fix: Deprecation Warnings in AutoCast API (#113)

* fix: warning of torch.amp.custom_bwd Signed-off-by: Abhishek <[email protected]> * fix: warning of torch.amp.custom_bwd Signed-off-by: Abhishek <[email protected]> * fix: warning of torch.amp.custom_bwd Signed-off-by: Abhishek <[email protected]> * fix: warning of torch.amp.custom_bwd Signed-off-by: Abhishek <[email protected]> * fix: warning of torch.amp.custom_bwd Signed-off-by: Abhishek <[email protected]> * fix: fmt, lint Signed-off-by: Abhishek <[email protected]> * fix: warning of torch.amp.custom_bwd Signed-off-by: Abhishek <[email protected]> --------- Signed-off-by: Abhishek <[email protected]>
foundation-model-stack · Dec 2, 2024 · c70ffe0 · c70ffe0
1 parent e7a0e2f
commit c70ffe0
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Showing 4 changed files with 16 additions and 18 deletions.
diff --git a/...s/accelerated-peft/src/fms_acceleration_peft/gptqmodel/nn_modules/triton_utils/dequant.py b/...s/accelerated-peft/src/fms_acceleration_peft/gptqmodel/nn_modules/triton_utils/dequant.py
@@ -17,7 +17,7 @@
 import itertools
 
 # Third Party
-from torch.cuda.amp import custom_bwd, custom_fwd
+from torch.amp import custom_bwd, custom_fwd
 import torch
 import triton
 import triton.language as tl
@@ -140,15 +140,15 @@ def quant_matmul_248(
 
 class QuantLinearFunction(torch.autograd.Function):
     @staticmethod
-    @custom_fwd
+    @custom_fwd(device_type="cuda")
     def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
         output = quant_matmul_248(input, qweight, scales, qzeros, g_idx, bits, maxq)
         ctx.save_for_backward(qweight, scales, qzeros, g_idx)
         ctx.bits, ctx.maxq = bits, maxq
         return output
 
     @staticmethod
-    @custom_bwd
+    @custom_bwd(device_type="cuda")
     def backward(ctx, grad_output):
         qweight, scales, qzeros, g_idx = ctx.saved_tensors
         bits, maxq = ctx.bits, ctx.maxq

diff --git a/plugins/fused-ops-and-kernels/src/fms_acceleration_foak/framework_plugin_fast_kernels.py b/plugins/fused-ops-and-kernels/src/fms_acceleration_foak/framework_plugin_fast_kernels.py
@@ -23,8 +23,8 @@
 import torch
 
 # Local
-from .utils import lora_adapters_switch_ddp_from_fsdp
 from .models.utils import filter_mp_rules
+from .utils import lora_adapters_switch_ddp_from_fsdp
 
 
 # consider rewriting register_foak_model_patch_rules into something

diff --git a/...s/fused-ops-and-kernels/src/fms_acceleration_foak/fused_ops/unsloth_lora/bnb/fast_lora.py b/...s/fused-ops-and-kernels/src/fms_acceleration_foak/fused_ops/unsloth_lora/bnb/fast_lora.py
@@ -57,7 +57,7 @@ class LoRA_MLP(torch.autograd.Function):
     Don't forget to see our blog post for more details!
     """
     @staticmethod
-    @torch.cuda.amp.custom_fwd
+    @torch.amp.custom_fwd(device_type='cuda')
     def forward(ctx, X : torch.Tensor,
                 gateW, gateW_quant, gate_bias, gateA, gateB, gateS,
                   upW,   upW_quant, up_bias, upA,   upB,   upS,
@@ -104,7 +104,7 @@ def forward(ctx, X : torch.Tensor,
 
 
     @staticmethod
-    @torch.cuda.amp.custom_bwd
+    @torch.amp.custom_bwd(device_type='cuda')
     def backward(ctx, dY : torch.Tensor):
         gateW, gateW_quant, gateS, upW, upW_quant, upS, downW, downW_quant, downS, \
             _backward_function = ctx.custom_saved_tensors
@@ -251,7 +251,7 @@ class LoRA_QKV(torch.autograd.Function):
     dC/dBv = A.T @ X.T @ D(Wv)
     """
     @staticmethod
-    @torch.cuda.amp.custom_fwd
+    @torch.amp.custom_fwd(device_type='cuda')
     def forward(ctx, X : torch.Tensor,
                 QW, QW_quant, Q_bias, QA, QB, QS,
                 KW, KW_quant, K_bias, KA, KB, KS,
@@ -294,7 +294,7 @@ def forward(ctx, X : torch.Tensor,
     pass
 
     @staticmethod
-    @torch.cuda.amp.custom_bwd
+    @torch.amp.custom_bwd(device_type='cuda')
     def backward(ctx, dQ, dK, dV):
         QW, QW_quant, QS, KW, KW_quant, KS, VW, VW_quant, VS = \
             ctx.custom_saved_tensors
@@ -404,7 +404,7 @@ class LoRA_W(torch.autograd.Function):
     dC/dBv = A.T @ X.T @ D(Wv)
     """
     @staticmethod
-    @torch.cuda.amp.custom_fwd
+    @torch.amp.custom_fwd(device_type='cuda')
     def forward(ctx, X : torch.Tensor,
                 W, W_quant, bias, A, B, S, dropout_O):
         dtype = X.dtype
@@ -423,7 +423,7 @@ def forward(ctx, X : torch.Tensor,
     pass
 
     @staticmethod
-    @torch.cuda.amp.custom_bwd
+    @torch.amp.custom_bwd(device_type='cuda')
     def backward(ctx, dY : torch.Tensor):
         W, W_quant, S = ctx.custom_saved_tensors
         A, B, X, OX = ctx.saved_tensors

diff --git a/.../fused-ops-and-kernels/src/fms_acceleration_foak/fused_ops/unsloth_lora/gptq/fast_lora.py b/.../fused-ops-and-kernels/src/fms_acceleration_foak/fused_ops/unsloth_lora/gptq/fast_lora.py
@@ -4,13 +4,11 @@
 
 # with modifications from The IBM Tuning Team
 
-import math
 from dataclasses import dataclass
 from logging import getLogger
 from typing import Optional
 
 import torch
-from torch.cuda.amp import custom_bwd, custom_fwd
 
 from .triton.kernels import dequant248
 from ..swiglu import swiglu_DWf_DW_dfg_kernel, swiglu_fg_kernel
@@ -213,7 +211,7 @@ class LoRA_MLP(torch.autograd.Function):
     """
 
     @staticmethod
-    @torch.cuda.amp.custom_fwd
+    @torch.amp.custom_fwd(device_type='cuda')
     def forward(
         ctx,
         X: torch.Tensor,
@@ -309,7 +307,7 @@ def forward(
         return i
 
     @staticmethod
-    @torch.cuda.amp.custom_bwd
+    @torch.amp.custom_bwd(device_type='cuda')
     def backward(ctx, dY: torch.Tensor):
         (
             gate_qweight,
@@ -497,7 +495,7 @@ class LoRA_QKV(torch.autograd.Function):
     """
 
     @staticmethod
-    @torch.cuda.amp.custom_fwd
+    @torch.amp.custom_fwd(device_type='cuda')
     def forward(
         ctx,
         X: torch.Tensor,
@@ -591,7 +589,7 @@ def forward(
         return Q, K, V
 
     @staticmethod
-    @torch.cuda.amp.custom_bwd
+    @torch.amp.custom_bwd(device_type='cuda')
     def backward(ctx, dQ, dK, dV):
         (
             Q_qweight,
@@ -770,7 +768,7 @@ class LoRA_W(torch.autograd.Function):
     """
 
     @staticmethod
-    @torch.cuda.amp.custom_fwd
+    @torch.amp.custom_fwd(device_type='cuda')
     def forward(
         ctx,
         X: torch.Tensor,
@@ -807,7 +805,7 @@ def forward(
         return XW
 
     @staticmethod
-    @torch.cuda.amp.custom_bwd
+    @torch.amp.custom_bwd(device_type='cuda')
     def backward(ctx, dY: torch.Tensor):
         O_qweight, O_scales, O_qzeros, O_g_idx, O_bits, S = ctx.custom_saved_tensors
         A, B, X, OX = ctx.saved_tensors