Update the implicit gemm kernel

Signed-off-by: Jingyu Xin <jingyux@nvidia.com>

Author: jingyu-ml

modelopt/torch/quantization/conv/README.md

@@ -0,0 +1,54 @@
+# Conv3D Implicit GEMM Kernels
+
+CUDA and Triton kernels for Conv3D via implicit GEMM with optional FP4 fake quantization.
+
+## Usage
+
+```python
+import torch
+from modelopt.torch.quantization.conv_gemm.implicit_gemm_cuda import conv3d_implicit_gemm_cuda
+
+x = torch.randn(1, 128, 21, 60, 106, device="cuda")
+w = torch.randn(512, 128, 3, 3, 3, device="cuda")
+
+# Without quantization (drop-in replacement for F.conv3d)
+out = conv3d_implicit_gemm_cuda(x, w, stride=(1,1,1), padding=(1,1,1))
+
+# With FP4 quantization
+out = conv3d_implicit_gemm_cuda(
+    x, w,
+    stride=(1,1,1),
+    padding=(1,1,1),
+    act_amax=x.abs().max().unsqueeze(0),
+    quant_act=True,
+    FP4_BLOCK_SIZE=128,  # 128 or 256
+)
+```
+
+The Triton kernel has the same API:
+
+```python
+from modelopt.torch.quantization.conv_gemm.implicit_gemm import conv3d_implicit_gemm_triton
+
+out = conv3d_implicit_gemm_triton(x, w, stride=(1,1,1), padding=(1,1,1))
+```
+
+## Parameters
+
+| Parameter | Description |
+|-----------|-------------|
+| `x` | Input tensor `[N, Cin, D, H, W]` |
+| `w` | Weight tensor `[Cout, Cin, kD, kH, kW]` |
+| `bias` | Optional bias `[Cout]` |
+| `stride` | Convolution stride `(D, H, W)` |
+| `padding` | Convolution padding `(D, H, W)` |
+| `dilation` | Convolution dilation `(D, H, W)` |
+| `act_amax` | Activation abs-max scalar tensor (required when `quant_act=True`) |
+| `quant_act` | Enable FP4 fake quantization on activations |
+| `FP4_BLOCK_SIZE` | Quantization block size: `128` or `256` |
+
+## Notes
+
+- The CUDA kernel is JIT-compiled on first call (takes a few seconds).
+- Both kernels return the same shape as `torch.nn.functional.conv3d`.
+- FP4 quantization fuses the quantize-dequantize into the GEMM tile load, so there is minimal overhead vs the non-quantized path.