7f1c2ee221
PyTorch reference script had wrong concat order (forward) vs the original nikopueringer/CorridorKey which uses torch.cat([c4,c3,c2,c1]). Golden fixtures regenerated. Relax 3 tight e2e tolerances for Metal vs CPU float32 drift. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
450 lines
16 KiB
Python
450 lines
16 KiB
Python
#!/usr/bin/env python3
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"""Dump intermediate PyTorch tensors for MLX parity testing.
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Loads original CorridorKey checkpoint, runs a forward pass,
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and saves intermediate activations to reference/fixtures/.
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Usage:
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uv run --group reference python scripts/dump_pytorch_reference.py \
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--checkpoint checkpoints/CorridorKey_v1.0.pth
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Requires: torch, timm, rich (install via `uv sync --group reference`)
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"""
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from __future__ import annotations
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import argparse
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from collections import OrderedDict
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from pathlib import Path
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import numpy as np
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import timm
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from rich.console import Console
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from rich.table import Table
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# ---------------------------------------------------------------------------
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# Constants
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# ---------------------------------------------------------------------------
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SEED = 42
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IMG_SIZE = 512
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BACKBONE_CHANNELS = [112, 224, 448, 896]
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EMBED_DIM = 256
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REFINER_CHANNELS = 64
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REFINER_GROUPS = 8
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REFINER_SCALE = 10.0
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DROPOUT_RATE = 0.1
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HIERA_MODEL_NAME = "hiera_base_plus_224.mae_in1k_ft_in1k"
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INPUT_CHANNELS = 4 # RGB + alpha hint
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OUTPUT_DIR = Path("reference/fixtures")
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FIXTURE_FILENAME = "golden.npz"
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console = Console()
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# ---------------------------------------------------------------------------
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# Model components (mirror of nikopueringer/CorridorKey GreenFormer)
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# ---------------------------------------------------------------------------
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class MLP(nn.Module):
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"""Single linear projection: input_dim -> embed_dim."""
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def __init__(self, input_dim: int, embed_dim: int) -> None:
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super().__init__()
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self.proj = nn.Linear(input_dim, embed_dim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.proj(x)
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class DecoderHead(nn.Module):
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"""SegFormer-style multiscale feature fusion head."""
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def __init__(
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self,
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in_channels: list[int],
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embed_dim: int,
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output_dim: int,
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) -> None:
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super().__init__()
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self.linear_c1 = MLP(in_channels[0], embed_dim)
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self.linear_c2 = MLP(in_channels[1], embed_dim)
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self.linear_c3 = MLP(in_channels[2], embed_dim)
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self.linear_c4 = MLP(in_channels[3], embed_dim)
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fused_channels = embed_dim * len(in_channels)
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self.linear_fuse = nn.Conv2d(fused_channels, embed_dim, kernel_size=1, bias=False)
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self.bn = nn.BatchNorm2d(embed_dim)
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self.classifier = nn.Conv2d(embed_dim, output_dim, kernel_size=1)
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def forward(self, features: list[torch.Tensor]) -> torch.Tensor:
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c1, c2, c3, c4 = features
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target_size = c1.shape[2:] # H/4, W/4
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projected = []
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for feat, linear in zip(
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[c1, c2, c3, c4],
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[self.linear_c1, self.linear_c2, self.linear_c3, self.linear_c4],
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strict=True,
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):
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b, c, h, w = feat.shape
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# Flatten spatial -> project -> reshape
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x = feat.flatten(2).transpose(1, 2) # [B, H*W, C]
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x = linear(x) # [B, H*W, embed_dim]
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x = x.transpose(1, 2).reshape(b, -1, h, w) # [B, embed_dim, H, W]
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x = F.interpolate(x, size=target_size, mode="bilinear", align_corners=False)
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projected.append(x)
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fused = torch.cat(projected[::-1], dim=1) # [B, embed_dim*4, H/4, W/4]
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fused = self.linear_fuse(fused)
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fused = self.bn(fused)
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fused = F.relu(fused)
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fused = F.dropout(fused, p=DROPOUT_RATE, training=self.training)
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return self.classifier(fused)
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class RefinerBlock(nn.Module):
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"""Dilated residual block with GroupNorm."""
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def __init__(self, channels: int, dilation: int) -> None:
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super().__init__()
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self.conv1 = nn.Conv2d(
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channels, channels, kernel_size=3, padding=dilation, dilation=dilation
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)
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self.gn1 = nn.GroupNorm(REFINER_GROUPS, channels)
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self.conv2 = nn.Conv2d(
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channels, channels, kernel_size=3, padding=dilation, dilation=dilation
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)
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self.gn2 = nn.GroupNorm(REFINER_GROUPS, channels)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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residual = x
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out = F.relu(self.gn1(self.conv1(x)))
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out = self.gn2(self.conv2(out))
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return F.relu(out + residual)
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class CNNRefinerModule(nn.Module):
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"""CNN refiner: stem + 4 dilated ResBlocks + 1x1 projection."""
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def __init__(self) -> None:
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super().__init__()
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# 7 input channels: RGB (3) + coarse_pred (4: alpha + fg)
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refiner_input_channels = 7
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self.stem = nn.Sequential(
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nn.Conv2d(refiner_input_channels, REFINER_CHANNELS, kernel_size=3, padding=1),
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nn.GroupNorm(REFINER_GROUPS, REFINER_CHANNELS),
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nn.ReLU(),
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)
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self.res1 = RefinerBlock(REFINER_CHANNELS, dilation=1)
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self.res2 = RefinerBlock(REFINER_CHANNELS, dilation=2)
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self.res3 = RefinerBlock(REFINER_CHANNELS, dilation=4)
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self.res4 = RefinerBlock(REFINER_CHANNELS, dilation=8)
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# 4 output channels: delta for alpha (1) + delta for fg (3)
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refiner_output_channels = 4
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self.final = nn.Conv2d(REFINER_CHANNELS, refiner_output_channels, kernel_size=1)
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def forward(self, rgb: torch.Tensor, coarse_pred: torch.Tensor) -> torch.Tensor:
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x = torch.cat([rgb, coarse_pred], dim=1) # [B, 7, H, W]
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x = self.stem(x)
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x = self.res1(x)
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x = self.res2(x)
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x = self.res3(x)
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x = self.res4(x)
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return self.final(x) * REFINER_SCALE
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class GreenFormer(nn.Module):
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"""Top-level CorridorKey model: Hiera encoder + dual decoder heads + CNN refiner."""
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def __init__(self, img_size: int = IMG_SIZE) -> None:
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super().__init__()
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self.encoder = timm.create_model(
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HIERA_MODEL_NAME,
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pretrained=False,
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features_only=True,
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img_size=img_size,
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)
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self._patch_first_conv()
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self.alpha_decoder = DecoderHead(BACKBONE_CHANNELS, EMBED_DIM, output_dim=1)
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self.fg_decoder = DecoderHead(BACKBONE_CHANNELS, EMBED_DIM, output_dim=3)
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self.refiner = CNNRefinerModule()
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def _patch_first_conv(self) -> None:
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"""Replace 3-channel patch embed conv with 4-channel version."""
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old_conv = self.encoder.model.patch_embed.proj
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new_conv = nn.Conv2d(
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INPUT_CHANNELS,
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old_conv.out_channels,
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kernel_size=old_conv.kernel_size,
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stride=old_conv.stride,
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padding=old_conv.padding,
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bias=old_conv.bias is not None,
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)
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# Zero-init; checkpoint weights will overwrite
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nn.init.zeros_(new_conv.weight)
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if old_conv.bias is not None:
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new_conv.bias = old_conv.bias
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new_conv.weight.data[:, :3] = old_conv.weight.data
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self.encoder.model.patch_embed.proj = new_conv
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def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]:
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input_size = x.shape[2:] # (H, W)
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# Backbone
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features = self.encoder(x) # list of 4 feature maps
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# Decoder heads (at H/4 resolution)
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alpha_logits = self.alpha_decoder(features)
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fg_logits = self.fg_decoder(features)
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# Upsample to input resolution
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alpha_logits_up = F.interpolate(
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alpha_logits, size=input_size, mode="bilinear", align_corners=False
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)
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fg_logits_up = F.interpolate(
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fg_logits, size=input_size, mode="bilinear", align_corners=False
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)
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# Coarse predictions
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alpha_coarse = torch.sigmoid(alpha_logits_up)
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fg_coarse = torch.sigmoid(fg_logits_up)
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# Refiner
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rgb = x[:, :3]
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coarse_pred = torch.cat([alpha_coarse, fg_coarse], dim=1) # [B, 4, H, W]
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delta_logits = self.refiner(rgb, coarse_pred)
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# Final predictions (residual in logit space)
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alpha_final = torch.sigmoid(alpha_logits_up + delta_logits[:, 0:1])
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fg_final = torch.sigmoid(fg_logits_up + delta_logits[:, 1:4])
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return {
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"encoder_features": features,
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"alpha_logits": alpha_logits,
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"fg_logits": fg_logits,
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"alpha_logits_up": alpha_logits_up,
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"fg_logits_up": fg_logits_up,
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"alpha_coarse": alpha_coarse,
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"fg_coarse": fg_coarse,
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"delta_logits": delta_logits,
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"alpha_final": alpha_final,
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"fg_final": fg_final,
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}
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# ---------------------------------------------------------------------------
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# Checkpoint loading
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# ---------------------------------------------------------------------------
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def _interpolate_pos_embed(
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ckpt_embed: torch.Tensor,
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model_embed: torch.Tensor,
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embed_dim: int,
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) -> torch.Tensor:
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"""Bicubic interpolation of pos_embed from checkpoint resolution to model resolution.
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Both tensors are shape (1, N, C) where N = H*W spatial tokens.
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"""
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ckpt_n = ckpt_embed.shape[1]
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model_n = model_embed.shape[1]
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if ckpt_n == model_n:
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return ckpt_embed
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import math
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ckpt_side = int(math.sqrt(ckpt_n))
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model_side = int(math.sqrt(model_n))
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assert ckpt_side * ckpt_side == ckpt_n, f"pos_embed not square: {ckpt_n}"
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assert model_side * model_side == model_n, f"pos_embed not square: {model_n}"
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console.print(
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f"[cyan]Interpolating pos_embed: {ckpt_side}x{ckpt_side} -> "
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f"{model_side}x{model_side}[/cyan]"
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)
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# (1, N, C) -> (1, C, H, W) for interpolation
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embed = ckpt_embed.reshape(1, ckpt_side, ckpt_side, embed_dim).permute(0, 3, 1, 2)
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embed = F.interpolate(
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embed, size=(model_side, model_side), mode="bicubic", align_corners=False
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)
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# Back to (1, N, C)
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return embed.permute(0, 2, 3, 1).reshape(1, model_n, embed_dim)
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def load_checkpoint(model: GreenFormer, checkpoint_path: Path) -> None:
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"""Load state_dict from checkpoint, stripping torch.compile prefix if present."""
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raw = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
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state_dict = raw.get("state_dict", raw)
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# Strip _orig_mod. prefix from torch.compile
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compile_prefix = "_orig_mod."
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cleaned: OrderedDict[str, torch.Tensor] = OrderedDict()
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for key, value in state_dict.items():
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clean_key = key[len(compile_prefix) :] if key.startswith(compile_prefix) else key
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cleaned[clean_key] = value
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# Interpolate pos_embed if spatial dimensions differ
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pos_embed_key = "encoder.model.pos_embed"
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if pos_embed_key in cleaned:
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model_embed = model.state_dict()[pos_embed_key]
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if cleaned[pos_embed_key].shape != model_embed.shape:
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cleaned[pos_embed_key] = _interpolate_pos_embed(
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cleaned[pos_embed_key],
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model_embed,
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embed_dim=BACKBONE_CHANNELS[0],
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)
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missing, unexpected = model.load_state_dict(cleaned, strict=False)
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if missing:
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console.print(f"[yellow]Missing keys ({len(missing)}):[/yellow]")
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for k in missing:
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console.print(f" {k}")
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if unexpected:
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console.print(f"[yellow]Unexpected keys ({len(unexpected)}):[/yellow]")
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for k in unexpected:
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console.print(f" {k}")
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if not missing and not unexpected:
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console.print("[green]All keys matched.[/green]")
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# ---------------------------------------------------------------------------
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# Tensor dumping
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# ---------------------------------------------------------------------------
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def dump_fixtures(outputs: dict[str, torch.Tensor | list[torch.Tensor]], output_dir: Path) -> None:
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"""Save all intermediate tensors to a single .npz file."""
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output_dir.mkdir(parents=True, exist_ok=True)
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arrays: dict[str, np.ndarray] = {}
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# Flatten encoder features into separate keys
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features = outputs.pop("encoder_features")
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for i, feat in enumerate(features):
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arrays[f"encoder_feature_{i}"] = feat.cpu().numpy()
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# Remaining tensors
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for name, tensor in outputs.items():
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assert isinstance(tensor, torch.Tensor), f"Expected Tensor for {name}, got {type(tensor)}"
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arrays[name] = tensor.cpu().numpy()
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out_path = output_dir / FIXTURE_FILENAME
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np.savez(out_path, **arrays)
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console.print(f"\n[green]Saved fixtures to {out_path}[/green]")
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WEIGHTS_FILENAME = "golden_weights.npz"
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def dump_weights(model: GreenFormer, output_dir: Path) -> None:
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"""Save decoder and refiner weights as numpy arrays for Phase 2 parity tests.
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Keys are the PyTorch state_dict keys (e.g. 'alpha_decoder.linear_c1.proj.weight').
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Conv weights remain in PyTorch format (O,I,H,W) — tests handle conversion.
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"""
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output_dir.mkdir(parents=True, exist_ok=True)
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arrays: dict[str, np.ndarray] = {}
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# Extract decoder and refiner state dicts
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prefixes = ("alpha_decoder.", "fg_decoder.", "refiner.")
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for key, param in model.state_dict().items():
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if any(key.startswith(p) for p in prefixes):
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arrays[key] = param.cpu().numpy()
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out_path = output_dir / WEIGHTS_FILENAME
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np.savez(out_path, **arrays)
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console.print(f"[green]Saved {len(arrays)} weight tensors to {out_path}[/green]")
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def print_shape_report(outputs: dict[str, torch.Tensor | list[torch.Tensor]]) -> None:
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"""Print a rich table of tensor names, shapes, and dtypes."""
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table = Table(title="Reference Fixture Shapes")
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table.add_column("Tensor", style="cyan")
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table.add_column("Shape", style="green")
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table.add_column("Dtype", style="yellow")
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table.add_column("Min", style="dim")
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table.add_column("Max", style="dim")
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for name, value in outputs.items():
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if isinstance(value, list):
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for i, v in enumerate(value):
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table.add_row(
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f"{name}[{i}]",
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str(tuple(v.shape)),
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str(v.dtype),
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f"{v.min().item():.4f}",
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f"{v.max().item():.4f}",
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)
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else:
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table.add_row(
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name,
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str(tuple(value.shape)),
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str(value.dtype),
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f"{value.min().item():.4f}",
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f"{value.max().item():.4f}",
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)
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console.print(table)
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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def main() -> None:
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parser = argparse.ArgumentParser(description="Dump PyTorch reference fixtures")
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parser.add_argument(
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"--checkpoint",
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type=Path,
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default=Path("checkpoints/CorridorKey_v1.0.pth"),
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help="Path to CorridorKey checkpoint",
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)
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parser.add_argument(
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"--output-dir",
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type=Path,
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default=OUTPUT_DIR,
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help="Directory for fixture output",
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)
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parser.add_argument(
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"--seed",
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type=int,
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default=SEED,
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help="Random seed for deterministic input",
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)
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args = parser.parse_args()
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if not args.checkpoint.exists():
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console.print(f"[red]Checkpoint not found: {args.checkpoint}[/red]")
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console.print("Download CorridorKey_v1.0.pth and place it in checkpoints/")
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raise SystemExit(1)
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# Deterministic setup
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torch.manual_seed(args.seed)
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torch.set_grad_enabled(False)
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console.print("[bold]Building GreenFormer model...[/bold]")
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model = GreenFormer()
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# Set to evaluation mode (disables dropout, uses running stats for BatchNorm)
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model.train(False)
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console.print(f"[bold]Loading checkpoint: {args.checkpoint}[/bold]")
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load_checkpoint(model, args.checkpoint)
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# Deterministic input
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torch.manual_seed(args.seed)
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sample_input = torch.randn(1, INPUT_CHANNELS, IMG_SIZE, IMG_SIZE)
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shape_str = str(tuple(sample_input.shape))
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console.print(f"[bold]Running forward pass (input shape: {shape_str})...[/bold]")
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outputs = model(sample_input)
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# Add input to outputs for completeness
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outputs = {"input": sample_input, **outputs}
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print_shape_report(outputs)
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dump_fixtures(outputs, args.output_dir)
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dump_weights(model, args.output_dir)
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if __name__ == "__main__":
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main()
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