e489ad7a21
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
86 lines
3.6 KiB
Python
86 lines
3.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""
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This file contains the Pydantic schemas for various quantization-related
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parameters. When a relevant quantization technique is specified, these
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parameters are loaded in the form of a JSON alongside the model weights
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and augment the model with additional information needed for use of that
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technique. The format of this JSON should be specified by one or more
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schemas contained here.
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For example, when the KV cache is quantized to FP8-E4M3 (currently only
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possible on ROCm), the model can be optionally augmented with KV cache
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scaling factors.
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"""
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from typing import Dict, Optional
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from pydantic import BaseModel, ConfigDict, ValidationInfo, model_validator
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class KVCacheQuantSchema(BaseModel):
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dtype: str
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# Each key is a TP rank. Each value is a dictionary mapping a TP rank's
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# layer indices to their per-tensor KV cache scaling factor.
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# TODO: Consider pulling this and its validation methods out into its
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# own schema class (tricky as its members are variable)
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scaling_factor: Dict[int, Dict[int, float]]
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@model_validator(mode="after")
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def check_is_fp8(self) -> "KVCacheQuantSchema":
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assert self.dtype == "float8_e4m3fn", (
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"Loaded scaling factors intended for KV cache dtype = "
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f"{self.dtype} rather than float8_e4m3fn!")
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return self
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@model_validator(mode="after")
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def check_tp_ranks(self, info: ValidationInfo) -> "KVCacheQuantSchema":
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context = info.context
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if context:
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tp_size = context["tp_size"]
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num_hidden_layers = context["num_hidden_layers"]
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assert len(self.scaling_factor) == tp_size, (
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f"Loaded dictionary has TP size {len(self.scaling_factor)} "
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f"but LLM engine is currently running with TP size {tp_size}.")
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for tp_rank, layer_maps in self.scaling_factor.items():
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assert len(layer_maps) == num_hidden_layers, (
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f"KV cache scales map for TP rank {tp_rank} is malformed. "
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f"Expected {num_hidden_layers} layers, got "
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f"{len(layer_maps)}.")
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for i in range(tp_size):
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assert i in self.scaling_factor, (
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f"KV cache scales map for TP rank {i} not found.")
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return self
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@model_validator(mode="after")
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def check_current_rank(self, info: ValidationInfo) -> "KVCacheQuantSchema":
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context = info.context
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if context:
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tp_rank = context["tp_rank"]
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num_hidden_layers = context["num_hidden_layers"]
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layer_scales_map = self.scaling_factor[tp_rank]
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for i in range(num_hidden_layers):
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assert i in layer_scales_map, (
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f"Could not find KV cache scales for layer {i} in "
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f"TP rank {tp_rank}.")
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return self
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class QuantParamSchema(BaseModel):
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# TODO: Generalize and extend with more fields
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# (e.g. weights/activations params) once functionality is enabled
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model_config = ConfigDict(protected_namespaces=())
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model_type: Optional[str]
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kv_cache: KVCacheQuantSchema
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@model_validator(mode="after")
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def check_model_type(self, info: ValidationInfo) -> "QuantParamSchema":
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context = info.context
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if context:
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model_type = context.get("model_type", None)
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if model_type is not None:
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assert model_type == self.model_type, (
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f"Model type is {model_type} but loaded "
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f"scaling factors belonging to different "
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f"model type {self.model_type}!")
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return self
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