Add support for using quantized models with ramtorch

toolkit/memory_management/manager_modules.py

@@ -10,6 +10,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from typing import TYPE_CHECKING, Optional, Tuple
+from torch.overrides import has_torch_function_unary  # (ADD) torchao detection
 
 if TYPE_CHECKING:
     from .manager import MemoryManager
@@ -55,11 +56,53 @@ def _get_device_state(device: torch.device):
     return _DEVICE_STATE[device]
 
 
+# (ADD) detect torchao wrapper tensors
+def _is_ao_quantized_tensor(t: Optional[torch.Tensor]) -> bool:
+    if t is None:
+        return False
+    try:
+        if has_torch_function_unary(t):
+            return t.__class__.__module__.startswith("torchao.")
+    except Exception:
+        pass
+    for attr in (
+        "_scale",
+        "_scales",
+        "_zero_point",
+        "_zp",
+        "_block_size",
+        "_group_size",
+        "_pack_dim",
+    ):
+        if hasattr(t, attr):
+            return True
+    return False
+
+
+def _is_quantized_tensor(t: Optional[torch.Tensor]) -> bool:
+    if t is None:
+        return False
+    # torch quantized tensors
+    try:
+        if torch.is_quantized(t):  # type: ignore[attr-defined]
+            return True
+    except Exception:
+        pass
+    # (ADD) torchao quantized wrappers
+    if _is_ao_quantized_tensor(t):
+        return True
+    # packed/int formats (weight-only)
+    return not t.dtype.is_floating_point
+
+
 def _ensure_cpu_pinned(t: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
     if t is None:
         return None
     if t.device.type != "cpu":
         t = t.to("cpu", copy=True)
+    # Don't attempt to pin quantized tensors; many backends don't support it
+    if _is_quantized_tensor(t):
+        return t
     if torch.cuda.is_available():
         try:
             t = t.pin_memory()
@@ -86,8 +129,35 @@ def _move_params_to_cpu_and_pin(module: nn.Module):
 class _BouncingLinearFn(torch.autograd.Function):
     @staticmethod
     def forward(ctx, x, weight_cpu, bias_cpu, device: torch.device):
+        # choose compute dtype to match activations
+        target_dtype = (
+            x.dtype
+            if x.dtype in (torch.bfloat16, torch.float16, torch.float32)
+            else torch.bfloat16
+        )
+
+        # GPU-side dequant/cast for quantized; float path unchanged
+        def _materialize_linear_weight(cpu_w, dev):
+            if _is_quantized_tensor(cpu_w):
+                # move quantized wrapper to GPU -> dequantize on GPU -> cast on GPU
+                w_q_gpu = cpu_w.to(dev, non_blocking=True)
+                try:
+                    w_fp_gpu = w_q_gpu.dequantize()
+                except Exception:
+                    w_fp_gpu = w_q_gpu.to(dtype=torch.float32, non_blocking=True)
+                if w_fp_gpu.dtype != target_dtype:
+                    w_fp_gpu = w_fp_gpu.to(target_dtype, non_blocking=True)
+                return w_fp_gpu
+            # float path (preserve original behavior: NO dtype cast)
+            w_gpu = cpu_w.to(dev, non_blocking=True)
+            return w_gpu
+
         if device.type != "cuda":
-            out = F.linear(x.to("cpu"), weight_cpu, bias_cpu)
+            out = F.linear(
+                x.to("cpu"),
+                _materialize_linear_weight(weight_cpu, torch.device("cpu")),
+                bias_cpu,
+            )
             ctx.save_for_backward(x.to("cpu"), weight_cpu, bias_cpu)
             ctx.device = torch.device("cpu")
             return out.to(x.device)
@@ -101,7 +171,7 @@ class _BouncingLinearFn(torch.autograd.Function):
 
         with torch.cuda.stream(ts):
             ts.wait_event(ev_cu_s)
-            w_bufs[idx] = weight_cpu.to(device, non_blocking=True)
+            w_bufs[idx] = _materialize_linear_weight(weight_cpu, device)
             b_bufs[idx] = (
                 bias_cpu.to(device, non_blocking=True) if bias_cpu is not None else None
             )
@@ -114,21 +184,39 @@ class _BouncingLinearFn(torch.autograd.Function):
 
         ctx.save_for_backward(x, weight_cpu, bias_cpu)
         ctx.device = device
+        ctx.target_dtype = target_dtype
         return out
 
     @staticmethod
     def backward(ctx, grad_out):
         x, weight_cpu, bias_cpu = ctx.saved_tensors
         device = ctx.device
+        target_dtype = getattr(ctx, "target_dtype", grad_out.dtype)
 
         if device.type != "cuda":
             go_cpu = grad_out.to("cpu")
             x_cpu = x.to("cpu")
-            grad_input = go_cpu @ weight_cpu
-            grad_weight = go_cpu.flatten(0, -2).T @ x_cpu.flatten(0, -2)
+            w_mat = (
+                weight_cpu.dequantize()
+                if _is_quantized_tensor(weight_cpu)
+                else weight_cpu
+            )
+            if w_mat.dtype != target_dtype and target_dtype in (
+                torch.bfloat16,
+                torch.float16,
+                torch.float32,
+            ):
+                w_mat = w_mat.to(target_dtype)
+            grad_input = go_cpu @ w_mat
+            grad_weight = (
+                go_cpu.flatten(0, -2).T @ x_cpu.flatten(0, -2)
+                if getattr(weight_cpu, "requires_grad", False)
+                and weight_cpu.dtype.is_floating_point
+                else None
+            )
             grad_bias = (
                 go_cpu.sum(dim=tuple(range(go_cpu.ndim - 1)))
-                if bias_cpu is not None
+                if (bias_cpu is not None and getattr(bias_cpu, "requires_grad", False))
                 else None
             )
             return grad_input.to(grad_out.device), grad_weight, grad_bias, None
@@ -148,45 +236,62 @@ class _BouncingLinearFn(torch.autograd.Function):
 
         idx = state["backward_clk"]
 
-        # Stage weights onto device (transfer stream), ping-pong to avoid races
+        # GPU-side dequant/cast for quantized; float path unchanged
+        def _materialize_for_bwd(cpu_w):
+            if _is_quantized_tensor(cpu_w):
+                w_q_gpu = cpu_w.to(device, non_blocking=True)
+                try:
+                    w_fp_gpu = w_q_gpu.dequantize()
+                except Exception:
+                    w_fp_gpu = w_q_gpu.to(dtype=torch.float32, non_blocking=True)
+                if w_fp_gpu.dtype != target_dtype:
+                    w_fp_gpu = w_fp_gpu.to(target_dtype, non_blocking=True)
+                return w_fp_gpu
+            # float path (preserve original behavior: NO dtype cast)
+            w = cpu_w.to(device, non_blocking=True)
+            return w
+
         with torch.cuda.stream(transfer_stream):
             transfer_stream.wait_event(ev_cu_b_start)
-            w_bwd_buffers[idx] = weight_cpu.to(device, non_blocking=True)
+            w_bwd_buffers[idx] = _materialize_for_bwd(weight_cpu)
             state["backward_clk"] ^= 1
             ev_tx_b.record()
 
-        # Compute stream waits for weights to arrive, then start compute
         torch.cuda.current_stream().wait_event(ev_tx_b)
         ev_cu_b_start.record()
 
-        # 1) Compute grad_input using the freshly transferred weights
-        grad_input = grad_out @ w_bwd_buffers[idx]
+        # grad wrt input (GPU)
+        grad_input = grad_out.to(dtype=target_dtype) @ w_bwd_buffers[idx]
 
-        # 2) Ensure previous grad-to-CPU transfer that used this slot finished
+        # ensure previous grad-to-CPU transfer that used this slot finished
         torch.cuda.current_stream().wait_event(ev_tx_w_bwd_done)
 
-        # 3) Compute weight/bias grads on GPU into staging buffers
-        w_grad_buffers[idx] = grad_out.flatten(0, -2).T @ x.flatten(0, -2)
-        if bias_cpu is not None:
+        # compute grads if float masters exist
+        grad_weight = None
+        grad_bias = None
+        if (
+            getattr(weight_cpu, "requires_grad", False)
+            and weight_cpu.dtype.is_floating_point
+        ):
+            w_grad_buffers[idx] = grad_out.flatten(0, -2).T @ x.flatten(0, -2)
+        if bias_cpu is not None and getattr(bias_cpu, "requires_grad", False):
             reduce_dims = tuple(range(grad_out.ndim - 1))
             b_grad_buffers[idx] = grad_out.sum(dim=reduce_dims)
 
-        # Mark end of GPU compute
         ev_cu_b_finish.record()
 
-        # 4) Launch non-blocking H2D->CPU transfers on a separate grad stream (full-duplex)
         with torch.cuda.stream(transfer_grad_stream):
             transfer_grad_stream.wait_event(ev_cu_b_finish)
-            grad_weight = w_grad_buffers[idx].to("cpu", non_blocking=True)
-            grad_bias = (
-                b_grad_buffers[idx].to("cpu", non_blocking=True)
-                if bias_cpu is not None
-                else None
-            )
-            # signal that this slot's CPU transfer is complete (safe for next reuse)
+            if (
+                getattr(weight_cpu, "requires_grad", False)
+                and weight_cpu.dtype.is_floating_point
+            ):
+                grad_weight = w_grad_buffers[idx].to("cpu", non_blocking=True)
+            if bias_cpu is not None and getattr(bias_cpu, "requires_grad", False):
+                grad_bias = b_grad_buffers[idx].to("cpu", non_blocking=True)
             state["transfer_weight_backward_finished_event"].record()
 
-        return grad_input, grad_weight, grad_bias, None
+        return grad_input.to(dtype=grad_out.dtype), grad_weight, grad_bias, None
 
 
 class _BouncingConv2dFn(torch.autograd.Function):
@@ -202,12 +307,39 @@ class _BouncingConv2dFn(torch.autograd.Function):
         dilation: Tuple[int, int],
         groups: int,
     ):
+        target_dtype = (
+            x.dtype
+            if x.dtype in (torch.bfloat16, torch.float16, torch.float32)
+            else torch.bfloat16
+        )
+
+        # GPU-side dequant/cast for quantized; float path unchanged
+        def _materialize_conv_weight(cpu_w, dev):
+            if _is_quantized_tensor(cpu_w):
+                w_q_gpu = cpu_w.to(dev, non_blocking=True)
+                try:
+                    w_fp_gpu = w_q_gpu.dequantize()
+                except Exception:
+                    w_fp_gpu = w_q_gpu.to(dtype=torch.float32, non_blocking=True)
+                if w_fp_gpu.dtype != target_dtype:
+                    w_fp_gpu = w_fp_gpu.to(target_dtype, non_blocking=True)
+                return w_fp_gpu
+            # float path (preserve original behavior: NO dtype cast)
+            w_gpu = cpu_w.to(dev, non_blocking=True)
+            return w_gpu
+
         if device.type != "cuda":
             out = F.conv2d(
-                x.to("cpu"), weight_cpu, bias_cpu, stride, padding, dilation, groups
+                x.to("cpu"),
+                _materialize_conv_weight(weight_cpu, torch.device("cpu")),
+                bias_cpu,
+                stride,
+                padding,
+                dilation,
+                groups,
             )
             ctx.save_for_backward(x.to("cpu"), weight_cpu, bias_cpu)
-            ctx.meta = ("cpu", stride, padding, dilation, groups)
+            ctx.meta = ("cpu", stride, padding, dilation, groups, target_dtype)
             return out.to(x.device)
 
         state = _get_device_state(device)
@@ -219,7 +351,7 @@ class _BouncingConv2dFn(torch.autograd.Function):
 
         with torch.cuda.stream(ts):
             ts.wait_event(ev_cu_s)
-            w_bufs[idx] = weight_cpu.to(device, non_blocking=True)
+            w_bufs[idx] = _materialize_conv_weight(weight_cpu, device)
             b_bufs[idx] = (
                 bias_cpu.to(device, non_blocking=True) if bias_cpu is not None else None
             )
@@ -231,22 +363,30 @@ class _BouncingConv2dFn(torch.autograd.Function):
         out = F.conv2d(x, w_bufs[idx], b_bufs[idx], stride, padding, dilation, groups)
 
         ctx.save_for_backward(x, weight_cpu, bias_cpu)
-        ctx.meta = (device, stride, padding, dilation, groups)
+        ctx.meta = (device, stride, padding, dilation, groups, target_dtype)
         return out
 
     @staticmethod
     def backward(ctx, grad_out):
         x, weight_cpu, bias_cpu = ctx.saved_tensors
-        meta = ctx.meta
-        device, stride, padding, dilation, groups = meta
+        device, stride, padding, dilation, groups, target_dtype = ctx.meta
 
         if (
             isinstance(device, torch.device) and device.type != "cuda"
         ) or device == "cpu":
-            # CPU grads
             go = grad_out.to("cpu")
             x_cpu = x.to("cpu")
-            w_cpu = weight_cpu
+            w_cpu = (
+                weight_cpu.dequantize()
+                if _is_quantized_tensor(weight_cpu)
+                else weight_cpu
+            )
+            if w_cpu.dtype != target_dtype and target_dtype in (
+                torch.bfloat16,
+                torch.float16,
+                torch.float32,
+            ):
+                w_cpu = w_cpu.to(target_dtype)
             from torch.nn.grad import conv2d_input, conv2d_weight  # type: ignore
 
             grad_input = conv2d_input(
@@ -258,16 +398,25 @@ class _BouncingConv2dFn(torch.autograd.Function):
                 dilation=dilation,
                 groups=groups,
             )
-            grad_weight = conv2d_weight(
-                x_cpu,
-                w_cpu.shape,
-                go,
-                stride=stride,
-                padding=padding,
-                dilation=dilation,
-                groups=groups,
+            grad_weight = (
+                conv2d_weight(
+                    x_cpu,
+                    w_cpu.shape,
+                    go,
+                    stride=stride,
+                    padding=padding,
+                    dilation=dilation,
+                    groups=groups,
+                )
+                if getattr(weight_cpu, "requires_grad", False)
+                and weight_cpu.dtype.is_floating_point
+                else None
+            )
+            grad_bias = (
+                go.sum(dim=(0, 2, 3))
+                if (bias_cpu is not None and getattr(bias_cpu, "requires_grad", False))
+                else None
             )
-            grad_bias = go.sum(dim=(0, 2, 3)) if bias_cpu is not None else None
             return (
                 grad_input.to(grad_out.device),
                 grad_weight,
@@ -279,12 +428,10 @@ class _BouncingConv2dFn(torch.autograd.Function):
                 None,
             )
 
-        # CUDA path (full-duplex)
         state = _get_device_state(device)
         transfer_stream = state["transfer_stream"]
         transfer_grad_stream = state["transfer_grad_stream"]
 
-        # device-side buffers
         w_bwd_buffers = state["w_bwd_buffers"]
         w_grad_buffers = state["w_grad_buffers"]
         b_grad_buffers = state["b_grad_buffers"]
@@ -296,23 +443,37 @@ class _BouncingConv2dFn(torch.autograd.Function):
 
         idx = state["backward_clk"]
 
+        # GPU-side dequant/cast for quantized; float path unchanged
+        def _materialize_for_bwd(cpu_w):
+            if _is_quantized_tensor(cpu_w):
+                w_q_gpu = cpu_w.to(device, non_blocking=True)
+                try:
+                    w_fp_gpu = w_q_gpu.dequantize()
+                except Exception:
+                    w_fp_gpu = w_q_gpu.to(dtype=torch.float32, non_blocking=True)
+                if w_fp_gpu.dtype != target_dtype:
+                    w_fp_gpu = w_fp_gpu.to(target_dtype, non_blocking=True)
+                return w_fp_gpu
+            # float path (preserve original behavior: NO dtype cast)
+            w = cpu_w.to(device, non_blocking=True)
+            return w
+
         # Stage weights for input-grad compute
         with torch.cuda.stream(transfer_stream):
             transfer_stream.wait_event(ev_cu_b_start)
-            w_bwd_buffers[idx] = weight_cpu.to(device, non_blocking=True)
+            w_bwd_buffers[idx] = _materialize_for_bwd(weight_cpu)
             state["backward_clk"] ^= 1
             ev_tx_b.record()
 
         torch.cuda.current_stream().wait_event(ev_tx_b)
         ev_cu_b_start.record()
 
-        # grad wrt input on GPU with streamed weights
         from torch.nn.grad import conv2d_input, conv2d_weight  # type: ignore
 
         grad_input = conv2d_input(
             x.shape,
             w_bwd_buffers[idx],
-            grad_out,
+            grad_out.to(dtype=target_dtype),
             stride=stride,
             padding=padding,
             dilation=dilation,
@@ -323,33 +484,48 @@ class _BouncingConv2dFn(torch.autograd.Function):
         torch.cuda.current_stream().wait_event(ev_tx_w_bwd_done)
 
         # Compute heavy grads on GPU into staging buffers
-        w_grad_buffers[idx] = conv2d_weight(
-            x,
-            weight_cpu.shape,
-            grad_out,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            groups=groups,
-        )
-        if bias_cpu is not None:
+        grad_weight = None
+        grad_bias = None
+        if (
+            getattr(weight_cpu, "requires_grad", False)
+            and weight_cpu.dtype.is_floating_point
+        ):
+            w_grad_buffers[idx] = conv2d_weight(
+                x,
+                weight_cpu.shape,
+                grad_out,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                groups=groups,
+            )
+        if bias_cpu is not None and getattr(bias_cpu, "requires_grad", False):
             b_grad_buffers[idx] = grad_out.sum(dim=(0, 2, 3))
 
-        # Mark end of GPU math
         ev_cu_b_finish.record()
 
         # Launch CPU copies on the dedicated grad stream (overlaps with next H2D)
         with torch.cuda.stream(transfer_grad_stream):
             transfer_grad_stream.wait_event(ev_cu_b_finish)
-            grad_weight = w_grad_buffers[idx].to("cpu", non_blocking=True)
-            grad_bias = (
-                b_grad_buffers[idx].to("cpu", non_blocking=True)
-                if bias_cpu is not None
-                else None
-            )
+            if (
+                getattr(weight_cpu, "requires_grad", False)
+                and weight_cpu.dtype.is_floating_point
+            ):
+                grad_weight = w_grad_buffers[idx].to("cpu", non_blocking=True)
+            if bias_cpu is not None and getattr(bias_cpu, "requires_grad", False):
+                grad_bias = b_grad_buffers[idx].to("cpu", non_blocking=True)
             state["transfer_weight_backward_finished_event"].record()
 
-        return grad_input, grad_weight, grad_bias, None, None, None, None, None
+        return (
+            grad_input.to(dtype=grad_out.dtype),
+            grad_weight,
+            grad_bias,
+            None,
+            None,
+            None,
+            None,
+            None,
+        )
 
 
 class BaseLayerMemoryManager: