.
+try:
+    direct_register_custom_op(
+        op_name="dequant_mxfp4",
+        op_func=_dequant_mxfp4,
+        fake_impl=_dequant_mxfp4_fake,
+    )
+    dequant_mxfp4 = torch.ops.vllm.dequant_mxfp4
+except AttributeError as error:
+    raise error
+
+try:
+    direct_register_custom_op(
+        op_name="quant_dequant_mxfp4",
+        op_func=_quant_dequant_mxfp4,
+        fake_impl=_quant_dequant_mxfp4_fake,
+    )
+    quant_dequant_mxfp4 = torch.ops.vllm.quant_dequant_mxfp4
+except AttributeError as error:
+    raise error