// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

import {TensorView} from '../../tensor';
import {ShapeUtil} from '../../util';
import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../attribute-with-cache-key';
import {ComputeContext, GpuDataType, ProgramInfo, ProgramInfoLoader, ProgramMetadata} from '../types';

import {
  fillVector,
  getMaxComponents,
  inputVariable,
  outputVariable,
  ShaderHelper, sumVector, tensorTypeToWsglStorageType,
} from './common';
import { DataType } from '../../../wasm-common'

export interface SkipLayerNormAttributes extends AttributeWithCacheKey {
  epsilon: number;
}

const validateInputs = (inputs: readonly TensorView[]): void => {
  if (!inputs || inputs.length < 3) {
    throw new Error('layerNorm requires at least 3 inputs.');
  }

  const input: TensorView = inputs[0];
  const skip: TensorView = inputs[1];
  const gamma: TensorView = inputs[2];

  if (input.dataType !== skip.dataType || input.dataType !== gamma.dataType) {
    throw new Error('All inputs must have the same data type');
  }

  if (input.dims.length !== 3 && input.dims.length !== 2) {
    throw new Error('Input must be 2D or 3D');
  }

  if (skip.dims.length !== 3 && skip.dims.length !== 2) {
    throw new Error('Skip must be 2D or 3D');
  }

  const hiddenSize = input.dims[input.dims.length - 1];
  const sequenceLength = input.dims[input.dims.length - 2];
  if (skip.dims[skip.dims.length - 1] !== hiddenSize) {
    throw new Error('Skip must have the same hidden size as input');
  }
  if (skip.dims[skip.dims.length - 2] !== sequenceLength) {
    throw new Error('Skip must have the same sequence length as input');
  }

  if (gamma.dims.length !== 1) {
    throw new Error('Gamma must be 1D');
  }
  if (gamma.dims[gamma.dims.length - 1] !== hiddenSize) {
    throw new Error('Gamma must have the same hidden size as input');
  }
  if (inputs.length > 3) {
    const beta: TensorView = inputs[3];
    if (beta.dims.length !== 1) {
      throw new Error('Beta must be 1D');
    }
    if (beta.dims[beta.dims.length - 1] !== hiddenSize) {
      throw new Error('Beta must have the same hidden size as input');
    }
  }

  if (inputs.length > 4) {
    const bias: TensorView = inputs[4];
    if (bias.dims.length !== 1) {
      throw new Error('Bias must be 1D');
    }
    if (bias.dims[bias.dims.length - 1] !== hiddenSize) {
      throw new Error('Bias must have the same hidden size as input');
    }
  }
};

const createSkipLayerNormProgramInfo =
    (metadata: ProgramMetadata, inputs: readonly TensorView[], attributes: SkipLayerNormAttributes, outputCount: number,
     isTraining: boolean): ProgramInfo => {
      const inputShape = inputs[0].dims;
      const inputSize = ShapeUtil.size(inputShape);
      const outputShape = inputShape;
      const outputSize = inputSize;
      const hiddenSize = inputShape.slice(-1)[0];
      const meanInvStdDevDim = isTraining ? inputShape.slice(0, -1).concat(1) : [];
      const hasBetaInput = inputs.length > 3;
      const hasBiasInput = inputs.length > 4;
      const hasMeanOutput = isTraining && outputCount > 1;
      const hasInvStdDevOutput = isTraining && outputCount > 2;
      const hasInputSkipBiasSumOutput = outputCount > 3;

      const components = getMaxComponents(hiddenSize);
      const variables = [
        inputVariable('x', inputs[0].dataType, inputs[0].dims, components),
        inputVariable('skip', inputs[1].dataType, inputs[1].dims, components),
        inputVariable('gamma', inputs[2].dataType, inputs[2].dims, components),
      ];
      if (hasBetaInput) {
        variables.push(inputVariable('beta', inputs[3].dataType, inputs[3].dims, components));
      }
      if (hasBiasInput) {
        variables.push(inputVariable('bias', inputs[4].dataType, inputs[4].dims, components));
      }
      variables.push(outputVariable('output', inputs[0].dataType, outputShape, components));
      if (hasMeanOutput) {
        variables.push(outputVariable('meanOutput', DataType.float, meanInvStdDevDim));
      }
      if (hasInvStdDevOutput) {
        variables.push(outputVariable('invStdOutput', DataType.float, meanInvStdDevDim));
      }
      if (hasInputSkipBiasSumOutput) {
        variables.push(outputVariable('inputSkipBiasSum', inputs[0].dataType, outputShape, components));
      }
      const dataType = tensorTypeToWsglStorageType(inputs[0].dataType);
      const castToF32 = components === 1 ? 'f32' : `vec${components}f`;
      const getShaderSource = (shaderHelper: ShaderHelper) => `
      const hiddenSize: u32 = ${hiddenSize};
      const hiddenSizeVectorized: u32 = ${hiddenSize / components};
      const epsilon: f32 = ${attributes.epsilon};

      ${shaderHelper.declareVariables(...variables)}

      ${shaderHelper.mainStart()}
        ${shaderHelper.guardAgainstOutOfBoundsWorkgroupSizes(outputSize / hiddenSize)}
        let offset = global_idx * hiddenSizeVectorized;
        var sum = ${fillVector('f32', components)};
        var squareSum = ${fillVector('f32', components)};
        for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {
          let skipValue = skip[offset + i];
          let biasValue = ${hasBiasInput ? 'bias[i]' : '0.0'};
          let inputValue = x[offset + i];
          let value = inputValue + skipValue + biasValue;
          ${hasInputSkipBiasSumOutput ? 'inputSkipBiasSum[offset + i] = value;' : ''}
          output[offset + i] = value;
          let f32Value = ${castToF32}(value);
          sum += f32Value;
          squareSum += f32Value * f32Value;
        }
        let mean = ${sumVector('sum', components)} / f32(hiddenSize);
        let variance = sqrt(${sumVector('squareSum', components)} / f32(hiddenSize) - mean * mean + epsilon);
        ${hasMeanOutput ? 'meanOutput[global_idx] = mean;' : ''}
        ${hasInvStdDevOutput ? 'invStdOutput[global_idx] = 1.0 / variance;' : ''}
        for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {
          output[offset + i] = (output[offset + i] - ${dataType}(mean)) / ${dataType}(variance) * gamma[i]
           + ${hasBetaInput ? 'beta[i]' : '0.0'};
        }
      }`;
      const outputs = [{dims: outputShape, dataType: inputs[0].dataType, gpuDataType: GpuDataType.default}];
      if (outputCount > 1) {
        outputs.push({dims: meanInvStdDevDim, dataType: inputs[0].dataType, gpuDataType: GpuDataType.default});
      }
      if (outputCount > 2) {
        outputs.push({dims: meanInvStdDevDim, dataType: inputs[0].dataType, gpuDataType: GpuDataType.default});
      }
      if (outputCount > 3) {
        outputs.push({dims: inputShape, dataType: inputs[0].dataType, gpuDataType: GpuDataType.default});
      }

      return {
        ...metadata,
        getShaderSource,
        outputs,
        dispatchGroup: () => ({x: Math.ceil(outputSize / hiddenSize / 64)})
      };
    };

const createSkipLayerNormProgramInfoLoader =
    (inputs: readonly TensorView[], attributes: SkipLayerNormAttributes, outputCount: number, isTraining: boolean):
        ProgramInfoLoader => {
          const inputTypes = new Array(inputs.length).fill(GpuDataType.default);
          const metadata: ProgramMetadata = {
            name: 'SkipLayerNormalization',
            inputTypes,
            cacheHint: attributes.cacheKey,
          };
          return {
            ...metadata,
            get: () => createSkipLayerNormProgramInfo(metadata, inputs, attributes, outputCount, isTraining)
          };
        };

export const skipLayerNorm = (context: ComputeContext, attributes: SkipLayerNormAttributes): void => {
  // TODO: initialize isTraining from ComputeContext
  const isTraining = false;
  validateInputs(context.inputs);
  // Mean and InvStdDev are only used in training mode and are not required for inference.
  // They are added here for completeness only.
  const outputs = [0];
  if (context.outputCount > 1) {
    outputs.push(isTraining ? 1 : -3);
  }
  if (context.outputCount > 2) {
    outputs.push(isTraining ? 2 : -3);
  }
  if (context.outputCount > 3) {
    outputs.push(3);
  }
  context.compute(
      createSkipLayerNormProgramInfoLoader(context.inputs, attributes, context.outputCount, isTraining), {outputs});
};

export const parseSkipLayerNormAttributes = (attributes: Record<string, unknown>): SkipLayerNormAttributes => {
  const epsilon = attributes.epsilon as number;
  return createAttributeWithCacheKey({epsilon});
};